Section 1 Technical Themes

**3**

**Chapter 1**

*Robert L. Drury*

in embodying complex systems adaptation.

complex adaptive systems

powerful conceptual approaches.

**1. Introduction**

**Abstract**

HRV in an Integrated Hardware/

Artificial Intelligence to Provide

Heart rate variability (HRV) is increasingly recognized as a central variable of interest in health maintenance, disease prevention and performance optimization. It is also a sensitive biomarker of health status, disease presence and functional abilities, acquiring and processing high fidelity inter beat interval data, along with other psychophysiological parameters that can assist in clinical assessment and intervention, population health studies/digital epidemiology and positive performance optimization. We describe a system using high-throughput artificial intelligence based on the KUBIOS platform to combine time, frequency and nonlinear data domains acquired by wearable or implanted biosensors to guide in clinical assessment, decision support and intervention, population health monitoring and individual self-regulation and performance enhancement, including the use of HRV biofeedback. This approach follows the iP4 health model which emphasizes an integral, personalized, predictive, preventive and participatory approach to human health and well-being. It therefore includes psychological, biological, genomic, sociocultural, evolutionary and spiritual variables as mutually interactive elements

**Keywords:** heart rate variability, HRV, health, well-being, health biomarker, high fidelity data acquisition, digital epidemiology, KUBIOS platform, high-throughput artificial intelligence, implantable biosensors, iP4 health model, complexity theory,

The history of science shows clearly that the development of new techniques and tools of observation lead to improved scientific understanding and the development of more adequate theories. The development of the telescope and microscope made for conceptual breakthroughs in both the physical and biological sciences, facilitating empirical observations that allowed astute observers to create new and more

Assessment, Intervention and

Performance Optimization

Software System Using

#### **Chapter 1**

## HRV in an Integrated Hardware/ Software System Using Artificial Intelligence to Provide Assessment, Intervention and Performance Optimization

*Robert L. Drury*

### **Abstract**

Heart rate variability (HRV) is increasingly recognized as a central variable of interest in health maintenance, disease prevention and performance optimization. It is also a sensitive biomarker of health status, disease presence and functional abilities, acquiring and processing high fidelity inter beat interval data, along with other psychophysiological parameters that can assist in clinical assessment and intervention, population health studies/digital epidemiology and positive performance optimization. We describe a system using high-throughput artificial intelligence based on the KUBIOS platform to combine time, frequency and nonlinear data domains acquired by wearable or implanted biosensors to guide in clinical assessment, decision support and intervention, population health monitoring and individual self-regulation and performance enhancement, including the use of HRV biofeedback. This approach follows the iP4 health model which emphasizes an integral, personalized, predictive, preventive and participatory approach to human health and well-being. It therefore includes psychological, biological, genomic, sociocultural, evolutionary and spiritual variables as mutually interactive elements in embodying complex systems adaptation.

**Keywords:** heart rate variability, HRV, health, well-being, health biomarker, high fidelity data acquisition, digital epidemiology, KUBIOS platform, high-throughput artificial intelligence, implantable biosensors, iP4 health model, complexity theory, complex adaptive systems

#### **1. Introduction**

The history of science shows clearly that the development of new techniques and tools of observation lead to improved scientific understanding and the development of more adequate theories. The development of the telescope and microscope made for conceptual breakthroughs in both the physical and biological sciences, facilitating empirical observations that allowed astute observers to create new and more powerful conceptual approaches.

This chapter will describe the development of heart rate variability (HRV) as a meaningful variable to monitor, interrogate and intervene in the functioning of the human nervous system and the psychophysiological systems it communicates with [1, 2]. It will provide a context for understanding HRV's role in the assessment, maintenance and enhancement of human health, well-being and performance. Further, appropriate techniques for studying HRV will be explored and a variety of applications will be described. Finally, iP4, a systems-based model of human health and optimal performance will be described, and several HRV-based integrated hardware/software systems will be described that exemplify that model.

#### **2. HRV and the nervous system**

Since the development of the original neuron approximately 600 billion years ago in worm-like creatures, the nervous system has emerged into increasingly complex and multifunctional neural networks that have vastly increased the adaptive capabilities of those organisms so endowed. This typifies the evolutionary process that has been studied as complexity theory within the systems view life [3]. Recently, the understanding of complex adaptive systems has been aided by the application of non-linear systems dynamics, as a supplement to more traditional linear modes of exploration and understanding. Increasingly complex entities emerge through processes of self-organization in interaction with environments demanding fitness to form adaptive systems which consist of multiple interactive and interdependent coevolving components. In the case of humans, the nervous system has played a decisive role in the increasingly dominant position currently occupied in the planetary ecosystem.

The central nervous system (CNS), composed of the brain and spinal cord has been historically identified as the most important part of the nervous system for conventional scientific study [4], with Kandel devoting only one chapter out of 64 to the autonomic nervous system (ANS). It is becoming increasingly clear that the peripheral nervous system (PNS) plays a crucial part in the remarkable abilities of humans. In particular, the ANS is known to mediate the sophisticated homeostatic dynamics that allow organisms to maintain a relatively stable interior environment needed to carryout complex adaptive tasks and supports the affective elements that comprise the significant motivational features characteristic of humans. The two major subdivisions of the ANS are the sympathetic and parasympathetic nervous systems [5]. The sympathetic nervous system is associated with energizing the organism during times of threat or challenge. Such activities have been described as "fight or flight" responses. The parasympathetic nervous system has been found to exert calming, stabilizing or reparative effects described as "tend and befriend" responses. A key structural and functional component which modulates the dynamic homeostatic balance is the vagus nerve complex, which originates in the brain stem and is widely connected with major organs such as the heart, lungs, stomach, genitals, pharynx, larynx, facial musculature, and middle ear muscles [1]. In addition to the stress "fight or flight" and calming "tend and befriend" responses, the vagus mediates the equally important "freeze" or immobilization response which is associated with death feigning in many species possessing the vagal nerve complex. These three response elements are integral to HRV, which is defined as the amount of variance in R-R wave intervals, also called the interbeat interval (IBI). The IBI is used to calculate the moment by moment variations in heart rate which constitutes HRV.

In addition to the systems described above, other neural network systems play significant roles in the overall integrated functioning of the human organism.

**5**

and Lane [2].

*HRV in an Integrated Hardware/Software System Using Artificial Intelligence to Provide…*

A key example is the enteric nervous system (ENS) which is an integral part of the enteric region and bidirectionally communicates with the CNS and ANS [6]. It has been described by some investigators as a "second brain" and functions largely independently, generating significant amounts of the neurotransmitter serotonin, important in proper neurobehavioral functioning. It also may be a key participant in the functioning of the immune system and mediate the role of the enteric microbiome. It is proposed here that the increasing empirical understanding of the vagal nerve complex warrants the use of the term cardio-vagal nervous system (CVNS). Notably, the role of the vagus nerve and its role in determining HRV, which has been studied for over 150 years [7], have been expanding rapidly with over 26,000 citations resulting from a recent PubMed search of the terms

The term vagus derives from the Latin term for "traveler." As described above, this is apt as the vagal nerve complex is widely distributed throughout the body. Its origins in the subcortical region of the CNS have been identified by Porges [1] as the nucleus ambiguus, the dorsomedial medulla and the nucleus tractus solitarius. These three neural structures represent the primary central regulatory components of the vagal complex and are responsible for three significant functions. The sympathetic mobilization for fight or flight has been recognized for some time, while the dorsal vagal response is a vestigial immobilization/death feigning system and the ventral vagal complex mediates the social engagement system for adaptive motion, emotion and communication. This is perhaps its most important feature to social organisms such as humans, where communication and mutual support have been identified as crucial aspects of evolutionary fitness, contributing to both biological and cultural evolution. This conceptual approach has been called the polyvagal theory by Porges [1] and the neuro-visceral integration model by Thayer

While these functions are not currently subject to isomorphic assessment, it has been demonstrated empirically that HRV is an accurate and sensitive measure of the actions of these three subsystems. HRV is defined as the instantaneous variability found when continuous R-R intervals in the EKG are recorded [8]. These intervals are easily recorded using both standard 12 lead EKG protocols and a wide variety of freestanding equipment whose quality ranges from adequate to poor. It is impossible to obtain reliable HRV data when the equipment used to acquire the R-R intervals is either lacking reliability or is poor fidelity. If high fidelity quality data are obtained, there are three primary approaches to data analysis that have been found valuable: frequency domain measures, time domain measures and nonlinear measures [9]. Each of these approaches have demonstrated utility, although it has also been shown that some measures are less sensitive to salient phenomena and therefore, selection of the most robust analytic strategy is an important area of ongoing investigation and will be discussed further in the section of algorithmic

The research literature on HRV indicates that it can be a sensitive biomarker for a wide variety of disorders and conditions [10]. This includes medical disorders such as all-cause mortality, sudden cardiac death, sepsis, myocardial infarction, diabetic neuropathy, transplantation issues, myocardial dysfunction/heart failure and noncardiovascular diseases such as Alzheimer's dementia, epilepsy, diabetes, tetraplegia and liver cirrhosis [11]. It is important to note that in some conditions such as sepsis, the onset of subjective symptoms is often delayed and makes

*DOI: http://dx.doi.org/10.5772/intechopen.89042*

"HRV" and "heart rate variability."

**3. Heart rate variability and the vagal nerve complex**

analysis, artificial intelligence and related issues.

*HRV in an Integrated Hardware/Software System Using Artificial Intelligence to Provide… DOI: http://dx.doi.org/10.5772/intechopen.89042*

A key example is the enteric nervous system (ENS) which is an integral part of the enteric region and bidirectionally communicates with the CNS and ANS [6]. It has been described by some investigators as a "second brain" and functions largely independently, generating significant amounts of the neurotransmitter serotonin, important in proper neurobehavioral functioning. It also may be a key participant in the functioning of the immune system and mediate the role of the enteric microbiome. It is proposed here that the increasing empirical understanding of the vagal nerve complex warrants the use of the term cardio-vagal nervous system (CVNS). Notably, the role of the vagus nerve and its role in determining HRV, which has been studied for over 150 years [7], have been expanding rapidly with over 26,000 citations resulting from a recent PubMed search of the terms "HRV" and "heart rate variability."

#### **3. Heart rate variability and the vagal nerve complex**

The term vagus derives from the Latin term for "traveler." As described above, this is apt as the vagal nerve complex is widely distributed throughout the body. Its origins in the subcortical region of the CNS have been identified by Porges [1] as the nucleus ambiguus, the dorsomedial medulla and the nucleus tractus solitarius. These three neural structures represent the primary central regulatory components of the vagal complex and are responsible for three significant functions. The sympathetic mobilization for fight or flight has been recognized for some time, while the dorsal vagal response is a vestigial immobilization/death feigning system and the ventral vagal complex mediates the social engagement system for adaptive motion, emotion and communication. This is perhaps its most important feature to social organisms such as humans, where communication and mutual support have been identified as crucial aspects of evolutionary fitness, contributing to both biological and cultural evolution. This conceptual approach has been called the polyvagal theory by Porges [1] and the neuro-visceral integration model by Thayer and Lane [2].

While these functions are not currently subject to isomorphic assessment, it has been demonstrated empirically that HRV is an accurate and sensitive measure of the actions of these three subsystems. HRV is defined as the instantaneous variability found when continuous R-R intervals in the EKG are recorded [8]. These intervals are easily recorded using both standard 12 lead EKG protocols and a wide variety of freestanding equipment whose quality ranges from adequate to poor. It is impossible to obtain reliable HRV data when the equipment used to acquire the R-R intervals is either lacking reliability or is poor fidelity. If high fidelity quality data are obtained, there are three primary approaches to data analysis that have been found valuable: frequency domain measures, time domain measures and nonlinear measures [9]. Each of these approaches have demonstrated utility, although it has also been shown that some measures are less sensitive to salient phenomena and therefore, selection of the most robust analytic strategy is an important area of ongoing investigation and will be discussed further in the section of algorithmic analysis, artificial intelligence and related issues.

The research literature on HRV indicates that it can be a sensitive biomarker for a wide variety of disorders and conditions [10]. This includes medical disorders such as all-cause mortality, sudden cardiac death, sepsis, myocardial infarction, diabetic neuropathy, transplantation issues, myocardial dysfunction/heart failure and noncardiovascular diseases such as Alzheimer's dementia, epilepsy, diabetes, tetraplegia and liver cirrhosis [11]. It is important to note that in some conditions such as sepsis, the onset of subjective symptoms is often delayed and makes

*Autonomic Nervous System Monitoring - Heart Rate Variability*

**2. HRV and the nervous system**

occupied in the planetary ecosystem.

This chapter will describe the development of heart rate variability (HRV) as a meaningful variable to monitor, interrogate and intervene in the functioning of the human nervous system and the psychophysiological systems it communicates with [1, 2]. It will provide a context for understanding HRV's role in the assessment, maintenance and enhancement of human health, well-being and performance. Further, appropriate techniques for studying HRV will be explored and a variety of applications will be described. Finally, iP4, a systems-based model of human health and optimal performance will be described, and several HRV-based integrated hardware/software systems will be described that exemplify that model.

Since the development of the original neuron approximately 600 billion years ago in worm-like creatures, the nervous system has emerged into increasingly complex and multifunctional neural networks that have vastly increased the adaptive capabilities of those organisms so endowed. This typifies the evolutionary process that has been studied as complexity theory within the systems view life [3]. Recently, the understanding of complex adaptive systems has been aided by the application of non-linear systems dynamics, as a supplement to more traditional linear modes of exploration and understanding. Increasingly complex entities emerge through processes of self-organization in interaction with environments demanding fitness to form adaptive systems which consist of multiple interactive and interdependent coevolving components. In the case of humans, the nervous system has played a decisive role in the increasingly dominant position currently

The central nervous system (CNS), composed of the brain and spinal cord has been historically identified as the most important part of the nervous system for conventional scientific study [4], with Kandel devoting only one chapter out of 64 to the autonomic nervous system (ANS). It is becoming increasingly clear that the peripheral nervous system (PNS) plays a crucial part in the remarkable abilities of humans. In particular, the ANS is known to mediate the sophisticated homeostatic dynamics that allow organisms to maintain a relatively stable interior environment needed to carryout complex adaptive tasks and supports the affective elements that comprise the significant motivational features characteristic of humans. The two major subdivisions of the ANS are the sympathetic and parasympathetic nervous systems [5]. The sympathetic nervous system is associated with energizing the organism during times of threat or challenge. Such activities have been described as "fight or flight" responses. The parasympathetic nervous system has been found to exert calming, stabilizing or reparative effects described as "tend and befriend" responses. A key structural and functional component which modulates the dynamic homeostatic balance is the vagus nerve complex, which originates in the brain stem and is widely connected with major organs such as the heart, lungs, stomach, genitals, pharynx, larynx, facial musculature, and middle ear muscles [1]. In addition to the stress "fight or flight" and calming "tend and befriend" responses, the vagus mediates the equally important "freeze" or immobilization response which is associated with death feigning in many species possessing the vagal nerve complex. These three response elements are integral to HRV, which is defined as the amount of variance in R-R wave intervals, also called the interbeat interval (IBI). The IBI is used to calculate the moment by moment variations in heart rate which

In addition to the systems described above, other neural network systems play significant roles in the overall integrated functioning of the human organism.

**4**

constitutes HRV.

effective intervention difficult or impossible. However, HRV is frequently suppressed before these subjective reports occur, giving a crucial advanced warning of serious developments. HRV has also been shown to be sensitive to psychosocial disorders and dysfunctions such as depression, anxiety, bipolar disorder, attention deficit/hyperactivity disorder, substance abuse/craving disorder and post-traumatic stress disorder. HRV has also been used as a sensitive monitoring strategy in pacing physical training and determining when rest and recovery are indicated to avoid overtraining. Similarly, HRV has been shown to be an indicator of the level of executive functioning and resilience, both positive psychological phenomena. Another positive adaptive measure is the respiratory sinus arrhythmia (RSA), which is observed when HR increases during inhalation and decreases during exhalation. Notably, when a person is near death, the RSA, and therefore, HRV, are diminished or nonexistent. It should be noted that while HRV fulfills the epidemiological virtue of relatively high sensitivity, it does not possess high specificity, and therefore a careful consideration of contextual factors is necessary to make HRV a useful biomarker of health [12, 13]. In general, reduced HRV indicates impairment or dysfunction, while increased HRV shows improved functional or health status.

#### **4. Methodological and technical issues**

Accompanying the explosive growth of interest and research on HRV (12) have come a number of significant issues and problems that can impede progress. While the standard 12 lead EKG protocol is widely used in conventional medicine to produce high quality data, it is cumbersome, obtrusive and expensive. It also lacks the necessary data analytic software capable of recording and interpreting multiple HRV domains. These issues limit accessibility and more widespread use. A number of devices are available for research and clinical applications, some of which use hardwired photoplethysmography and occasionally wireless photoplethysmography accomplished by Bluetooth, relieving the individual from being physically connected to the equipment. Research studies have shown that implanted sensors may acquire interbeat interval data from which HRV can be derived. The interbeat interval data is either stored for later analysis or processed onboard with some type of feedback "HRV" score generated. The actual details regarding the meaning of some composite "HRV" scores is not always clear.

Similar devices are offered on the consumer market, targeting customers wishing to enhance or "fine tune" their physical training regimens. These devices most often use either ring or watch based sensors to obtain interbeat data and reliability data are not generally available, but it is likely that these modes of data collection and analysis contain significant artifacts and other data flaws more accurate chest straps and photoplethysmography sensors are seldom used. A transparent approach to the operation of such devices is desirable to examine both the reliability and validity of such devices, although manufacturers sometimes proclaim proprietary interests which shield them from this type of accountability. A technically feasible approach for interbeat data collection is to use implanted sensor systems which combine high fidelity data acquisition with Bluetooth data transmission and wireless capacitive battery charging so that the device could remain in place for a significant period, even indefinitely. This method would allow collection of more longitudinal data and make data collection in the natural environment relatively simple. Such data would be invaluable in determining a person's baseline state, a feature that is often missing in brief "snapshot" assessments. With such baseline data, a much more meaningful "vital sign" could be available, not only during office visits, but at any other time deemed relevant. It would also popularize the use of

**7**

infographics.

*HRV in an Integrated Hardware/Software System Using Artificial Intelligence to Provide…*

HRV, since one of the drawbacks cited by many individuals is the cumbersome and inconvenient nature of currently available devices. Using the Bluetooth protocol to transmit data to a smart phone would also make data collection and analysis much more accessible, since smart phones are very widely used globally, including in areas with no other communication resources. With appropriate machine learning algorithms, the smart phone could also mediate actionable patient prompting or intervention, including HRV biofeedback. Using the digital epidemiological approach of population surveillance, ongoing monitoring could be available to distant healthcare facilities to prompt more detailed assessment and/or intervention at the individual or group level. Since this approach could involve an implanted device and longitudinal assessment, it might be perceived as more "invasive" and raise privacy and confidentiality issues, especially as used in healthcare contexts. Such concerns are legitimate and would need to be carefully addressed, although data collected by other means is equally worthy of such consideration, especially at this time when individual's data are regularly "harvested" or "scraped" surreptitiously

Until our understanding of HRV improves through machine learning and other artificial intelligence approaches, any one metric amongst the more than a dozen available, is somewhat incomplete. Currently, the SDNN time domain measure (standard deviation of interbeat intervals) is most frequently used and has value. A very comprehensive systemic software suite has been developed by Tarvainen and colleagues [14] called KUBIOS. It is available for analysis of HRV in multiple modes of time domain, frequency domain and nonlinear modes using a batching approach. A current limitation, however, is that KUBIOS does not conduct its analyses in real time, but that limitation is being addressed and a real time version of KUBIOS is in development for purposes of both scientific clarity and consumer use [15]. The KUBIOS platform offers many benefits such as multi-method analytic strategies and clear documentation, making it ideal for increasingly popular big data approaches such as artificial intelligence, machine learning, and high-throughput and cloud computing. Such approaches are especially applicable to the large number

*DOI: http://dx.doi.org/10.5772/intechopen.89042*

by commercial ventures and monetized.

**5. Innovative applications of HRV**

of data points that can be collected in longitudinal HR data.

Given the popularity and "sizzle" of developments in the big data areas of artificial intelligence, machine learning and high-throughput cloud computing, a recent report by Liu and colleagues [16] illustrates an area of high potential value. Building on the work of King and associates [17] on the use of HRV in decision support for trauma patients being transported by helicopter to a trauma center, Liu used machine learning to create predictive models that could detect the need for lifesaving interventions. Their results were near perfect predictions with receiver operating characteristics under the curve = 0.99. While their preliminary report suffered from issues such a relatively small sample size and difficulty extracting high fidelity HRV data, it is still proof of concept that such a systems approach is feasible and relevant. Another important model described by the National Institute of Standards and Technology as the Analysis as a Service (AaaS) has been developed and exemplified by IBM's Watson Analytics (WA), which is a cloud based AaaS that claims to "carry out a number of significant data analysis and display approaches in a user friendly manner" [18]. The Explore and Predict modalities use a variety of data clustering and machine learning approaches that can go far beyond single variable linear prediction, while the Assemble modality develops effective data display and

*HRV in an Integrated Hardware/Software System Using Artificial Intelligence to Provide… DOI: http://dx.doi.org/10.5772/intechopen.89042*

HRV, since one of the drawbacks cited by many individuals is the cumbersome and inconvenient nature of currently available devices. Using the Bluetooth protocol to transmit data to a smart phone would also make data collection and analysis much more accessible, since smart phones are very widely used globally, including in areas with no other communication resources. With appropriate machine learning algorithms, the smart phone could also mediate actionable patient prompting or intervention, including HRV biofeedback. Using the digital epidemiological approach of population surveillance, ongoing monitoring could be available to distant healthcare facilities to prompt more detailed assessment and/or intervention at the individual or group level. Since this approach could involve an implanted device and longitudinal assessment, it might be perceived as more "invasive" and raise privacy and confidentiality issues, especially as used in healthcare contexts. Such concerns are legitimate and would need to be carefully addressed, although data collected by other means is equally worthy of such consideration, especially at this time when individual's data are regularly "harvested" or "scraped" surreptitiously by commercial ventures and monetized.

Until our understanding of HRV improves through machine learning and other artificial intelligence approaches, any one metric amongst the more than a dozen available, is somewhat incomplete. Currently, the SDNN time domain measure (standard deviation of interbeat intervals) is most frequently used and has value. A very comprehensive systemic software suite has been developed by Tarvainen and colleagues [14] called KUBIOS. It is available for analysis of HRV in multiple modes of time domain, frequency domain and nonlinear modes using a batching approach. A current limitation, however, is that KUBIOS does not conduct its analyses in real time, but that limitation is being addressed and a real time version of KUBIOS is in development for purposes of both scientific clarity and consumer use [15]. The KUBIOS platform offers many benefits such as multi-method analytic strategies and clear documentation, making it ideal for increasingly popular big data approaches such as artificial intelligence, machine learning, and high-throughput and cloud computing. Such approaches are especially applicable to the large number of data points that can be collected in longitudinal HR data.

#### **5. Innovative applications of HRV**

Given the popularity and "sizzle" of developments in the big data areas of artificial intelligence, machine learning and high-throughput cloud computing, a recent report by Liu and colleagues [16] illustrates an area of high potential value. Building on the work of King and associates [17] on the use of HRV in decision support for trauma patients being transported by helicopter to a trauma center, Liu used machine learning to create predictive models that could detect the need for lifesaving interventions. Their results were near perfect predictions with receiver operating characteristics under the curve = 0.99. While their preliminary report suffered from issues such a relatively small sample size and difficulty extracting high fidelity HRV data, it is still proof of concept that such a systems approach is feasible and relevant.

Another important model described by the National Institute of Standards and Technology as the Analysis as a Service (AaaS) has been developed and exemplified by IBM's Watson Analytics (WA), which is a cloud based AaaS that claims to "carry out a number of significant data analysis and display approaches in a user friendly manner" [18]. The Explore and Predict modalities use a variety of data clustering and machine learning approaches that can go far beyond single variable linear prediction, while the Assemble modality develops effective data display and infographics.

*Autonomic Nervous System Monitoring - Heart Rate Variability*

**4. Methodological and technical issues**

some composite "HRV" scores is not always clear.

effective intervention difficult or impossible. However, HRV is frequently suppressed before these subjective reports occur, giving a crucial advanced warning of serious developments. HRV has also been shown to be sensitive to psychosocial disorders and dysfunctions such as depression, anxiety, bipolar disorder, attention deficit/hyperactivity disorder, substance abuse/craving disorder and post-traumatic

stress disorder. HRV has also been used as a sensitive monitoring strategy in pacing physical training and determining when rest and recovery are indicated to avoid overtraining. Similarly, HRV has been shown to be an indicator of the level of executive functioning and resilience, both positive psychological phenomena. Another positive adaptive measure is the respiratory sinus arrhythmia (RSA), which is observed when HR increases during inhalation and decreases during exhalation. Notably, when a person is near death, the RSA, and therefore, HRV, are diminished or nonexistent. It should be noted that while HRV fulfills the epidemiological virtue of relatively high sensitivity, it does not possess high specificity, and therefore a careful consideration of contextual factors is necessary to make HRV a useful biomarker of health [12, 13]. In general, reduced HRV indicates impairment or dysfunction, while increased HRV shows improved functional or health status.

Accompanying the explosive growth of interest and research on HRV (12) have come a number of significant issues and problems that can impede progress. While the standard 12 lead EKG protocol is widely used in conventional medicine to produce high quality data, it is cumbersome, obtrusive and expensive. It also lacks the necessary data analytic software capable of recording and interpreting multiple HRV domains. These issues limit accessibility and more widespread use. A number of devices are available for research and clinical applications, some of which use hardwired photoplethysmography and occasionally wireless photoplethysmography accomplished by Bluetooth, relieving the individual from being physically connected to the equipment. Research studies have shown that implanted sensors may acquire interbeat interval data from which HRV can be derived. The interbeat interval data is either stored for later analysis or processed onboard with some type of feedback "HRV" score generated. The actual details regarding the meaning of

Similar devices are offered on the consumer market, targeting customers wishing to enhance or "fine tune" their physical training regimens. These devices most often use either ring or watch based sensors to obtain interbeat data and reliability data are not generally available, but it is likely that these modes of data collection and analysis contain significant artifacts and other data flaws more accurate chest straps and photoplethysmography sensors are seldom used. A transparent approach to the operation of such devices is desirable to examine both the reliability and validity of such devices, although manufacturers sometimes proclaim proprietary interests which shield them from this type of accountability. A technically feasible approach for interbeat data collection is to use implanted sensor systems which combine high fidelity data acquisition with Bluetooth data transmission and wireless capacitive battery charging so that the device could remain in place for a significant period, even indefinitely. This method would allow collection of more longitudinal data and make data collection in the natural environment relatively simple. Such data would be invaluable in determining a person's baseline state, a feature that is often missing in brief "snapshot" assessments. With such baseline data, a much more meaningful "vital sign" could be available, not only during office visits, but at any other time deemed relevant. It would also popularize the use of

**6**

The use of WA by Guidi and colleagues [19] demonstrates proof of concept for a cloud-based data acquisition and analysis system which can make accurate clinical diagnostic decisions differentiating patients with heart failure from normal individuals on the basis of HRV. The process developed by Guidi involves data acquisition using the PhysioBank and PhysioNet to obtain and categorize ECG data into the appropriate format of R to R intervals using the PhysioNet HRV Toolkit [20]. This data consisted of 15 subjects with severe heart failure, 29 subjects with moderate heart failure and 54 healthy subjects with normal respiratory sinus arrhythmia. All subject data was initially collected using standard ECG protocols. The resulting data set was examined by WA and a variety of commonly used HRV statistics were derived. These statistics were compared to the data available in the current literature. This shows the results concerning accuracy of prediction using the Total Power HRV (TOT\_PWR) statistic with a 90% predictive accuracy.

The use of such tools in critical care is exemplary and similar approaches have been suggested by Drury [21] in the areas of more routine clinical care and digital epidemiology. As noted by Topol [22], while the use of the terms artificial intelligence and machine learning has tended to be overblown, the use of existing big data sets and extensive longitudinal data as proposed here is ideal for such an approach and can assist in the laborious and sometimes obscure task of empirical investigation. I have previously suggested [10] using a wireless implantable high fidelity data acquisition device, networked by Bluetooth to a suitable machine learning version of the functionalities possessed by KUBIOS could interrogate the data sets for the creation of the most suitable predictive models for making not only clinical decisions based on changes in patient status, but monitor patients regarding the emergence of new conditions. This type of monitoring could also realize the concept of digital epidemiology, including the use of self-monitoring prompts to aid individuals in appropriate help seeking and self-regulation strategies. See **Figures 1** and **2** to visualize the use of such a system and its ability to discriminate rest, activity and recovery periods [23]. As is indicated by the development of groups such as the quantitative self and biohackers, there is a social demand to assist individuals in optimizing their health and well-being and enhancing their performance in a wide variety of areas. As proposed here, these groups have sometimes used

#### **Figure 1.**

*ReThink wireless biosensor with capability to acquire HR, three-dimension accelerometry and respiration data. These data are then transferred to a Bluetooth enhanced personal device, as well as stored there. Quarter is shown for size comparison, with dimensions of 7.5 mm width, 4 mm height, and 1.4 mm depth (used with permission).*

**9**

**Figure 2.**

*with permission).*

*HRV in an Integrated Hardware/Software System Using Artificial Intelligence to Provide…*

implanted devices to monitor their physiological status. The professional community has perennially spoken through the Institute of Medicine, the National Science Foundation, the National Institutes of Health, the World Health Organization and other organizations of the need for accessible, safe and effective healthcare.

*A subject using the ReThink wireless biosensor during rest, activity and recovery periods. Note that multiple physiological data channels (HR, respiration and accelerometry) are displayed on a laptop computer (used* 

The development and utilization of predictive models derived from artificial intelligence approaches such as machine learning could be beneficial in many aspects of care provided by the current healthcare industry. If clinicians had highly specific empirically based decision support data readily available, their interactions with patients might be more timely, less stressful and "more human," to use Topol's

*DOI: http://dx.doi.org/10.5772/intechopen.89042*

*HRV in an Integrated Hardware/Software System Using Artificial Intelligence to Provide… DOI: http://dx.doi.org/10.5772/intechopen.89042*

#### **Figure 2.**

*Autonomic Nervous System Monitoring - Heart Rate Variability*

HRV (TOT\_PWR) statistic with a 90% predictive accuracy.

The use of WA by Guidi and colleagues [19] demonstrates proof of concept for a cloud-based data acquisition and analysis system which can make accurate clinical diagnostic decisions differentiating patients with heart failure from normal individuals on the basis of HRV. The process developed by Guidi involves data acquisition using the PhysioBank and PhysioNet to obtain and categorize ECG data into the appropriate format of R to R intervals using the PhysioNet HRV Toolkit [20]. This data consisted of 15 subjects with severe heart failure, 29 subjects with moderate heart failure and 54 healthy subjects with normal respiratory sinus arrhythmia. All subject data was initially collected using standard ECG protocols. The resulting data set was examined by WA and a variety of commonly used HRV statistics were derived. These statistics were compared to the data available in the current literature. This shows the results concerning accuracy of prediction using the Total Power

The use of such tools in critical care is exemplary and similar approaches have been suggested by Drury [21] in the areas of more routine clinical care and digital epidemiology. As noted by Topol [22], while the use of the terms artificial intelligence and machine learning has tended to be overblown, the use of existing big data sets and extensive longitudinal data as proposed here is ideal for such an approach and can assist in the laborious and sometimes obscure task of empirical investigation. I have previously suggested [10] using a wireless implantable high fidelity data acquisition device, networked by Bluetooth to a suitable machine learning version of the functionalities possessed by KUBIOS could interrogate the data sets for the creation of the most suitable predictive models for making not only clinical decisions based on changes in patient status, but monitor patients regarding the emergence of new conditions. This type of monitoring could also realize the concept of digital epidemiology, including the use of self-monitoring prompts to aid individuals in appropriate help seeking and self-regulation strategies. See **Figures 1** and **2** to visualize the use of such a system and its ability to discriminate rest, activity and recovery periods [23]. As is indicated by the development of groups such as the quantitative self and biohackers, there is a social demand to assist individuals in optimizing their health and well-being and enhancing their performance in a wide variety of areas. As proposed here, these groups have sometimes used

*ReThink wireless biosensor with capability to acquire HR, three-dimension accelerometry and respiration data. These data are then transferred to a Bluetooth enhanced personal device, as well as stored there. Quarter is shown for size comparison, with dimensions of 7.5 mm width, 4 mm height, and 1.4 mm depth (used with* 

**8**

**Figure 1.**

*permission).*

*A subject using the ReThink wireless biosensor during rest, activity and recovery periods. Note that multiple physiological data channels (HR, respiration and accelerometry) are displayed on a laptop computer (used with permission).*

implanted devices to monitor their physiological status. The professional community has perennially spoken through the Institute of Medicine, the National Science Foundation, the National Institutes of Health, the World Health Organization and other organizations of the need for accessible, safe and effective healthcare.

The development and utilization of predictive models derived from artificial intelligence approaches such as machine learning could be beneficial in many aspects of care provided by the current healthcare industry. If clinicians had highly specific empirically based decision support data readily available, their interactions with patients might be more timely, less stressful and "more human," to use Topol's

phrase, with less time with the care provider on the computer and more face to face interaction. If ongoing HRV monitoring was used to track clinical status of patients, the need for routine exams, especially of the "worried well" would likely decrease. Prompt intervention for patients whose ongoing status was being monitored would be more likely, thus addressing the serious problems of both under and overutilization of medical services. This would accomplish a transition away from the expensive intensive care model to a less expensive, more extensive model. As is the case with the work of Liu's proof of concept, this approach is currently feasible and the elements of such a hardware-software system exist. Using the rubric of artificial intelligence could complete the development of such an approach by creating the necessary predictive models, as I have advocated elsewhere [10].

#### **6. Conclusion: health, well-being and HRV**

It is an oversimplification to suggest that the adoption of the HRV software/ hardware integrated system proposed would resolve the many serious issues that plague the current healthcare environment in the United States. Similarly, Topol of the Scripps Transformational Research Institute observes in his recent Deep Medicine [22] that the increasing use of artificial intelligence (AI) is not a panacea and can only contribute to improving the status quo. In particular, machine learning may make significant progress possible by using the relatively large number of data points generated by HRV and other psychophysiological parameters. It is a truism that both data quality and quantity are crucial in producing the most valuable predictive algorithmic equations. In addition to Topol's astute observations, other physicians such as Agus in the End of Illness [24] and Emanuel in Prescription for the Future [25] have also voiced more nuanced critiques of the healthcare venture in the US, identifying multiple domains of concern. The use of innovations such as machine learning and integrated HRV systems, however, may contribute to the achievement of a reformulated model of health, well-being and healthcare that is both more comprehensive and more tailored to the care of specific individuals. This approach was initially introduced as the human genome was being sequenced as personalized or precision medicine, the implication being that knowledge of the details of the individual's genetic makeup would make for highly specific treatment recommendations perhaps involving genetic engineering. A more multidimensional approach has emerged which addressed the many individual characteristics that each individual possesses; not just genetic, but biochemical, anatomical/physiological, cognitive/affective, social, cultural and spiritual. One of the major limitations of the majority of AI initiatives is the relative neglect of the affective domain of functioning that most distinctively characterizes humans. These multiple domains of individual variability require not only a highly personalized approach, but an integral view of the caring relationship that is participatory, predictive and preventive, as well. I have described this approach as the iP4 health model [10]. The emphasis on an *integral* perspective [26] highlights that each person is a complex adaptive system and that no aspect of their condition is independent from other details of their internal characteristics and external environmental conditions. The focus on *predictive* understanding acknowledges that a variety of risk and resilience factors exist and that any effort to maintain optimal health and well-being must note and plan to deal with such factors. Similarly, a *preventive* approach focuses on identification and early intervention to minimize or eliminate the onset and/ or severity of disease states and promote health. Perhaps most important, we hope to partner with informed individuals and support their decisions that will maximize their *participation* in the care process, making it not only more efficient and

**11**

**Author details**

**Acknowledgements**

**Conflict of interest**

Robert L. Drury1,2

1 ReThink Health, Bainbridge Island, WA, USA

provided the original work is properly cited.

Sesha Robin Hanson-Drury (D.D.S., Ph.D. Cand).

The author declares no conflict of interest.

\*Address all correspondence to: rl.drury@gmail.com

2 Institute for Discovery, University of Wisconsin, Madison, WI, USA

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*HRV in an Integrated Hardware/Software System Using Artificial Intelligence to Provide…*

effective, but also actually demonstrating care for the individual. It is only through a detailed integration of such important issues that a truly *personalized* health care is possible. Since we do not see this as a continuation of the existing approach to patient care, we prefer to designate the individual not as a patient, but a Pioneer. The role which HRV may play in actualizing this model is expanding rapidly and, together with Drs. Steven Porges, Julian Thayer and Jay Ginsberg, I have edited a Research Topic that has jointly appeared in the journals Frontiers in Medicine and Frontiers in Public Health entitled "Heart Rate Variability, Health and Wellbeing: A Systems Perspective" [27]. This series of research papers and reviews summarizes current empirical findings and conceptual bases for applying our understanding of HRV to a wide variety of problems, diseases and issues. Thus, our efforts will not only monitor the various human nervous systems but help to assist and optimize them in their important task of shepherding each individual's health and well-being. Through both scientific and technological investigation using advanced AI tools such as machine learning, and appropriate provider education, a common interactional language must evolve that allows the evaluation of HRV and other relevant parameters to generate actionable feedback, whether decision support for physicians and other healthcare personnel or personal behavioral prompts or interventions for individuals. Though the current state of our understanding is relatively primitive, there is reason to be optimistic that such an evolution is possible.

The author acknowledges the valuable intellectual contributions of Drs. Wasyl Malyj, Steven Porges, Julian Thayer, Jack Ginsberg, Lee Hood, James Spira, Emma Seppala, Fritjof Capra, Steven Strogatz, Francisco Varela, Humberto Maturana and

*DOI: http://dx.doi.org/10.5772/intechopen.89042*

*HRV in an Integrated Hardware/Software System Using Artificial Intelligence to Provide… DOI: http://dx.doi.org/10.5772/intechopen.89042*

effective, but also actually demonstrating care for the individual. It is only through a detailed integration of such important issues that a truly *personalized* health care is possible. Since we do not see this as a continuation of the existing approach to patient care, we prefer to designate the individual not as a patient, but a Pioneer.

The role which HRV may play in actualizing this model is expanding rapidly and, together with Drs. Steven Porges, Julian Thayer and Jay Ginsberg, I have edited a Research Topic that has jointly appeared in the journals Frontiers in Medicine and Frontiers in Public Health entitled "Heart Rate Variability, Health and Wellbeing: A Systems Perspective" [27]. This series of research papers and reviews summarizes current empirical findings and conceptual bases for applying our understanding of HRV to a wide variety of problems, diseases and issues. Thus, our efforts will not only monitor the various human nervous systems but help to assist and optimize them in their important task of shepherding each individual's health and well-being. Through both scientific and technological investigation using advanced AI tools such as machine learning, and appropriate provider education, a common interactional language must evolve that allows the evaluation of HRV and other relevant parameters to generate actionable feedback, whether decision support for physicians and other healthcare personnel or personal behavioral prompts or interventions for individuals. Though the current state of our understanding is relatively primitive, there is reason to be optimistic that such an evolution is possible.

#### **Acknowledgements**

*Autonomic Nervous System Monitoring - Heart Rate Variability*

necessary predictive models, as I have advocated elsewhere [10].

**6. Conclusion: health, well-being and HRV**

phrase, with less time with the care provider on the computer and more face to face interaction. If ongoing HRV monitoring was used to track clinical status of patients, the need for routine exams, especially of the "worried well" would likely decrease. Prompt intervention for patients whose ongoing status was being monitored would be more likely, thus addressing the serious problems of both under and overutilization of medical services. This would accomplish a transition away from the expensive intensive care model to a less expensive, more extensive model. As is the case with the work of Liu's proof of concept, this approach is currently feasible and the elements of such a hardware-software system exist. Using the rubric of artificial intelligence could complete the development of such an approach by creating the

It is an oversimplification to suggest that the adoption of the HRV software/ hardware integrated system proposed would resolve the many serious issues that plague the current healthcare environment in the United States. Similarly, Topol of the Scripps Transformational Research Institute observes in his recent Deep Medicine [22] that the increasing use of artificial intelligence (AI) is not a panacea and can only contribute to improving the status quo. In particular, machine learning may make significant progress possible by using the relatively large number of data points generated by HRV and other psychophysiological parameters. It is a truism that both data quality and quantity are crucial in producing the most valuable predictive algorithmic equations. In addition to Topol's astute observations, other physicians such as Agus in the End of Illness [24] and Emanuel in Prescription for the Future [25] have also voiced more nuanced critiques of the healthcare venture in the US, identifying multiple domains of concern. The use of innovations such as machine learning and integrated HRV systems, however, may contribute to the achievement of a reformulated model of health, well-being and healthcare that is both more comprehensive and more tailored to the care of specific individuals. This approach was initially introduced as the human genome was being sequenced as personalized or precision medicine, the implication being that knowledge of the details of the individual's genetic makeup would make for highly specific treatment recommendations perhaps involving genetic engineering. A more multidimensional approach has emerged which addressed the many individual characteristics that each individual possesses; not just genetic, but biochemical, anatomical/physiological, cognitive/affective, social, cultural and spiritual. One of the major limitations of the majority of AI initiatives is the relative neglect of the affective domain of functioning that most distinctively characterizes humans. These multiple domains of individual variability require not only a highly personalized approach, but an integral view of the caring relationship that is participatory, predictive and preventive, as well. I have described this approach as the iP4 health model [10]. The emphasis on an *integral* perspective [26] highlights that each person is a complex adaptive system and that no aspect of their condition is independent from other details of their internal characteristics and external environmental conditions. The focus on *predictive* understanding acknowledges that a variety of risk and resilience factors exist and that any effort to maintain optimal health and well-being must note and plan to deal with such factors. Similarly, a *preventive* approach focuses on identification and early intervention to minimize or eliminate the onset and/ or severity of disease states and promote health. Perhaps most important, we hope to partner with informed individuals and support their decisions that will maximize their *participation* in the care process, making it not only more efficient and

**10**

The author acknowledges the valuable intellectual contributions of Drs. Wasyl Malyj, Steven Porges, Julian Thayer, Jack Ginsberg, Lee Hood, James Spira, Emma Seppala, Fritjof Capra, Steven Strogatz, Francisco Varela, Humberto Maturana and Sesha Robin Hanson-Drury (D.D.S., Ph.D. Cand).

#### **Conflict of interest**

The author declares no conflict of interest.

#### **Author details**

Robert L. Drury1,2

1 ReThink Health, Bainbridge Island, WA, USA

2 Institute for Discovery, University of Wisconsin, Madison, WI, USA

\*Address all correspondence to: rl.drury@gmail.com

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **References**

[1] Porges S. The Polyvagal Theory. New York: Norton; 2011

[2] Thayer JF, Lane RD. Claude Bernard and the heart-brain connection: Further elaboration of a model of neurovisceral integration. Neuroscience and Biobehavioral Reviews. 2009;**33**(2):81-88

[3] Capra F, Luisi L. The Systems View of Life. Delhi: Cambridge University Press; 2014

[4] Kandel E et al. Neural Science. 5th ed. New York: McGraw; 2018

[5] Squire L et al. Fundamental Neuroscience. 4th ed. Burlington, MA: Academic Press; 2012

[6] Purves D et al. Neuroscience. 5th ed. Sunderland, MA: Sinauer; 2012

[7] Bainbridge FA. The relation between respiration and the pulse rate. The Journal of Physiology. 1920;**54**:192-202

[8] Cacioppo J et al. Handbook of Psychophysiology. 4th ed. New York: Cambridge University Press; 2016

[9] European Society of Cardiology and North American Society of Pacing and Electrophysiology. Heart rate variability: Standards of measurement, physiological interpretation and clinical use. Task force of the European Society of Cardiology and the north American Society of Pacing and Electrophysiology. Circulation. 1996;**93**:1043-1068

[10] Drury R. Unraveling health as a complex adaptive system: Data mining, cloud computing, machine learning, and biosensors as synergistic technologies. International Journal of Genomics and Data Mining. 2017;**J105**:1-4

[11] Sammito S, Bockelman E. Factors influencing heart rate variability.

International Cardiovascular Forum Journal. 2016;**6**:18-22

[12] Thayer JF, Ahs F, Fredrikson M, Sollers JJ 3rd, Wager TD. A metaanalysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health. Neuroscience and Biobehavioral Reviews. 2012;**36**(2):747-756

[13] Compos M. Heart Rate Variability: A New Way to Track Well-Being. Cambridge: Harvard Health Blog, Harvard Medical School; 2017

[14] Tarvainen MP et al. Kubios HRVheart rate variability analysis software. Computer Methods and Programs in Biomedicine. 2014;**113**(1):210-220

[15] Haddad G. Medical Director. Seattle, WA: P4 Medical Institute. Personal Communication

[16] Liu NT et al. Utility of vital signs, heart-rate variability and complexity, and machine learning for identifying the need for life-saving interventions in trauma patients. Shock. 2014. pp. 10,1097

[17] King DR, Ogilvie MP, Pereira BM, Chang Y, Manning RJ, et al. Heart rate variability as a triage tool in patients with trauma during prehospital helicopter transport. The Journal of Trauma. 2009;**67**:436-440

[18] IBM Watson Analytics: A Smart Data Discovery Service Available on the Cloud, it Guides Data Exploration, Automates Predictive Analytics and Enables Effortless Dashboard and Infographic Creation. Available from: www.ibm.com [Accessed: 10 July 2019]

[19] Guidi G, Roberto M, Matteo M, Ernesto I. Case study: IBM Watson

**13**

*HRV in an Integrated Hardware/Software System Using Artificial Intelligence to Provide…*

*DOI: http://dx.doi.org/10.5772/intechopen.89042*

analytics cloud platform as analytics-asa-service system for heart failure early detection. Future Internet. 2016;**8**:32

[20] PhysioNet. PhysioNet is supported by the National Institute of General Medical Sciences (NIGMS) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number 2R01GM104987-09. N.D. Available from: www.physionet.org [Accessed: 15

[21] Drury R. Wearable biosensor systems and resilience: A perfect storm in health care? Frontiers in Psychology.

[23] Drury R, Malyj W, Phares R. Resilience enhancement: A psychoeducational intervention using miniaturized electronic data collection and analysis. In: Presented at the American Medical Informatics Association Meeting ; San Francisco,

[24] Agus D. The End of Illness. New York: Free Press; 2012

ed. Philadelphia: Elsevier; 2019

[27] Drury R, Porges S, Thayer J, Ginsberg J, editors. Heart Rate Variability, Health and Wellbeing: A Systems Perspective. Special Research Topic in Frontiers in Medicine and Frontiers in Public Health, 2018

[25] Emanuel E. Prescription for the Future. New York: Public Affairs; 2017

[26] Rakel D. Integrative Medicine. 4th

[22] Topol E. Deep Medicine. New York:

March 2017]

2014;**5**:853

CA; 2010

Hatchett; 2019

*HRV in an Integrated Hardware/Software System Using Artificial Intelligence to Provide… DOI: http://dx.doi.org/10.5772/intechopen.89042*

analytics cloud platform as analytics-asa-service system for heart failure early detection. Future Internet. 2016;**8**:32

[20] PhysioNet. PhysioNet is supported by the National Institute of General Medical Sciences (NIGMS) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number 2R01GM104987-09. N.D. Available from: www.physionet.org [Accessed: 15 March 2017]

[21] Drury R. Wearable biosensor systems and resilience: A perfect storm in health care? Frontiers in Psychology. 2014;**5**:853

[22] Topol E. Deep Medicine. New York: Hatchett; 2019

[23] Drury R, Malyj W, Phares R. Resilience enhancement: A psychoeducational intervention using miniaturized electronic data collection and analysis. In: Presented at the American Medical Informatics Association Meeting ; San Francisco, CA; 2010

[24] Agus D. The End of Illness. New York: Free Press; 2012

[25] Emanuel E. Prescription for the Future. New York: Public Affairs; 2017

[26] Rakel D. Integrative Medicine. 4th ed. Philadelphia: Elsevier; 2019

[27] Drury R, Porges S, Thayer J, Ginsberg J, editors. Heart Rate Variability, Health and Wellbeing: A Systems Perspective. Special Research Topic in Frontiers in Medicine and Frontiers in Public Health, 2018

**12**

*Autonomic Nervous System Monitoring - Heart Rate Variability*

International Cardiovascular Forum

[12] Thayer JF, Ahs F, Fredrikson M, Sollers JJ 3rd, Wager TD. A metaanalysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health. Neuroscience and Biobehavioral Reviews.

[13] Compos M. Heart Rate Variability: A New Way to Track Well-Being. Cambridge: Harvard Health Blog, Harvard Medical School; 2017

[14] Tarvainen MP et al. Kubios HRVheart rate variability analysis software. Computer Methods and Programs in Biomedicine. 2014;**113**(1):210-220

[15] Haddad G. Medical Director. Seattle, WA: P4 Medical Institute.

[16] Liu NT et al. Utility of vital signs, heart-rate variability and complexity, and machine learning for identifying the need for life-saving interventions in trauma patients. Shock. 2014. pp.

[17] King DR, Ogilvie MP, Pereira BM, Chang Y, Manning RJ, et al. Heart rate variability as a triage tool in patients with trauma during prehospital helicopter transport. The Journal of

[18] IBM Watson Analytics: A Smart Data Discovery Service Available on the Cloud, it Guides Data Exploration, Automates Predictive Analytics and Enables Effortless Dashboard and Infographic Creation. Available from: www.ibm.com [Accessed: 10 July

[19] Guidi G, Roberto M, Matteo M, Ernesto I. Case study: IBM Watson

Personal Communication

Trauma. 2009;**67**:436-440

10,1097

2019]

Journal. 2016;**6**:18-22

2012;**36**(2):747-756

[1] Porges S. The Polyvagal Theory.

[2] Thayer JF, Lane RD. Claude Bernard and the heart-brain connection: Further elaboration of a model of neurovisceral integration. Neuroscience

[3] Capra F, Luisi L. The Systems View of Life. Delhi: Cambridge University

[4] Kandel E et al. Neural Science. 5th

Neuroscience. 4th ed. Burlington, MA:

[6] Purves D et al. Neuroscience. 5th ed.

[7] Bainbridge FA. The relation between respiration and the pulse rate. The Journal of Physiology. 1920;**54**:192-202

ed. New York: McGraw; 2018

[5] Squire L et al. Fundamental

Sunderland, MA: Sinauer; 2012

[8] Cacioppo J et al. Handbook of Psychophysiology. 4th ed. New York: Cambridge University Press; 2016

[9] European Society of Cardiology and North American Society of Pacing and Electrophysiology. Heart rate variability: Standards of measurement, physiological interpretation and clinical use. Task force of the European Society of Cardiology and the north American Society of Pacing and Electrophysiology.

Circulation. 1996;**93**:1043-1068

Data Mining. 2017;**J105**:1-4

[10] Drury R. Unraveling health as a complex adaptive system: Data mining, cloud computing, machine learning, and biosensors as synergistic technologies. International Journal of Genomics and

[11] Sammito S, Bockelman E. Factors influencing heart rate variability.

Academic Press; 2012

New York: Norton; 2011

**References**

and Biobehavioral Reviews.

2009;**33**(2):81-88

Press; 2014

**15**

**Chapter 2**

*Giovanni Minarini*

come can change and what does it mean.

cardiac mechanism, ANS influence

change of homeostatic state [1, 3].

**1. Introduction**

**Abstract**

Root Mean Square of the

after ANS Stimulation

Successive Differences as Marker

of the Parasympathetic System

and Difference in the Outcome

The autonomic nervous system has a huge impact on the cardiac regulatory mechanism, and many markers exist for evaluating it. In this chapter we are going to focus on the RMSSD (Root mean square of successive differences), considered the most precise marker for the parasympathetic effector on the heart. Before is necessary to learn what the Heart Rate Variability is and how it works, which type of range of HRV exists and how we can measure it. Finally, there will be a presentation of how the RMSSD can be used in different field, and how and why the out-

parasympathetic nervous system, root mean square of successive differences, heart,

*Homeostasis* can be defined as the result of the stability of physiological systems that maintain life; it applies strictly to a limited number of systems such as regulation of pH, concentration of different ions in the extracellular fluid, osmolality of extracellular fluid, glucose levels and arterial oxygen tension, which are truly essential for life and are therefore maintained within a narrow range for the current life history stage [1]. The homeostatic balance is considered as a change of state compatible with the actual environmental situations [2]. The temporary variations between the "set point" of the homeostatic control system during adaptation to internal (i.e. digestion) or external (i.e. climatic condition) perturbations are called *allostasis* [1]. Thus, allostasis is the reaching of physiological stability through a

The allostatic adaptations during environmental changes are temporary processes; if not turned off when not needed, if they occur too frequently or fail to occur at all, there may be development of systemic disease(s) such as cardiovascular disease, type II diabetes, obesity, etc. [4–6]. Allostatic and homeostatic control are ruled by the autonomic nervous system (ANS) integrated within the central

**Keywords:** autonomic nervous system, sympathetic nervous system,

#### **Chapter 2**

## Root Mean Square of the Successive Differences as Marker of the Parasympathetic System and Difference in the Outcome after ANS Stimulation

*Giovanni Minarini*

#### **Abstract**

The autonomic nervous system has a huge impact on the cardiac regulatory mechanism, and many markers exist for evaluating it. In this chapter we are going to focus on the RMSSD (Root mean square of successive differences), considered the most precise marker for the parasympathetic effector on the heart. Before is necessary to learn what the Heart Rate Variability is and how it works, which type of range of HRV exists and how we can measure it. Finally, there will be a presentation of how the RMSSD can be used in different field, and how and why the outcome can change and what does it mean.

**Keywords:** autonomic nervous system, sympathetic nervous system, parasympathetic nervous system, root mean square of successive differences, heart, cardiac mechanism, ANS influence

#### **1. Introduction**

*Homeostasis* can be defined as the result of the stability of physiological systems that maintain life; it applies strictly to a limited number of systems such as regulation of pH, concentration of different ions in the extracellular fluid, osmolality of extracellular fluid, glucose levels and arterial oxygen tension, which are truly essential for life and are therefore maintained within a narrow range for the current life history stage [1]. The homeostatic balance is considered as a change of state compatible with the actual environmental situations [2]. The temporary variations between the "set point" of the homeostatic control system during adaptation to internal (i.e. digestion) or external (i.e. climatic condition) perturbations are called *allostasis* [1]. Thus, allostasis is the reaching of physiological stability through a change of homeostatic state [1, 3].

The allostatic adaptations during environmental changes are temporary processes; if not turned off when not needed, if they occur too frequently or fail to occur at all, there may be development of systemic disease(s) such as cardiovascular disease, type II diabetes, obesity, etc. [4–6]. Allostatic and homeostatic control are ruled by the autonomic nervous system (ANS) integrated within the central

nervous system (CNS) [1]. A disorder of the ANS can affect the homeostatic and allostatic processes, leading to a risk of developing systemic disorder such as hypertension [7] baroreflex failure for blood pressure regulation [8], type II diabetes or affect the immune system and the inflammatory process [9]. Moreover, this has been demonstrated how the ANS is strictly correlated to the modulation of pain perceived by the subject.

One of the most common markers of the ANS is the rMSSD, square root of the mean squared differences of successive NN intervals. It is evaluated through the Heart Rate Variability (HRV) and the distance between the peaks of the R values in the echocardiogram. The rMSSD allowed the researcher to monitor the alteration in the parasympathetic activity with good precision. In this chapter will be explained how the rMSDD is related to parasympathetic nervous system, what could modulate the outcome and what a different result mean.

#### **2. Heart rate variability**

Heart rate variability consists of changes in the time intervals between consecutive heartbeats called interbeat intervals (IBIs) [10]. A healthy heart is not a metronome. The oscillations of a healthy heart are complex and constantly changing, which allow the cardiovascular system to rapidly adjust to sudden physical and psychological challenges to homeostasis.

The HRV is the fluctuation in time intervals between adjacent heartbeats, it indexes neurocardiac function and is generated by heart-brain interactions and dynamic non-linear autonomic nervous system (ANS) processes. HRV is an emergent property of interdependent regulatory systems which operate on different time scales to help us adapt to environmental and psychological challenges by stimulating and regulating some vascular component of the allostasis: HRV reflects regulation of autonomic balance, blood pressure (BP), gas exchange, gut, heart, and vascular tone, which refers to the diameter of the blood vessels that regulate BP, and possibly facial muscles [11].

Higher HRV is not always associated to better state of health of the subjects, numerous diseases affect the HRV and has the potential to increase this value. When cardiac conduction abnormalities cause an increase in the HRV, this is strongly linked to increased risk of mortality, particularly among the elderly (e.g. causing atrial fibrillation) [12].

Despite that has been demonstrated how optimal level of HRV are associated to health and self-regulatory capacity, adaptability and resilience [13, 14]. This is due to the vagal modulation of the HRV: heart rate (HR) estimated at any given time represents the net effect of the neural output of the parasympathetic (vagus) nerves, which slow HR, and the sympathetic nerves, which accelerate it. Opthof published a study on the normal range and the determinants of intrinsic heart rate in man, following the main research done before on the subject by Jose an Collins in 1970: he found that a denervated human heart, with no connections to the ANS, the intrinsic rate generated by the pacemaker, the Senoatrial Node (SA), is near to 100 beats per minute [15]. Whenever the rate decrease below this level, it means that a parasympathetic outflow is predominating in the balance between sympathetic and parasympathetic activity. This happens usually during normal daily activities, at rest and when we sleep. On the contrary, if the ratio raises over 100 beats the shift is toward the sympathetic system. The average 24 h HR in healthy people is approximately 73 bpm. Higher HRs are independent markers of mortality in a wide spectrum of conditions [16].

**17**

*Root Mean Square of the Successive Differences as Marker of the Parasympathetic System…*

Sinus node pacemaker cells activity is continuously under regulation by specific

The SA node is targeted by the descending efferent sympathetic nerves via the intrinsic cardiac nervous system and the bulk of the myocardium. Norepinephrine and epinephrine release, which increases HR and strengthens the contractility of the atria and ventricles, is triggered by these motor neurons action potentials. Subsequent the onset of sympathetic stimulus, there is a delay of up to 5 seconds before the stimulation induces a progressive rise in HR, which reaches a stable level in 20–30 seconds if the stimulus is continuous [17]. A sympathetic stimulus, even if brief, can easily affect the HRV rhythm for 5–10 seconds. This is in contrast with the vagal stimulation, which is almost instantaneous, due to the acetylcholine degradation mechanism [17, 18], we will see that later on this chapter. What does that mean? That any sudden changes in the HR, up or down, or between the beat, is primarily

The vagus nerves innervate the intrinsic cardiac nervous system. Inside the intrinsic cardiac nervous systems are present some synapse between vagus nerve and motor neurons that directly project to the sinoatrial node and a portion of the surrounding tissue. They trigger acetylcholine release to slow HR. [19] However, more than 80% of the efferent preganglionic vagal neurons has connection to local circuitry neurons in the intrinsic cardiac nervous system where motor information

The single efferent vagal stimulation on the SA node is very short, resulting in an immediate response that typically occurs within the cardiac circle in which it occurs, affecting only 1 or 2 heartbeats after its onset [17]. After cessation of vagal stimulation, HR rapidly increases to its previous level. An increase in heart rate can also be achieved by reduced vagal activity, or vagal withdrawal. Hence, any sudden increase or decrease HR, between 1 beat and the next, are primarily parasympathetically mediated [17, 18]. The medulla oblongata is the major structure integrating incoming afferent information from the heart, lungs and face with inputs from cortical and subcortical structures and is the source of the respiratory modulation of the activity patterns in sympathetic and parasympathetic outflow. The intrinsic cardiac nervous system integrates mechanosensitive and chemosensitive neuron inputs with efferent information from both the sympathetic and parasympathetic inputs from the brain. As a complete system, it affects HRV, vasoconstriction, venoconstriction,

The European Society of Cardiology and the North American Society of Pacing and Electrophysiology Task Force Report on HRV divided heart rhythm oscillations into 4 primary frequency bands: high-frequency (HF), low-frequency (LF), very-lowfrequency (VLF), and ultra-low-frequency (ULF) [20]. Most HRV analysis is done in 5-min segments (of a 24 h recording), although other recording periods are often used. When other recording lengths are analyzed, the length of the recording should be reported since this has large effects on both HRV frequency and time domain values.

The HF range is from 0.15 to 0.4 Hz, which correspond to a rhythm period between 2.5 and 7 seconds. This band is called the "respiratory band" because

**2.1 Sympathetic and parasympathetic pathways on the heart**

*DOI: http://dx.doi.org/10.5772/intechopen.89827*

mediated by the parasympathetic nervous system.

and cardiac contractility in order to regulate HR and BP [17].

**2.2 Heart rate variability frequency band**

*2.2.1 High-frequency band*

is integrated with inputs from T.

neural mechanism.

*Root Mean Square of the Successive Differences as Marker of the Parasympathetic System… DOI: http://dx.doi.org/10.5772/intechopen.89827*

#### **2.1 Sympathetic and parasympathetic pathways on the heart**

Sinus node pacemaker cells activity is continuously under regulation by specific neural mechanism.

The SA node is targeted by the descending efferent sympathetic nerves via the intrinsic cardiac nervous system and the bulk of the myocardium. Norepinephrine and epinephrine release, which increases HR and strengthens the contractility of the atria and ventricles, is triggered by these motor neurons action potentials. Subsequent the onset of sympathetic stimulus, there is a delay of up to 5 seconds before the stimulation induces a progressive rise in HR, which reaches a stable level in 20–30 seconds if the stimulus is continuous [17]. A sympathetic stimulus, even if brief, can easily affect the HRV rhythm for 5–10 seconds. This is in contrast with the vagal stimulation, which is almost instantaneous, due to the acetylcholine degradation mechanism [17, 18], we will see that later on this chapter. What does that mean? That any sudden changes in the HR, up or down, or between the beat, is primarily mediated by the parasympathetic nervous system.

The vagus nerves innervate the intrinsic cardiac nervous system. Inside the intrinsic cardiac nervous systems are present some synapse between vagus nerve and motor neurons that directly project to the sinoatrial node and a portion of the surrounding tissue. They trigger acetylcholine release to slow HR. [19] However, more than 80% of the efferent preganglionic vagal neurons has connection to local circuitry neurons in the intrinsic cardiac nervous system where motor information is integrated with inputs from T.

The single efferent vagal stimulation on the SA node is very short, resulting in an immediate response that typically occurs within the cardiac circle in which it occurs, affecting only 1 or 2 heartbeats after its onset [17]. After cessation of vagal stimulation, HR rapidly increases to its previous level. An increase in heart rate can also be achieved by reduced vagal activity, or vagal withdrawal. Hence, any sudden increase or decrease HR, between 1 beat and the next, are primarily parasympathetically mediated [17, 18].

The medulla oblongata is the major structure integrating incoming afferent information from the heart, lungs and face with inputs from cortical and subcortical structures and is the source of the respiratory modulation of the activity patterns in sympathetic and parasympathetic outflow. The intrinsic cardiac nervous system integrates mechanosensitive and chemosensitive neuron inputs with efferent information from both the sympathetic and parasympathetic inputs from the brain. As a complete system, it affects HRV, vasoconstriction, venoconstriction, and cardiac contractility in order to regulate HR and BP [17].

#### **2.2 Heart rate variability frequency band**

The European Society of Cardiology and the North American Society of Pacing and Electrophysiology Task Force Report on HRV divided heart rhythm oscillations into 4 primary frequency bands: high-frequency (HF), low-frequency (LF), very-lowfrequency (VLF), and ultra-low-frequency (ULF) [20]. Most HRV analysis is done in 5-min segments (of a 24 h recording), although other recording periods are often used. When other recording lengths are analyzed, the length of the recording should be reported since this has large effects on both HRV frequency and time domain values.

#### *2.2.1 High-frequency band*

The HF range is from 0.15 to 0.4 Hz, which correspond to a rhythm period between 2.5 and 7 seconds. This band is called the "respiratory band" because

*Autonomic Nervous System Monitoring - Heart Rate Variability*

the outcome and what a different result mean.

psychological challenges to homeostasis.

and possibly facial muscles [11].

(e.g. causing atrial fibrillation) [12].

spectrum of conditions [16].

perceived by the subject.

**2. Heart rate variability**

nervous system (CNS) [1]. A disorder of the ANS can affect the homeostatic and allostatic processes, leading to a risk of developing systemic disorder such as hypertension [7] baroreflex failure for blood pressure regulation [8], type II diabetes or affect the immune system and the inflammatory process [9]. Moreover, this has been demonstrated how the ANS is strictly correlated to the modulation of pain

One of the most common markers of the ANS is the rMSSD, square root of the mean squared differences of successive NN intervals. It is evaluated through the Heart Rate Variability (HRV) and the distance between the peaks of the R values in the echocardiogram. The rMSSD allowed the researcher to monitor the alteration in the parasympathetic activity with good precision. In this chapter will be explained how the rMSDD is related to parasympathetic nervous system, what could modulate

Heart rate variability consists of changes in the time intervals between consecutive heartbeats called interbeat intervals (IBIs) [10]. A healthy heart is not a metronome. The oscillations of a healthy heart are complex and constantly changing, which allow the cardiovascular system to rapidly adjust to sudden physical and

The HRV is the fluctuation in time intervals between adjacent heartbeats, it indexes neurocardiac function and is generated by heart-brain interactions and dynamic non-linear autonomic nervous system (ANS) processes. HRV is an emergent property of interdependent regulatory systems which operate on different time scales to help us adapt to environmental and psychological challenges by stimulating and regulating some vascular component of the allostasis: HRV reflects regulation of autonomic balance, blood pressure (BP), gas exchange, gut, heart, and vascular tone, which refers to the diameter of the blood vessels that regulate BP,

Higher HRV is not always associated to better state of health of the subjects, numerous diseases affect the HRV and has the potential to increase this value. When cardiac conduction abnormalities cause an increase in the HRV, this is strongly linked to increased risk of mortality, particularly among the elderly

Despite that has been demonstrated how optimal level of HRV are associated to health and self-regulatory capacity, adaptability and resilience [13, 14]. This is due to the vagal modulation of the HRV: heart rate (HR) estimated at any given time represents the net effect of the neural output of the parasympathetic (vagus) nerves, which slow HR, and the sympathetic nerves, which accelerate it. Opthof published a study on the normal range and the determinants of intrinsic heart rate in man, following the main research done before on the subject by Jose an Collins in 1970: he found that a denervated human heart, with no connections to the ANS, the intrinsic rate generated by the pacemaker, the Senoatrial Node (SA), is near to 100 beats per minute [15]. Whenever the rate decrease below this level, it means that a parasympathetic outflow is predominating in the balance between sympathetic and parasympathetic activity. This happens usually during normal daily activities, at rest and when we sleep. On the contrary, if the ratio raises over 100 beats the shift is toward the sympathetic system. The average 24 h HR in healthy people is approximately 73 bpm. Higher HRs are independent markers of mortality in a wide

**16**

correspond to the HR variations related to the respiratory cycle, known also as "respiratory sinus arrhythmia". Is conventionally recorded over a minimum 1min period. For infants and children, who breathe faster than adults, the resting range can be adjusted to 0.24–1.04Hz [21]. A complex regulatory mechanism involving both central and reflex interaction is the main organizer of this system. During inhalation there is an acceleration in the heart rate due to an inhibition of the vagal/ outflow from the cardio-respiratory center. On the opposite, while exhaling, the vagal outflow is restored to a normal level resulting in slowing the HR.

The HF modulation has also psychological involvement: reduced vagally mediated HRV has been related to a reduced self-regulatory capacity and cognitive functions that involve the executive centers of the prefrontal cortex. This is consistent with the finding that lower HF power is associated with stress, panic, anxiety, or worry. It has to be noted how this reaction is due a reduction of the parasympathetic activity, and not to an increase in sympathetic ones. This has been shown by numerous studies, where a total vagal blockade obtained pharmacologically eliminates the HF oscillations and reduces power in the LF range, resulting in a strong reduction of the HRV, including LF and VLF bands. Thus, they concluded that HRV is a resultant of the parasympathetic mechanism.

High-frequency power is highly correlated with the pNN50 and RMSSD timedomain measures [22]. HF band power may increase at night and decrease during the day [10].

#### *2.2.2 Low-frequency band*

LF range is between 0.04 and 0.15 Hz, which equates to rhythms or modulations periods between 7 and 25 seconds. Is typically recorded over a minimum 2min period [23]. This range of action was called the "baroreceptor range" or "mid-frequency band", due to its strong correlation with baroreceptor activity at rest [10, 24]. Baroreceptors are stretch-sensitive mechanoreceptors located in vena cavae, carotid sinuses, aortic arch and heart chambers. The ones found in the carotid are the most sensitive. Baroreflex is transported to the brain by the vagus nerve and represent the beat-to-beat change in HR per unit of change in systolic blood pressure [25]. A decreased baroreflex is related to aging and weakened regulatory capacity [26].

There is a different influence of the sympathetic and parasympathetic system inside this band, due to the rhythms: above 0.1 Hz the SNS seems to be lesser influent, whilst the parasympathetic affect heart rhythms down to 0.05 Hz [27, 28]. There rhythms are obtained during slow respiration rates, where a vagal activity easily generates oscillations in the heart rhythms crossing into the LF band [29, 30]. Therefore, when the respiratory rates are below 8.5 per minutes, or 1 in 7 seconds, or when a subject take a deep breath there is a vagal mediation influence.

Despite has been generally accepted the LF band has a marker for the sympathetic activity, and the LF/HF ratio is used to assess the balance between SNS and PNS, is still not totally clear if in resting condition the Low Frequency band reflect the baroreflex activity instead of the cardiac sympathetic innervation [31–33].

#### *2.2.3 Very-low-frequency band*

The VLF is the power in the range between 0.0033 and 0.04 Hz, which equates to rhythms or modulations with periods that occur between 25 and 300 seconds. Although all 24 h clinical measures of HRV reflecting low HRV are linked with increased risk of adverse outcomes, the VLF band has stronger associations with allcause mortality than the LF and HF bands [34–37]. Low VLF power has been shown

**19**

*Root Mean Square of the Successive Differences as Marker of the Parasympathetic System…*

to be associated with arrhythmic death [38] and posttraumatic stress disorder (PTSD) [39]. Moreover, low power expression in this band has been associated with high inflammation [40, 41] and has been correlated with low levels of testosterone. In contrast, other biochemical markers, such as those mediated by the hypothalamic–pituitary–adrenal (HPA) *Axis axis* (e.g., cortisol), did not [42]. Longer time periods using 24 h HRV recordings should be obtained to provide comprehensive

Historically, is still not well defined the physiological explanation and mechanisms involved in the generation of the VLF component, compared to the LF and HF components. Despite the accuracy and the most predictive outcomes, this area has been largely ignored even. Long-term regulatory mechanisms and ANS activity related to thermoregulation, the renin-angiotensin system, and other hormonal

Very-low-frequency power is strongly correlated with the SDNNI time-domain measure, which averages 5min standard deviations for all NN intervals over a 24 h period. There is uncertainty regarding the physiological mechanisms responsible for activity within this band [14]. The heart's intrinsic nervous system appears to contribute to the VLF rhythm and the SNS influences the amplitude and frequency

Based on work by Armor [45] and Kember et al. [32, 46], the VLF rhythm appears to be generated by the stimulation of afferent sensory neurons in the heart. This, in turn, activates various levels of the feedback and feed-forward loops in the heart's intrinsic cardiac nervous system, as well as between the heart, the extrinsic cardiac ganglia, and spinal column. This experimental evidence suggests that the heart intrinsically generates the VLF rhythm and efferent SNS activity due to physi-

The ultra-low-frequency band (ULF) falls below 0.0033 Hz (333 seconds or 5.6 minutes). Oscillations or events in the heart rhythm with a period of 5 minutes or greater are reflected in this band and it can only be assessed with 24 h and longer recordings [22]. The circadian oscillation in HR is the primary source of the ULF power, although other very slow-acting regulatory processes, such as core body temperature regulation, metabolism, and the renin-angiotensin system likely add to the power in this band [20]. The Task Force Report on HRV suggests that 24 h recordings should be divided into 5-min segments and that HRV analysis should be performed on the individual segments prior to the calculation of mean values. This effectively filters out any oscillations with periods longer than 5 minutes. However, when spectral analysis is applied to entire 24 h records, several lower frequency

There is disagreement about the contribution by the PNS and SNS to this band. Different psychiatric disorders show distinct circadian patterns in 24h HRs, par-

Three types of measurement exist for the HRV, time-domain index, frequencydomain index and non-linear measurements. Time-domain indices quantify the amount of HRV observed during monitoring periods that may range from ~2min to 24h. Frequency-domain values calculate the absolute or relative amount of signal energy within component bands. *Non-linear measurements* allow us to quantify the

cal activity and stress responses modulates its amplitude and frequency.

rhythms are easily detected in healthy individuals [23].

*DOI: http://dx.doi.org/10.5772/intechopen.89827*

assessment of VLF and ULF fluctuations [22].

factors appear to contribute to this band [43, 44].

of its oscillations [20].

*2.2.4 Ultra-low-frequency band*

ticularly during sleep [25, 47].

unpredictability of a time series.

**2.3 Heart rate variability measurement**

#### *Root Mean Square of the Successive Differences as Marker of the Parasympathetic System… DOI: http://dx.doi.org/10.5772/intechopen.89827*

to be associated with arrhythmic death [38] and posttraumatic stress disorder (PTSD) [39]. Moreover, low power expression in this band has been associated with high inflammation [40, 41] and has been correlated with low levels of testosterone. In contrast, other biochemical markers, such as those mediated by the hypothalamic–pituitary–adrenal (HPA) *Axis axis* (e.g., cortisol), did not [42]. Longer time periods using 24 h HRV recordings should be obtained to provide comprehensive assessment of VLF and ULF fluctuations [22].

Historically, is still not well defined the physiological explanation and mechanisms involved in the generation of the VLF component, compared to the LF and HF components. Despite the accuracy and the most predictive outcomes, this area has been largely ignored even. Long-term regulatory mechanisms and ANS activity related to thermoregulation, the renin-angiotensin system, and other hormonal factors appear to contribute to this band [43, 44].

Very-low-frequency power is strongly correlated with the SDNNI time-domain measure, which averages 5min standard deviations for all NN intervals over a 24 h period. There is uncertainty regarding the physiological mechanisms responsible for activity within this band [14]. The heart's intrinsic nervous system appears to contribute to the VLF rhythm and the SNS influences the amplitude and frequency of its oscillations [20].

Based on work by Armor [45] and Kember et al. [32, 46], the VLF rhythm appears to be generated by the stimulation of afferent sensory neurons in the heart. This, in turn, activates various levels of the feedback and feed-forward loops in the heart's intrinsic cardiac nervous system, as well as between the heart, the extrinsic cardiac ganglia, and spinal column. This experimental evidence suggests that the heart intrinsically generates the VLF rhythm and efferent SNS activity due to physical activity and stress responses modulates its amplitude and frequency.

#### *2.2.4 Ultra-low-frequency band*

*Autonomic Nervous System Monitoring - Heart Rate Variability*

of the parasympathetic mechanism.

the day [10].

*2.2.2 Low-frequency band*

regulatory capacity [26].

*2.2.3 Very-low-frequency band*

correspond to the HR variations related to the respiratory cycle, known also as "respiratory sinus arrhythmia". Is conventionally recorded over a minimum 1min period. For infants and children, who breathe faster than adults, the resting range can be adjusted to 0.24–1.04Hz [21]. A complex regulatory mechanism involving both central and reflex interaction is the main organizer of this system. During inhalation there is an acceleration in the heart rate due to an inhibition of the vagal/ outflow from the cardio-respiratory center. On the opposite, while exhaling, the

The HF modulation has also psychological involvement: reduced vagally mediated HRV has been related to a reduced self-regulatory capacity and cognitive functions that involve the executive centers of the prefrontal cortex. This is consistent with the finding that lower HF power is associated with stress, panic, anxiety, or worry. It has to be noted how this reaction is due a reduction of the parasympathetic activity, and not to an increase in sympathetic ones. This has been shown by numerous studies, where a total vagal blockade obtained pharmacologically eliminates the HF oscillations and reduces power in the LF range, resulting in a strong reduction of the HRV, including LF and VLF bands. Thus, they concluded that HRV is a resultant

High-frequency power is highly correlated with the pNN50 and RMSSD timedomain measures [22]. HF band power may increase at night and decrease during

LF range is between 0.04 and 0.15 Hz, which equates to rhythms or modulations periods between 7 and 25 seconds. Is typically recorded over a minimum 2min period [23]. This range of action was called the "baroreceptor range" or "mid-frequency band", due to its strong correlation with baroreceptor activity at rest [10, 24]. Baroreceptors are stretch-sensitive mechanoreceptors located in vena cavae, carotid sinuses, aortic arch and heart chambers. The ones found in the carotid are the most sensitive. Baroreflex is transported to the brain by the vagus nerve and represent the beat-to-beat change in HR per unit of change in systolic blood pressure [25]. A decreased baroreflex is related to aging and weakened

There is a different influence of the sympathetic and parasympathetic system inside this band, due to the rhythms: above 0.1 Hz the SNS seems to be lesser influent, whilst the parasympathetic affect heart rhythms down to 0.05 Hz [27, 28]. There rhythms are obtained during slow respiration rates, where a vagal activity easily generates oscillations in the heart rhythms crossing into the LF band [29, 30]. Therefore, when the respiratory rates are below 8.5 per minutes, or 1 in 7 seconds,

Despite has been generally accepted the LF band has a marker for the sympathetic activity, and the LF/HF ratio is used to assess the balance between SNS and PNS, is still not totally clear if in resting condition the Low Frequency band reflect the baroreflex activity instead of the cardiac sympathetic innervation [31–33].

The VLF is the power in the range between 0.0033 and 0.04 Hz, which equates to rhythms or modulations with periods that occur between 25 and 300 seconds. Although all 24 h clinical measures of HRV reflecting low HRV are linked with increased risk of adverse outcomes, the VLF band has stronger associations with allcause mortality than the LF and HF bands [34–37]. Low VLF power has been shown

or when a subject take a deep breath there is a vagal mediation influence.

vagal outflow is restored to a normal level resulting in slowing the HR.

**18**

The ultra-low-frequency band (ULF) falls below 0.0033 Hz (333 seconds or 5.6 minutes). Oscillations or events in the heart rhythm with a period of 5 minutes or greater are reflected in this band and it can only be assessed with 24 h and longer recordings [22]. The circadian oscillation in HR is the primary source of the ULF power, although other very slow-acting regulatory processes, such as core body temperature regulation, metabolism, and the renin-angiotensin system likely add to the power in this band [20]. The Task Force Report on HRV suggests that 24 h recordings should be divided into 5-min segments and that HRV analysis should be performed on the individual segments prior to the calculation of mean values. This effectively filters out any oscillations with periods longer than 5 minutes. However, when spectral analysis is applied to entire 24 h records, several lower frequency rhythms are easily detected in healthy individuals [23].

There is disagreement about the contribution by the PNS and SNS to this band. Different psychiatric disorders show distinct circadian patterns in 24h HRs, particularly during sleep [25, 47].

#### **2.3 Heart rate variability measurement**

Three types of measurement exist for the HRV, time-domain index, frequencydomain index and non-linear measurements. Time-domain indices quantify the amount of HRV observed during monitoring periods that may range from ~2min to 24h. Frequency-domain values calculate the absolute or relative amount of signal energy within component bands. *Non-linear measurements* allow us to quantify the unpredictability of a time series.

#### *2.3.1 Time domain measurements of heart rate variability*

Time domain measures are the simplest to calculate. Time domain measures do not provide a means to adequately quantify autonomic dynamics or determine the rhythmic or oscillatory activity generated by the different physiological control systems. However, since they are always calculated the same way, data collected by different researchers are comparable but only if the recordings are exactly the same length of time and the data are collected under the same conditions. Time domain indices quantify the amount of variance in the inter-beat-intervals (IBI) using statistical measures. The three most important and commonly reported time domain measures are the standard deviation of normal-to-normal (SDNN), the SDNN index, and the root mean square of successive differences (RMSSD) are the most commonly reported metrics.

#### *2.3.2 The standard deviation of the normal-to-normal*

The SDNN is the standard deviation of the normal-to-normal (NN) sinusinitiated IBIs measured in milliseconds. This measure reflects the ebb and flow of all the factors that contribute to HRV. In 24 h recordings, the SDNN is highly correlated with ULF and total power [48]. In short-term resting recordings, the primary source of the variation is parasympathetically mediated, especially with slow, deep breathing protocols. However, in ambulatory and longer term recordings the SDNN values are highly correlated with lower frequency rhythms [23]. Thus, low age-adjusted values predict morbidity and mortality. For example, patients with moderate SDNN values (50–100 milliseconds) have a 400% lower risk of mortality than those with low values (0–50 milliseconds) in 24 h recordings [49, 50].

#### *2.3.3 Standard deviation of the normal-to-normal index*

The SDNN index is the mean of the standard deviations of all the NN intervals for each 5 min segment. Therefore, this measurement only estimates variability due to the factors affecting HRV within a 5 min period. In 24 h HRV recordings, it is calculated by first dividing the 24 h record into 288 five-minute segments and then calculating the standard deviation of all NN intervals contained within each segment. The SDNN index is the average of these 288 values [20]. The SDNN index is believed to primarily measure autonomic influence on HRV. This measure tends to correlate with VLF power over a 24 h period [23].

#### *2.3.4 The root mean square of successive differences*

The RMSSD is the root mean square of successive differences between normal heartbeats. This value is obtained by first calculating each successive time difference between heartbeats in milliseconds. Each of the values is then squared and the result is averaged before the square root of the total is obtained. The RMSSD reflects the beat-to-beat variance in HR and is the primary time domain measure used to estimate the vagally mediated changes reflected in HRV [20]. The RMSSD is correlated with HF power and therefore also reflects self-regulatory capacity as discussed earlier [23].

#### **3. Root mean square of successive differences as PNS marker**

As aforementioned, the RMSSD reflects the vagally mediated changes in the relation that occur between two peaks in the R value of an echocardiogram, thus give to the researcher an overview of the PNS activity.

**21**

*Root Mean Square of the Successive Differences as Marker of the Parasympathetic System…*

The parasympathetic modification evaluation is one of the most used parameters and find its utility in different research [51, 52]. Accordingly, to Zygmunt and Stanczyk [53], the rMSSD *"describes short-term variations, and thus reflects parasympathetic activities"*. Although the HRV and rMSSD does not reflect perfectly the tonic activity of parasympathetic and sympathetic, but rather the resultant on the effector, that are sinus cells node receptors; the vagal activity is predominant compared to sympathetic one. Influence of parasympathetic is fast and transient, due to acetylcholine degradation by acetylesterase. These physiological redundancies cause parasympathetic activities to be visible in the cycle that follows the stimulus, whilst the sympathetic stimulation develop more slowly, and their effects are visible only after 2–3 s, causing slower oscillation with higher amplitude [54]. One concern regarding this issue is that HRV studies are quite sensitive to a number of factors as eloquently pointed out by Piché and Descarreaux [55], which can make

Due to the ambiguity in physiological meaning in low frequency (LF) variations during short recording periods [20] the Time Domain Indices (rMSSD) has revealed itself more reliable than frequency domain [56], and considered as PNS modulation indices [20]. R-R intervals is a time domain measure of HRV calculated by the equation of Kim et al. [57]. According to Hayward et al. [58], the rMSSD time domain measurement has high sensitivity to identify ANS modification in temporal window of 1–2 minutes, concordant to Thong et al. [59] who found how rMSSD is a valuable measurement for ultra-short-term records (1–5 minutes) due to its ability to be improved by combining disjoint records; e.g. combining 6 rMSSD records of 10 seconds each to obtain the equivalent of a 60 seconds length rMMSD registration. Moreover, Esco and Flat [60] showed and almost perfect relationship between ultra-short-term and criterion measures (5 minutes) by recording rMSSD in 23

RMSSD is often used for professional athletes in order to monitor cardiac activity and modulation of the HRV subsequentially to physical performance. Acute decreases in HRV have been reported to occur following intense endurance training [61], resistance training [62], and competition [63]. Therefore, low HRV is commonly thought to provide a reflection of acute fatigue from training or competing. But despite what has been accepted for the last years, recent discovery shows

In the context of monitoring fatigue or training status in athletes, a common belief is that high HRV is good and low HRV is bad. Or, in terms of observing the overall trend, increasing HRV trends are good, indicative of positive adaptation or increases in fitness. Decreasing trends are bad, indicative of fatigue accumulation or

Unfortunately, an increasing HRV trend throughout training is not always a good thing and thus should not always be interpreted as such. In fact, several studies have reported increasing HRV trends in overtrained athletes predominately involved in endurance sports. For example, Le Meur et al. [64] showed decreased maximal incremental exercise performance and increased weekly HRV mean values in elite endurance athletes following a 3 week overload period, compared to a control group who saw no changes. Following a taper, performance supercompensation

He most common response to overload training is a progressive decrease in HRV. This is your typical alarm response to a stressor, where the sympathetic arm of the autonomic nervous system is activated. In this situation, resting HR is elevated

*DOI: http://dx.doi.org/10.5772/intechopen.89827*

data interpretation challenging.

athletes pre- and post-exercises.

**3.1 RMSSD modulation with physical activity**

"overtraining" and performance decrements.

how not always an increase in this value is a positive result.

was observed along with a return of HRV toward baseline.

*Root Mean Square of the Successive Differences as Marker of the Parasympathetic System… DOI: http://dx.doi.org/10.5772/intechopen.89827*

The parasympathetic modification evaluation is one of the most used parameters and find its utility in different research [51, 52]. Accordingly, to Zygmunt and Stanczyk [53], the rMSSD *"describes short-term variations, and thus reflects parasympathetic activities"*. Although the HRV and rMSSD does not reflect perfectly the tonic activity of parasympathetic and sympathetic, but rather the resultant on the effector, that are sinus cells node receptors; the vagal activity is predominant compared to sympathetic one. Influence of parasympathetic is fast and transient, due to acetylcholine degradation by acetylesterase. These physiological redundancies cause parasympathetic activities to be visible in the cycle that follows the stimulus, whilst the sympathetic stimulation develop more slowly, and their effects are visible only after 2–3 s, causing slower oscillation with higher amplitude [54]. One concern regarding this issue is that HRV studies are quite sensitive to a number of factors as eloquently pointed out by Piché and Descarreaux [55], which can make data interpretation challenging.

Due to the ambiguity in physiological meaning in low frequency (LF) variations during short recording periods [20] the Time Domain Indices (rMSSD) has revealed itself more reliable than frequency domain [56], and considered as PNS modulation indices [20]. R-R intervals is a time domain measure of HRV calculated by the equation of Kim et al. [57]. According to Hayward et al. [58], the rMSSD time domain measurement has high sensitivity to identify ANS modification in temporal window of 1–2 minutes, concordant to Thong et al. [59] who found how rMSSD is a valuable measurement for ultra-short-term records (1–5 minutes) due to its ability to be improved by combining disjoint records; e.g. combining 6 rMSSD records of 10 seconds each to obtain the equivalent of a 60 seconds length rMMSD registration. Moreover, Esco and Flat [60] showed and almost perfect relationship between ultra-short-term and criterion measures (5 minutes) by recording rMSSD in 23 athletes pre- and post-exercises.

#### **3.1 RMSSD modulation with physical activity**

RMSSD is often used for professional athletes in order to monitor cardiac activity and modulation of the HRV subsequentially to physical performance. Acute decreases in HRV have been reported to occur following intense endurance training [61], resistance training [62], and competition [63]. Therefore, low HRV is commonly thought to provide a reflection of acute fatigue from training or competing.

But despite what has been accepted for the last years, recent discovery shows how not always an increase in this value is a positive result.

In the context of monitoring fatigue or training status in athletes, a common belief is that high HRV is good and low HRV is bad. Or, in terms of observing the overall trend, increasing HRV trends are good, indicative of positive adaptation or increases in fitness. Decreasing trends are bad, indicative of fatigue accumulation or "overtraining" and performance decrements.

Unfortunately, an increasing HRV trend throughout training is not always a good thing and thus should not always be interpreted as such. In fact, several studies have reported increasing HRV trends in overtrained athletes predominately involved in endurance sports. For example, Le Meur et al. [64] showed decreased maximal incremental exercise performance and increased weekly HRV mean values in elite endurance athletes following a 3 week overload period, compared to a control group who saw no changes. Following a taper, performance supercompensation was observed along with a return of HRV toward baseline.

He most common response to overload training is a progressive decrease in HRV. This is your typical alarm response to a stressor, where the sympathetic arm of the autonomic nervous system is activated. In this situation, resting HR is elevated

*Autonomic Nervous System Monitoring - Heart Rate Variability*

*2.3.1 Time domain measurements of heart rate variability*

*2.3.2 The standard deviation of the normal-to-normal*

*2.3.3 Standard deviation of the normal-to-normal index*

to correlate with VLF power over a 24 h period [23].

*2.3.4 The root mean square of successive differences*

Time domain measures are the simplest to calculate. Time domain measures do not provide a means to adequately quantify autonomic dynamics or determine the rhythmic or oscillatory activity generated by the different physiological control systems. However, since they are always calculated the same way, data collected by different researchers are comparable but only if the recordings are exactly the same length of time and the data are collected under the same conditions. Time domain indices quantify the amount of variance in the inter-beat-intervals (IBI) using statistical measures. The three most important and commonly reported time domain measures are the standard deviation of normal-to-normal (SDNN), the SDNN index, and the root mean square of successive differences (RMSSD) are the most commonly reported metrics.

The SDNN is the standard deviation of the normal-to-normal (NN) sinusinitiated IBIs measured in milliseconds. This measure reflects the ebb and flow of all the factors that contribute to HRV. In 24 h recordings, the SDNN is highly correlated with ULF and total power [48]. In short-term resting recordings, the primary source of the variation is parasympathetically mediated, especially with slow, deep breathing protocols. However, in ambulatory and longer term recordings the SDNN values are highly correlated with lower frequency rhythms [23]. Thus, low age-adjusted values predict morbidity and mortality. For example, patients with moderate SDNN values (50–100 milliseconds) have a 400% lower risk of mortality

than those with low values (0–50 milliseconds) in 24 h recordings [49, 50].

The SDNN index is the mean of the standard deviations of all the NN intervals for each 5 min segment. Therefore, this measurement only estimates variability due to the factors affecting HRV within a 5 min period. In 24 h HRV recordings, it is calculated by first dividing the 24 h record into 288 five-minute segments and then calculating the standard deviation of all NN intervals contained within each segment. The SDNN index is the average of these 288 values [20]. The SDNN index is believed to primarily measure autonomic influence on HRV. This measure tends

The RMSSD is the root mean square of successive differences between normal heartbeats. This value is obtained by first calculating each successive time difference between heartbeats in milliseconds. Each of the values is then squared and the result is averaged before the square root of the total is obtained. The RMSSD reflects the beat-to-beat variance in HR and is the primary time domain measure used to estimate the vagally mediated changes reflected in HRV [20]. The RMSSD is correlated with HF power and therefore also reflects self-regulatory capacity as discussed earlier [23].

As aforementioned, the RMSSD reflects the vagally mediated changes in the relation that occur between two peaks in the R value of an echocardiogram, thus

**3. Root mean square of successive differences as PNS marker**

give to the researcher an overview of the PNS activity.

**20**

and HRV decreases. With insufficient recovery time, HRV may not fully recover to baseline before the next training stimulus and thus will result in a downward trend when this cycle is perpetuated. An intense day of training can result in suppressed HRV for up to 72 hours post-exercise [65]. With the higher training frequencies and training volumes often associated with overload periods, it makes sense that HRV will show a decreasing trend. Typically, HRV will respond first with a decreasing trend and performance decrements will follow if the overload period is sustained.

A study by Pichot et al. [66] provides a good example of a decreasing HRV trend in response to overload training. They showed that middle distance runners saw a progressive downward HRV trend (up to −43%) during a 3 week overload period. In week 4, training loads were reduced and HRV recovered and exceeded baseline values.

These aspects demonstrate a new aspect of the RMSSD modulation due to a physical stimulus, and the complexity of the cardiac regulation mechanism.

#### **3.2 RMSSD application inside the psychological field**

Altered cardiac autonomic functions in form of reduced Heart Rate Variability (HRV) have been found to be associated with increased cardiovascular morbidity and mortality in depressive patients.

Most studies have now identified depression as a strong and independent risk factor for cardiovascular disease even in physically healthy individuals [67] and also for adverse cardiovascular outcomes such as mortality [68]. Although the underlying pathophysiological mechanism is yet to be elucidated, autonomic imbalance has been projected as one of the underlying mechanisms [69]. Heart Rate Variability (HRV) is a useful non-invasive measure for assessing cardiac autonomic modulations.

Reduced HRV has been reported in several studies done in depressed patients both with and without cardiovascular diseases compared to non-depressed subjects [70, 71]. Although negative studies have been reported as well, which were unable to prove an association [72]. Most of the researches carried out to observe the association between HRV and depression have been done in individuals who were already either having Cardiovascular Disease (CVD) besides depression or were on medications [73].

A meta-analysis done by Kemp et al., in depression patients without cardiovascular diseases also reported the association between reduced HRV and depression and was found to be more in severely depressed individuals [74].

In addition, Agelink et al., showed the inverse correlation of parasympathetic HRV values with the severity of depression [75]. In a recent study Wang et al., also observed higher LF, LF:HF Ratio and lower SDNN, RMSSD and HF values in depression group compared to control group [52].

#### **4. Conclusion**

The rMSSD can be a very useful tool for many relevant findings: from the parasympathetic activation to a marker for cardiac dysfunction. Despite the findings and the large are of application, it is still an unknown area.

The correlation between the psychological issues and the rMSSD value add a deeper meaning on how the body is strictly correlated to the mind, and the interrelation between the thought and its physical response. Many research has been done regarding the heart and its physiology and mechanism, but only now we are starting to really understand how this tissue really works, and for better comprehend how

**23**

**Author details**

Giovanni Minarini

Studio Alphaomega, Modena, Italy

provided the original work is properly cited.

\*Address all correspondence to: giovanni.minarini@gmail.com

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*Root Mean Square of the Successive Differences as Marker of the Parasympathetic System…*

this organ is connected to the body and how it respond to the numerous different stimuli throughout the day, is necessary to delineate a clear structure of evaluation capable of considering also these new aspect, like the psychological impact and the

*DOI: http://dx.doi.org/10.5772/intechopen.89827*

psychological response.

*Root Mean Square of the Successive Differences as Marker of the Parasympathetic System… DOI: http://dx.doi.org/10.5772/intechopen.89827*

this organ is connected to the body and how it respond to the numerous different stimuli throughout the day, is necessary to delineate a clear structure of evaluation capable of considering also these new aspect, like the psychological impact and the psychological response.

#### **Author details**

*Autonomic Nervous System Monitoring - Heart Rate Variability*

**3.2 RMSSD application inside the psychological field**

and mortality in depressive patients.

and HRV decreases. With insufficient recovery time, HRV may not fully recover to baseline before the next training stimulus and thus will result in a downward trend when this cycle is perpetuated. An intense day of training can result in suppressed HRV for up to 72 hours post-exercise [65]. With the higher training frequencies and training volumes often associated with overload periods, it makes sense that HRV will show a decreasing trend. Typically, HRV will respond first with a decreasing trend and performance decrements will follow if the overload period is sustained. A study by Pichot et al. [66] provides a good example of a decreasing HRV trend in response to overload training. They showed that middle distance runners saw a progressive downward HRV trend (up to −43%) during a 3 week overload period. In week 4, training loads were reduced and HRV recovered and exceeded baseline

These aspects demonstrate a new aspect of the RMSSD modulation due to a physical stimulus, and the complexity of the cardiac regulation mechanism.

Altered cardiac autonomic functions in form of reduced Heart Rate Variability (HRV) have been found to be associated with increased cardiovascular morbidity

Reduced HRV has been reported in several studies done in depressed patients both with and without cardiovascular diseases compared to non-depressed subjects [70, 71]. Although negative studies have been reported as well, which were unable to prove an association [72]. Most of the researches carried out to observe the association between HRV and depression have been done in individuals who were already either having Cardiovascular Disease (CVD) besides depression or were on

A meta-analysis done by Kemp et al., in depression patients without cardiovascular diseases also reported the association between reduced HRV and depression

In addition, Agelink et al., showed the inverse correlation of parasympathetic HRV values with the severity of depression [75]. In a recent study Wang et al., also observed higher LF, LF:HF Ratio and lower SDNN, RMSSD and HF values in

The rMSSD can be a very useful tool for many relevant findings: from the parasympathetic activation to a marker for cardiac dysfunction. Despite the findings

The correlation between the psychological issues and the rMSSD value add a deeper meaning on how the body is strictly correlated to the mind, and the interrelation between the thought and its physical response. Many research has been done regarding the heart and its physiology and mechanism, but only now we are starting to really understand how this tissue really works, and for better comprehend how

and was found to be more in severely depressed individuals [74].

depression group compared to control group [52].

and the large are of application, it is still an unknown area.

Most studies have now identified depression as a strong and independent risk factor for cardiovascular disease even in physically healthy individuals [67] and also for adverse cardiovascular outcomes such as mortality [68]. Although the underlying pathophysiological mechanism is yet to be elucidated, autonomic imbalance has been projected as one of the underlying mechanisms [69]. Heart Rate Variability (HRV) is a useful non-invasive measure for assessing cardiac autonomic

**22**

values.

modulations.

medications [73].

**4. Conclusion**

Giovanni Minarini Studio Alphaomega, Modena, Italy

\*Address all correspondence to: giovanni.minarini@gmail.com

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **References**

[1] McEwen BS, Wingfield JC. The concepts of allostasis in biology and biomedicine. Hormones and Behavior. 2003;**43**:2-15

[2] Janig W. Homeostasis and Allostasys. In: The Integrative Action of the Autonomic Nervous System. Vol. 2. Cambridge: Cambridge University Press; 2008. 1p

[3] Schulkin J. Allostasis: A neural behavioral perspective. Hormones and Behavior. 2003;**43**:21-27

[4] Bjorntop P. Behavior and metabolic disease. International Journal of Behavioral Medicine. 1997;**2**:285-302

[5] Folkow B, Schmidt T, Uvnas-Moberg K. Stress, health and the social environment. Acta Physiologica Scandinavica. 1997;**161**(640):1-179

[6] McEwen BS. Neurobiology of interpreting and responding to stressful events: Paradigmatic role of the hippocampus. In: Handbook of Physiology. Section 7: The Endocrine System, Vol. IV, Coping with the Environment: Neural and Neuroendocrine Mechanisms. Oxford New York: Oxford University Press; 2001. pp. 155-178

[7] Grassi G, Serravalle G. Sympathovagal imbalance in hypertension. In: Robertson D, Biaggioni I, Burnstock G, Low PA, Paton JFR, editors. Primer on the Autonomic Nervous System. Amsterdam: Elsevier; 2012

[8] Jordan J. Baroreflex failure. In: Robertson D, Biaggioni I, Burnstock G, Low PA, Paton JFR, editors. Primer on the Autonomic Nervous System. Croydon, UK: Academic Press; 2012. pp. 349-353

[9] Marvar PJ, Harrison DG. Inflammation, immunity and the autonomic nervous system. In: Robertson D, Biaggioni I, Burnstock G, Low PA, Paton JFR, editors. Primer on the Autonomic Nervous System. Amsterdam: Elsevier; 2012

[10] McCraty R, Shaffer F. Heart rate variability: New perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk. Global Advances in Health and Medicine. 2015;**4**(1):46-61

[11] Gevirtz RN, Lehrer PM, Schwartz MS. Cardiorespiratory biofeedback. In: Schwartz MS, Andrasik F, editors. Biofeedback: A Practitioner's Guide. 4th ed. New York: The Guilford Press; 2016. pp. 196-213

[12] Stein PK, Domitrovich PP, Hui N, Rautaharju P, Gottdiener J. Sometimes higher heart rate variability is not better heart rate variability: Results of graphical and nonlinear analyses. Journal of Cardiovascular Electrophysiology. 2005;**16**(9):954-959

[13] McCraty R, Atkinson M, Tomasino D, Bradley RT. The Coherent Heart: Heart-Brain Interactions, Psychophysiological Coherence, and the Emergence of System-Wide Order. Boulder Creek, CA: Institute of Heartmath; 2009

[14] Segerstrom SC, Nes LS. Heart rate variability reflects selfregulatory strength, effort, and fatigue. Psychological Science. 2007;**18**(3):275-281

[15] Opthof T. The normal range and determinants of the intrinsic heart rate in man. Cardiovascular Research. 2000;**45**(1):173-176

[16] Palatini P. Elevated heart rate as a predictor of increased cardiovascular morbidity. Journal of Hypertension. Supplement. 1999;**17**(3):S3-S10

**25**

*Root Mean Square of the Successive Differences as Marker of the Parasympathetic System…*

pp. 173-188

2004;**25**(2):523-538

2):H680-H689

In: Malik M, Camm AJ, editors. Heart Rate Variability. Armonk NY: Futura Publishing Company, Inc; 1995.

[25] Bowers EJ, Murray A. Effects on baroreflex sensitivity measurements when different protocols are used to induce regular changes in beat-tobeat intervals and systolic pressure. Physiological Measurement.

[26] Bristow JD, Gribbin B, Honour AJ, Pickering TG, Sleight P. Diminished baroreflex sensitivity in high blood pressure and ageing man. The Journal of

Physiology. 1969;**202**(1):45-46

[27] de Boer RW, Karemaker JM, Strackee J. Hemodynamic fluctuations and baroreflex sensitivity in humans: A beat-to-beat model. The American Journal of Physiology. 1987;**253**(3 Pt

[28] Baselli G, Cerutti S, Badilini F, Biancardi L, Porta A, Pagani M, et al.

[29] Ahmed AK, Harness JB, Mearns AJ. Respiratory control of heart rate. European Journal of Applied Physiology. 1982;**50**:95-104

[30] Tiller WA, McCraty R, Atkinson M. Cardiac coherence: A new, noninvasive measure of autonomic nervous system order. Alternative Therapies in Health

Environmental and Exercise Physiology.

and Medicine. 1996;**2**(1):52-65

[31] Eckberg DL. Human sinus arrhythmia as an index of vagal cardiac outflow. Journal of Applied Physiology: Respiratory,

1983;**54**(4):961-966

Model for the assessment of heart period and arterial pressure variability interactions and of respiration influences. Medical & Biological Engineering & Computing.

1994;**32**(2):143-152

*DOI: http://dx.doi.org/10.5772/intechopen.89827*

[17] Hainsworth R. The control and physiological importance on heart rate. In: Malik M, Camm AJ, editors. Heart Rate Variability. Armonk, NY: Futura Publishing Company, Inc; 1995

[18] Lacey BC, Lacey JI. Two-way communication between the heart and the brain. Significance of time within the cardiac cycle. The American

Psychologist. 1978;**33**(2):99-113

Press; 1991. pp. 1-37

[21] Quintana DS, Elstad M,

srep37212

Kaufmann T, Brandt CL, Haatveit B, Haram M, et al. Resting-state highfrequency heart rate variability is related to respiratory frequency in individuals with severe mental illness but not healthy controls. Scientific Reports. 2016;**6**:37212. DOI: 10.1038/

[22] Kleiger RE, Stein PK, Bigger JT. Heart rate variability: Measurement and clinical utility. Annals of Noninvasive Electrocardiology. 2005;**10**:88-101. DOI: 10.1111/j.1542-474X.2005.10101.x

[23] Shaffer F, McCraty R, Zerr CL. A healthy heart is not a metronome: An integrative review of the heart's anatomy and heart rate variability. Frontiers in Psychology. 2014;**5**:1040. DOI: 10.3389/fpsyg.2014.01040ù

[24] Malliani A. Association of heart rate variability components with physiological regulatory mechanisms.

[19] Armour JA. Anatomy and function of the intrathoracic neurons regulating the mammalian heart. In: Zucker IH, Gilmore JP, editors. Reflex Control of the Circulation. Boca Raton, FL: CRC

[20] Task Force of the European Society of Cardiology, North American Society of Pacing and Electrophysiology. Heart rate variability standards of measurement, physiological interpretation, and clinical use. Circulation. 1996;**93**(5):1043-1065

*Root Mean Square of the Successive Differences as Marker of the Parasympathetic System… DOI: http://dx.doi.org/10.5772/intechopen.89827*

[17] Hainsworth R. The control and physiological importance on heart rate. In: Malik M, Camm AJ, editors. Heart Rate Variability. Armonk, NY: Futura Publishing Company, Inc; 1995

[18] Lacey BC, Lacey JI. Two-way communication between the heart and the brain. Significance of time within the cardiac cycle. The American Psychologist. 1978;**33**(2):99-113

[19] Armour JA. Anatomy and function of the intrathoracic neurons regulating the mammalian heart. In: Zucker IH, Gilmore JP, editors. Reflex Control of the Circulation. Boca Raton, FL: CRC Press; 1991. pp. 1-37

[20] Task Force of the European Society of Cardiology, North American Society of Pacing and Electrophysiology. Heart rate variability standards of measurement, physiological interpretation, and clinical use. Circulation. 1996;**93**(5):1043-1065

[21] Quintana DS, Elstad M, Kaufmann T, Brandt CL, Haatveit B, Haram M, et al. Resting-state highfrequency heart rate variability is related to respiratory frequency in individuals with severe mental illness but not healthy controls. Scientific Reports. 2016;**6**:37212. DOI: 10.1038/ srep37212

[22] Kleiger RE, Stein PK, Bigger JT. Heart rate variability: Measurement and clinical utility. Annals of Noninvasive Electrocardiology. 2005;**10**:88-101. DOI: 10.1111/j.1542-474X.2005.10101.x

[23] Shaffer F, McCraty R, Zerr CL. A healthy heart is not a metronome: An integrative review of the heart's anatomy and heart rate variability. Frontiers in Psychology. 2014;**5**:1040. DOI: 10.3389/fpsyg.2014.01040ù

[24] Malliani A. Association of heart rate variability components with physiological regulatory mechanisms. In: Malik M, Camm AJ, editors. Heart Rate Variability. Armonk NY: Futura Publishing Company, Inc; 1995. pp. 173-188

[25] Bowers EJ, Murray A. Effects on baroreflex sensitivity measurements when different protocols are used to induce regular changes in beat-tobeat intervals and systolic pressure. Physiological Measurement. 2004;**25**(2):523-538

[26] Bristow JD, Gribbin B, Honour AJ, Pickering TG, Sleight P. Diminished baroreflex sensitivity in high blood pressure and ageing man. The Journal of Physiology. 1969;**202**(1):45-46

[27] de Boer RW, Karemaker JM, Strackee J. Hemodynamic fluctuations and baroreflex sensitivity in humans: A beat-to-beat model. The American Journal of Physiology. 1987;**253**(3 Pt 2):H680-H689

[28] Baselli G, Cerutti S, Badilini F, Biancardi L, Porta A, Pagani M, et al. Model for the assessment of heart period and arterial pressure variability interactions and of respiration influences. Medical & Biological Engineering & Computing. 1994;**32**(2):143-152

[29] Ahmed AK, Harness JB, Mearns AJ. Respiratory control of heart rate. European Journal of Applied Physiology. 1982;**50**:95-104

[30] Tiller WA, McCraty R, Atkinson M. Cardiac coherence: A new, noninvasive measure of autonomic nervous system order. Alternative Therapies in Health and Medicine. 1996;**2**(1):52-65

[31] Eckberg DL. Human sinus arrhythmia as an index of vagal cardiac outflow. Journal of Applied Physiology: Respiratory, Environmental and Exercise Physiology. 1983;**54**(4):961-966

**24**

pp. 349-353

*Autonomic Nervous System Monitoring - Heart Rate Variability*

autonomic nervous system. In:

Amsterdam: Elsevier; 2012

Medicine. 2015;**4**(1):46-61

[11] Gevirtz RN, Lehrer PM, Schwartz MS. Cardiorespiratory biofeedback. In: Schwartz MS, Andrasik F, editors. Biofeedback: A Practitioner's Guide. 4th ed. New York: The Guilford Press; 2016. pp. 196-213

Robertson D, Biaggioni I, Burnstock G, Low PA, Paton JFR, editors. Primer on the Autonomic Nervous System.

[10] McCraty R, Shaffer F. Heart rate variability: New perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk. Global Advances in Health and

[12] Stein PK, Domitrovich PP, Hui N, Rautaharju P, Gottdiener J. Sometimes

Tomasino D, Bradley RT. The Coherent Heart: Heart-Brain Interactions, Psychophysiological Coherence, and the Emergence of System-Wide Order. Boulder Creek, CA: Institute of

[14] Segerstrom SC, Nes LS. Heart rate variability reflects selfregulatory strength, effort, and fatigue. Psychological Science.

[15] Opthof T. The normal range and determinants of the intrinsic heart rate in man. Cardiovascular Research.

[16] Palatini P. Elevated heart rate as a predictor of increased cardiovascular morbidity. Journal of Hypertension. Supplement. 1999;**17**(3):S3-S10

higher heart rate variability is not better heart rate variability: Results of graphical and nonlinear analyses. Journal of Cardiovascular Electrophysiology. 2005;**16**(9):954-959

[13] McCraty R, Atkinson M,

Heartmath; 2009

2007;**18**(3):275-281

2000;**45**(1):173-176

[1] McEwen BS, Wingfield JC. The concepts of allostasis in biology and biomedicine. Hormones and Behavior.

In: The Integrative Action of the Autonomic Nervous System. Vol. 2. Cambridge: Cambridge University

[3] Schulkin J. Allostasis: A neural behavioral perspective. Hormones and

[5] Folkow B, Schmidt T, Uvnas-Moberg K. Stress, health and the social environment. Acta Physiologica Scandinavica. 1997;**161**(640):1-179

[6] McEwen BS. Neurobiology of interpreting and responding to stressful events: Paradigmatic role of the hippocampus. In: Handbook of Physiology. Section 7: The Endocrine System, Vol. IV, Coping with the Environment: Neural and Neuroendocrine Mechanisms. Oxford New York: Oxford University Press;

[7] Grassi G, Serravalle G. Sympathovagal imbalance in hypertension. In: Robertson D, Biaggioni I, Burnstock G, Low PA, Paton JFR, editors. Primer on the Autonomic Nervous System.

Amsterdam: Elsevier; 2012

[9] Marvar PJ, Harrison DG. Inflammation, immunity and the

[8] Jordan J. Baroreflex failure. In: Robertson D, Biaggioni I, Burnstock G, Low PA, Paton JFR, editors. Primer on the Autonomic Nervous System. Croydon, UK: Academic Press; 2012.

2001. pp. 155-178

[4] Bjorntop P. Behavior and metabolic disease. International Journal of Behavioral Medicine. 1997;**2**:285-302

Behavior. 2003;**43**:21-27

[2] Janig W. Homeostasis and Allostasys.

2003;**43**:2-15

**References**

Press; 2008. 1p

[32] Kember GC, Fenton GA, Armour JA, Kalyaniwalla N. Competition model for aperiodic stochastic resonance in a Fitzhugh-Nagumo model of cardiac sensory neurons. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 2001;**63**(4 Pt 1):041911

[33] MacKinnon S, Gevirtz R, McCraty R, Brown M. Utilizing heartbeat evoked potentials to identify cardiac regulation of vagal afferents during emotion and resonant breathing. Applied Psychophysiology and Biofeedback. 2013;**38**(4):241-255

[34] Tsuji H, Venditti FJ, Manders ES, Evans JC, Larson MG, Feldman CL, et al. Reduced heart rate variability and mortality risk in an elderly cohort. The Framingham heart study. Circulation. 1994;**90**(2):878-883

[35] Schmidt H, Müller-Werdan U, Hoffmann T, Francis DP, Piepoli MF, Rauchhaus M, et al. Autonomic dysfunction predicts mortality in patients with multiple organ dysfunction syndrome of different age groups. Critical Care Medicine. 2005;**33**(9):1994-2002

[36] Hadase M, Azuma A, Zen K, et al. Very low frequency power of heart rate variability is a powerful predictor of clinical prognosis in patients with congestive heart failure. Circulation Journal. 2004;**68**(4):343-347

[37] Tsuji H, Larson MG, Venditti FJ Jr, Manders ES, Evans JC, Feldman CL, et al. Impact of reduced heart rate variability on risk for cardiac events. The Framingham heart study. Circulation. 1996;**94**(11):2850-2855

[38] Bigger JT Jr, Fleiss JL, Steinman RC, Rolnitzky LM, Kleiger RE, Rottman JN. Frequency domain measures of heart period variability and mortality after myocardial infarction. Circulation. 1992;**85**(1):164-171

[39] Shah AJ, Lampert R, Goldberg J, Veledar E, Bremner JD, Vaccarino V. Posttraumatic stress disorder and impaired autonomic modulation in male twins. Biological Psychiatry. 2013;**73**(11):1103-1110

[40] Lampert R, Bremner JD, Su S, et al. Decreased heart rate variability is associated with higher levels of inflammation in middle-aged men. American Heart Journal. 2008;**156**(4):759.e1-759.e7

[41] Carney RM, Freedland KE, Stein PK, et al. Heart rate variability and markers of inflammation and coagulation in depressed patients with coronary heart disease. Journal of Psychosomatic Research. 2007;**62**(4):463-467

[42] Theorell T, Liljeholm-Johansson Y, Björk H, Ericson M. Saliva testosterone and heart rate variability in the professional symphony orchestra after "public faintings" of an orchestra member. Psychoneuroendocrinology. 2007;**32**(6):660-668

[43] Akselrod S, Gordon D, Ubel FA, Shannon DC, Berger AC, Cohen RJ. Power spectrum analysis of heart rate fluctuation: A quantitative probe of beat-to-beat cardiovascular control. Science. 1981;**213**(4504):220-222

[44] Cerutti S, Bianchi AM, Mainardi LT. Spectral analysis of the heart rate variability signal. In: Malik M, Camm AJ, editors. Heart Rate Variability. Armonk NY: Futura Publishing Company, Inc; 1995. pp. 63-74

[45] Armour JA. Neurocardiology: Anatomical and Functional Principles. Boulder Creek, CA: Institute of HeartMath; 2003

[46] Kember GC, Fenton GA, Collier K, Armour JA. Aperiodic stochastic resonance in a hysteretic

**27**

*Root Mean Square of the Successive Differences as Marker of the Parasympathetic System…*

[54] Pinna GD, Maestri R, Torunski A. Heart rate variability measures: A fresh look at reliability. Clinical Science.

[55] Piché M, Descarreaux M. Heart rate variability modulation after manipulation in pain-free patients vs patients in pain? The importance of controlling for respiration rate changes. Journal of Manipulative and Physiological Therapeutics.

[56] Al Haddad H, Laursen PB, Chollet D, Ahmaidi S, Buchheit M. Reliability of resting and postexercise heart rate measures. International Journal of Sports Medicine.

[57] Kim KK, Lim YG, Kim JS, Park KS. Effect of missing RR-interval data on heart rate variability analysis in the time domain. Physiological Measurement.

[58] Hayward C, Patel H, Manohar S, Waton J, Middleton L, Wardle A, et al. Heart rate variability, assessed in one minute windows, provides insight into the time course of changes in autonomic nervous system activity. Journal of the American College of Cardiology.

[59] Thong T, Li K, McNames J, Aboy M, Goldstein B. Accuracy of ultra-short heart rate variability measures. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol. 3. 2003. pp. 2424-2427

[60] Esco MR, Flatt AA. Ultra-shortterm heart rate variability indexes at rest and post-exercise in athletes: Evaluating the agreement with accepted recommendations. Journal of Sports Science and Medicine. 2014;**13**:535-541

[61] Plews DJ, Laursen PB, Kilding AE, Buchheit M. Heart rate variability and

2007;**113**:131-140

2010;**33**:554-555

2011;**32**:598-605

2015;**65**(10)

2007;**28**(12):1458-1494

*DOI: http://dx.doi.org/10.5772/intechopen.89827*

population of cardiac neurons. Physical Review E. 2000;**61**:1816-1824. DOI:

[47] Armour JA. Peripheral autonomic neuronal interactions in cardiac regulation: Neurocardiology. In: Armour JA, Ardell JL, editors. New York: Oxford University Press;

[48] Umetani K, Singer DH, McCraty R, Atkinson M. Twenty-four hour time domain heart rate variability and heart rate: Relations to age and gender over nine decades. Journal of the American College of Cardiology.

10.1103/PhysRevE.61.1816

1994. pp. 219-244

1998;**31**(3):593-601

2005;**16**(1):13-20

2015;**15**:314-318

2013;**13**:187

2010;**6**(1):11-18

[49] Stein PK, Domitrovich PP, Huikuri HV, Kleiger RE. Cast investigators. Traditional and

nonlinear heart rate variability are each independently associated with mortality after myocardial infarction. Journal of Cardiovascular Electrophysiology.

[50] Kleiger RE, Miller JP, Bigger JT Jr, Moss AJ. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. The American Journal of Cardiology. 1987;**59**(4):256-262

[51] Koenig J, Jarczok MN, Ellis RJ, Warth M, Hillecke TK. Lowered parasympathetic activity in apparently healthy subjects with self-reported symptoms of pain: Preliminary results from a pilot study. Pain Practice.

[52] Wang Y, Zhao X, O'Neil A,

autonomic nervous function in depression. BMC Psychiatry.

Turner A, LiuEmail X. Altered cardiac

[53] Zygmunt A, Stanczyk J. Methods of evaluation of autonomic nervous system function. Archives of Medical Science.

*Root Mean Square of the Successive Differences as Marker of the Parasympathetic System… DOI: http://dx.doi.org/10.5772/intechopen.89827*

population of cardiac neurons. Physical Review E. 2000;**61**:1816-1824. DOI: 10.1103/PhysRevE.61.1816

*Autonomic Nervous System Monitoring - Heart Rate Variability*

[39] Shah AJ, Lampert R, Goldberg J, Veledar E, Bremner JD, Vaccarino V. Posttraumatic stress disorder and impaired autonomic modulation in male twins. Biological Psychiatry.

[40] Lampert R, Bremner JD, Su S, et al. Decreased heart rate variability is associated with higher levels of inflammation in middle-aged men. American Heart Journal. 2008;**156**(4):759.e1-759.e7

[41] Carney RM, Freedland KE, Stein PK, et al. Heart rate variability and markers of inflammation and coagulation in depressed patients with coronary heart disease. Journal of Psychosomatic Research.

[42] Theorell T, Liljeholm-Johansson Y, Björk H, Ericson M. Saliva testosterone

[43] Akselrod S, Gordon D, Ubel FA, Shannon DC, Berger AC, Cohen RJ. Power spectrum analysis of heart rate fluctuation: A quantitative probe of beat-to-beat cardiovascular control. Science. 1981;**213**(4504):220-222

[44] Cerutti S, Bianchi AM, Mainardi LT. Spectral analysis of the heart rate variability signal. In: Malik M, Camm AJ, editors. Heart Rate Variability. Armonk NY: Futura Publishing Company, Inc; 1995.

[45] Armour JA. Neurocardiology: Anatomical and Functional Principles.

Boulder Creek, CA: Institute of

[46] Kember GC, Fenton GA, Collier K, Armour JA. Aperiodic stochastic resonance in a hysteretic

pp. 63-74

HeartMath; 2003

and heart rate variability in the professional symphony orchestra after "public faintings" of an orchestra member. Psychoneuroendocrinology.

2007;**62**(4):463-467

2007;**32**(6):660-668

2013;**73**(11):1103-1110

[32] Kember GC, Fenton GA, Armour JA, Kalyaniwalla N. Competition model for aperiodic stochastic resonance in a Fitzhugh-Nagumo model of cardiac sensory neurons. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 2001;**63**(4 Pt 1):041911

[33] MacKinnon S, Gevirtz R, McCraty R, Brown M. Utilizing heartbeat evoked potentials to identify cardiac regulation of vagal afferents during emotion and resonant breathing. Applied Psychophysiology and Biofeedback.

[34] Tsuji H, Venditti FJ, Manders ES, Evans JC, Larson MG, Feldman CL, et al. Reduced heart rate variability and mortality risk in an elderly cohort. The Framingham heart study. Circulation.

[35] Schmidt H, Müller-Werdan U,

[36] Hadase M, Azuma A, Zen K, et al. Very low frequency power of heart rate variability is a powerful predictor of clinical prognosis in patients with congestive heart failure. Circulation

[37] Tsuji H, Larson MG, Venditti FJ Jr, Manders ES, Evans JC, Feldman CL, et al. Impact of reduced heart rate variability on risk for cardiac events. The Framingham heart study. Circulation. 1996;**94**(11):2850-2855

[38] Bigger JT Jr, Fleiss JL, Steinman RC, Rolnitzky LM, Kleiger RE, Rottman JN. Frequency domain measures of heart period variability and mortality after myocardial infarction. Circulation.

Journal. 2004;**68**(4):343-347

2013;**38**(4):241-255

1994;**90**(2):878-883

Hoffmann T, Francis DP, Piepoli MF, Rauchhaus M, et al. Autonomic dysfunction predicts mortality in patients with multiple organ dysfunction syndrome of different age groups. Critical Care Medicine. 2005;**33**(9):1994-2002

**26**

1992;**85**(1):164-171

[47] Armour JA. Peripheral autonomic neuronal interactions in cardiac regulation: Neurocardiology. In: Armour JA, Ardell JL, editors. New York: Oxford University Press; 1994. pp. 219-244

[48] Umetani K, Singer DH, McCraty R, Atkinson M. Twenty-four hour time domain heart rate variability and heart rate: Relations to age and gender over nine decades. Journal of the American College of Cardiology. 1998;**31**(3):593-601

[49] Stein PK, Domitrovich PP, Huikuri HV, Kleiger RE. Cast investigators. Traditional and nonlinear heart rate variability are each independently associated with mortality after myocardial infarction. Journal of Cardiovascular Electrophysiology. 2005;**16**(1):13-20

[50] Kleiger RE, Miller JP, Bigger JT Jr, Moss AJ. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. The American Journal of Cardiology. 1987;**59**(4):256-262

[51] Koenig J, Jarczok MN, Ellis RJ, Warth M, Hillecke TK. Lowered parasympathetic activity in apparently healthy subjects with self-reported symptoms of pain: Preliminary results from a pilot study. Pain Practice. 2015;**15**:314-318

[52] Wang Y, Zhao X, O'Neil A, Turner A, LiuEmail X. Altered cardiac autonomic nervous function in depression. BMC Psychiatry. 2013;**13**:187

[53] Zygmunt A, Stanczyk J. Methods of evaluation of autonomic nervous system function. Archives of Medical Science. 2010;**6**(1):11-18

[54] Pinna GD, Maestri R, Torunski A. Heart rate variability measures: A fresh look at reliability. Clinical Science. 2007;**113**:131-140

[55] Piché M, Descarreaux M. Heart rate variability modulation after manipulation in pain-free patients vs patients in pain? The importance of controlling for respiration rate changes. Journal of Manipulative and Physiological Therapeutics. 2010;**33**:554-555

[56] Al Haddad H, Laursen PB, Chollet D, Ahmaidi S, Buchheit M. Reliability of resting and postexercise heart rate measures. International Journal of Sports Medicine. 2011;**32**:598-605

[57] Kim KK, Lim YG, Kim JS, Park KS. Effect of missing RR-interval data on heart rate variability analysis in the time domain. Physiological Measurement. 2007;**28**(12):1458-1494

[58] Hayward C, Patel H, Manohar S, Waton J, Middleton L, Wardle A, et al. Heart rate variability, assessed in one minute windows, provides insight into the time course of changes in autonomic nervous system activity. Journal of the American College of Cardiology. 2015;**65**(10)

[59] Thong T, Li K, McNames J, Aboy M, Goldstein B. Accuracy of ultra-short heart rate variability measures. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol. 3. 2003. pp. 2424-2427

[60] Esco MR, Flatt AA. Ultra-shortterm heart rate variability indexes at rest and post-exercise in athletes: Evaluating the agreement with accepted recommendations. Journal of Sports Science and Medicine. 2014;**13**:535-541

[61] Plews DJ, Laursen PB, Kilding AE, Buchheit M. Heart rate variability and

training intensity distribution in elite rowers. International Journal of Sports Physiology and Performance. 2014

[62] Chen JL, Yeh DP, Lee JP, Chen CY, Huang CY, Lee SD, et al. Parasympathetic nervous activity mirrors recovery status in weightlifting performance after training. The Journal of Strength & Conditioning Research. 2011;**25**(6):1546-1552

[63] Edmonds RC, Sinclair WH, Leicht AS. Effect of a training week on heart rate variability in elite youth rugby league players. International Journal of Sports Medicine. Dec 2013;**34**(12):1087-1092

[64] Le Meur Y, Pichon A, Schaal K, Schmitt L, Louis J, Gueneron J, et al. Evidence of parasympathetic hyperactivity in functionally overreached athletes. Medicine and Science in Sports and Exercise. 2013;**45**(11):2061-2071

[65] Stanley J, Peake JM, Buchheit M. Cardiac parasympathetic reactivation following exercise: Implications for training prescription. Sports Medicine. 2013;**43**(12):1259-1277

[66] Pichot V, Roche F, Gaspoz JM, Enjolras F, Antoniadis A, Minini P, et al. Relation between heart rate variability and training load in middle-distance runners. Medicine and Science in Sports and Exercise. 2000;**32**(10):1729-1736

[67] Gan Y, Gong Y, Tong X, Sun H, Cong Y, Dong X, et al. Depression and the risk of coronary heart disease: A meta-analysis of prospective cohort studies. BMC Psychiatry. 2014;**14**:371

[68] Freedland KE, Carney RM. Depression as a risk factor for adverse outcomes in coronary heart disease. BMC Medicine. 2013;**11**:131

[69] Huffman JC, Celano CM, Beach SR, Motiwala SR, Januzzi JL. Depression

and cardiac disease: Epidemiology, mechanisms and diagnosis. Cardiovascular Psychiatry and Neurology. 2013;**2013**:1-14

**Chapter 3**

**Abstract**

Heart Rate Variability

further can be used for the ANS assessment.

**1. Introduction**

**29**

**Keywords:** HRV, PPG, stress, autonomic function, ECG

Recording System Using

*Noor Aimie-Salleh, Nurul Aliaa Abdul Ghani,*

Photoplethysmography Sensor

*Nurhafiezah Hasanudin and Siti Nur Shakiroh Shafie*

Heart rate variability (HRV) is a physiological measurement that can help to monitor and diagnose chronic diseases such as cardiovascular disease, depression, and psychological stress. HRV measurement is commonly extracted from the electrocardiography (ECG). However, ECG has bulky wires where it needs at least three surface electrodes to be placed on the skin. This may cause distraction during the recording and need longer time to setup. Therefore, photoplethysmography (PPG), a simple optical technique, was suggested to obtain heart rate. This study proposes to investigate the effectiveness of PPG recording and derivation of HRV for feature analysis. The PPG signal was preprocessed to remove all the noise and to extract the HRV. HRV features were collected using time-domain analysis (TA), frequency-domain analysis (FA) and nonlinear time-frequency analysis (TFA). Five out of 22 HRV features, which are HR, RMSSD, LF/HF, LFnu, and HFnu, showed high correlation (rho > 0.6 and prho < 0.05) in comparison to standard 5-min excerpt while producing significant difference (p-value < 0.05) during the stressing condition across all interval HRV excerpts. This simple yet accurate PPG recording system perhaps might useful to assess the HRV signal in a short time, and

Human body is interacting between each other where it consists of many different interacting systems. Any changes in human body will generate response to all parts of the body include the autonomic nervous system (ANS) [1]. ANS controls the system that regulates bodily functions such as the digestion, respiratory rate, heart rate, pupillary response, urination, and sexual arousal. Any changes in ANS can be detected by heart rate variability (HRV) since HRV and ANS is directly related.

Heart rate can be defined as the number of heart beats per minute while heart rate variability (HRV) is the fluctuation in the time intervals between adjacent heartbeats. HRV refers to the time series of the interval variation between consecutive heart beats and it can be analyzed in time, frequency and nonlinear domains [2]. The fluctuations in HRV value reflects neurocardiac function of the body as it is generated through heart-brain connection and autonomic nervous system (ANS) dynamics [3, 4].

[70] Glassman AH, Bigger JT, Gaffney M, Vanzyl LT. Heart rate variability in acute coronary syndrome patients with major depression: Influence of sertraline and mood improvement. Archives of General Psychiatry. 2007;**64**:1025-1031

[71] Kemp AH, Quintana DS, Felmingham KL, Matthews S, Jelinek HF. Depression, comorbid anxiety disorders and heart rate variability in physically healthy, unmedicatedpatients: Implications for cardiovascular risk. PLoS One. 2012;**7**:e30777

[72] Gehi A, Mangano D, Pipkin S, Browner WS, Whooley MA. Depression and heart rate variability in patients with stable coronary heart disease: Findings from the heart and soul study. Archives of General Psychiatry. 2005;**62**:661-666

[73] Carney RM, Freedland KE. Depression and heart rate variability in patients with coronary heart disease. Cleveland Clinic Journal of Medicine. 2009;**76**:13-17

[74] Kemp AH, Quintana DS, Gray MA, Felmingham KL, Brown K, Gatt JM. Impact of depression and antidepressant treatment on heart rate variability: A review and meta-analysis. Biological Psychiatry. 2010;**67**:1067-1074

[75] Agelink MW, Majewski T, Wurthmann C, Postert T, Linka T, Rotterdam S, et al. Autonomic neurocardiac function in patients with major depression and effects of antidepressive treatment with nefazodone. Journal of Affective Disorders. 2001;**62**:187-198

#### **Chapter 3**

*Autonomic Nervous System Monitoring - Heart Rate Variability*

and cardiac disease: Epidemiology,

mechanisms and diagnosis. Cardiovascular Psychiatry and Neurology. 2013;**2013**:1-14

[70] Glassman AH, Bigger JT, Gaffney M, Vanzyl LT. Heart rate variability in acute coronary syndrome

patients with major depression: Influence of sertraline and mood improvement. Archives of General Psychiatry. 2007;**64**:1025-1031

[71] Kemp AH, Quintana DS, Felmingham KL, Matthews S, Jelinek HF. Depression, comorbid anxiety disorders and heart rate variability in physically healthy, unmedicatedpatients: Implications for cardiovascular risk. PLoS One.

[72] Gehi A, Mangano D, Pipkin S, Browner WS, Whooley MA. Depression and heart rate variability in patients with stable coronary heart disease: Findings from the heart and soul study. Archives of General Psychiatry.

[73] Carney RM, Freedland KE.

[75] Agelink MW, Majewski T, Wurthmann C, Postert T, Linka T, Rotterdam S, et al. Autonomic neurocardiac function in patients with major depression and effects of antidepressive treatment with nefazodone. Journal of Affective Disorders. 2001;**62**:187-198

Depression and heart rate variability in patients with coronary heart disease. Cleveland Clinic Journal of Medicine.

[74] Kemp AH, Quintana DS, Gray MA, Felmingham KL, Brown K, Gatt JM. Impact of depression and antidepressant treatment on heart rate variability: A review and meta-analysis. Biological Psychiatry. 2010;**67**:1067-1074

2012;**7**:e30777

2005;**62**:661-666

2009;**76**:13-17

training intensity distribution in elite rowers. International Journal of Sports Physiology and Performance. 2014

[62] Chen JL, Yeh DP, Lee JP, Chen CY, Huang CY, Lee SD, et al. Parasympathetic nervous activity mirrors recovery status in weightlifting performance after training. The Journal of Strength & Conditioning Research.

[63] Edmonds RC, Sinclair WH, Leicht AS. Effect of a training week on heart rate variability in elite youth rugby league players. International Journal of Sports Medicine. Dec

[64] Le Meur Y, Pichon A, Schaal K, Schmitt L, Louis J, Gueneron J, et al. Evidence of parasympathetic hyperactivity in functionally overreached athletes. Medicine and Science in Sports and Exercise.

[65] Stanley J, Peake JM, Buchheit M. Cardiac parasympathetic reactivation following exercise: Implications for training prescription. Sports Medicine.

[66] Pichot V, Roche F, Gaspoz JM, Enjolras F, Antoniadis A, Minini P, et al. Relation between heart rate variability and training load in middle-distance runners. Medicine and Science in Sports and Exercise. 2000;**32**(10):1729-1736

[67] Gan Y, Gong Y, Tong X, Sun H, Cong Y, Dong X, et al. Depression and the risk of coronary heart disease: A meta-analysis of prospective cohort studies. BMC Psychiatry. 2014;**14**:371

[68] Freedland KE, Carney RM. Depression as a risk factor for adverse outcomes in coronary heart disease.

[69] Huffman JC, Celano CM, Beach SR, Motiwala SR, Januzzi JL. Depression

BMC Medicine. 2013;**11**:131

2011;**25**(6):1546-1552

2013;**34**(12):1087-1092

2013;**45**(11):2061-2071

2013;**43**(12):1259-1277

**28**

## Heart Rate Variability Recording System Using Photoplethysmography Sensor

*Noor Aimie-Salleh, Nurul Aliaa Abdul Ghani, Nurhafiezah Hasanudin and Siti Nur Shakiroh Shafie*

#### **Abstract**

Heart rate variability (HRV) is a physiological measurement that can help to monitor and diagnose chronic diseases such as cardiovascular disease, depression, and psychological stress. HRV measurement is commonly extracted from the electrocardiography (ECG). However, ECG has bulky wires where it needs at least three surface electrodes to be placed on the skin. This may cause distraction during the recording and need longer time to setup. Therefore, photoplethysmography (PPG), a simple optical technique, was suggested to obtain heart rate. This study proposes to investigate the effectiveness of PPG recording and derivation of HRV for feature analysis. The PPG signal was preprocessed to remove all the noise and to extract the HRV. HRV features were collected using time-domain analysis (TA), frequency-domain analysis (FA) and nonlinear time-frequency analysis (TFA). Five out of 22 HRV features, which are HR, RMSSD, LF/HF, LFnu, and HFnu, showed high correlation (rho > 0.6 and prho < 0.05) in comparison to standard 5-min excerpt while producing significant difference (p-value < 0.05) during the stressing condition across all interval HRV excerpts. This simple yet accurate PPG recording system perhaps might useful to assess the HRV signal in a short time, and further can be used for the ANS assessment.

**Keywords:** HRV, PPG, stress, autonomic function, ECG

#### **1. Introduction**

Human body is interacting between each other where it consists of many different interacting systems. Any changes in human body will generate response to all parts of the body include the autonomic nervous system (ANS) [1]. ANS controls the system that regulates bodily functions such as the digestion, respiratory rate, heart rate, pupillary response, urination, and sexual arousal. Any changes in ANS can be detected by heart rate variability (HRV) since HRV and ANS is directly related.

Heart rate can be defined as the number of heart beats per minute while heart rate variability (HRV) is the fluctuation in the time intervals between adjacent heartbeats. HRV refers to the time series of the interval variation between consecutive heart beats and it can be analyzed in time, frequency and nonlinear domains [2]. The fluctuations in HRV value reflects neurocardiac function of the body as it is generated through heart-brain connection and autonomic nervous system (ANS) dynamics [3, 4].

HRV is a common measurement that can be extracted from the physiological measurement and helps to monitor the psychological stress [5]. It is because, HRV has direct connection with the autonomic nervous system (ANS) where any changes that occurred in human body can be directly detected by the HRV. The common methods to get the HRV are by using the ECG. However, there are several difficulties to record the ECG signal. First, it requires at least three surface electrodes to be placed on the skin to get single lead channel [6]. This clearly shows bulky of wires are needed for the recording and might cause distraction and uncomfortable feeling to the patient. Furthermore, it requires several times to set up the ECG before start the recording.

As previously discussed, both ECG and PPG system are able to provide information on cardiovascular activities. While ECG system allow better depiction of real cardiac movement through the measurement of the electrical signals produced by the action potential of the tissue, PPG allow adequate cardiovascular measurements such as heart rate and cardiac output only through pulsatile flow of blood in the arteries. Several studies have shown that the cardiovascular parameters collected through PPG systems are highly correlative and comparable to the measurements taken through standard ECG system [8, 10, 11]. This proves that despite not being able to illustrate exact cardiac waveforms or ectopic beats, PPG could serve as

*Heart Rate Variability Recording System Using Photoplethysmography Sensor*

*DOI: http://dx.doi.org/10.5772/intechopen.89901*

In terms of measurement accuracy, there are several factors to be considered to ensure the reliability of data collection. Topographical factor such as position of sensor placement on the body plays an important factor since different area of the body constitutes different accuracy of perfusion readings. The most accurate perfusion readings are recorded in earlobe; however, the wrist does allow perfusion readings with appropriate accuracy [9]. PPG watch is not subjected to electrical

Therefore, this study proposes a PPG recording system for heart rate variability

ECG and PPG signal has been collected from 12 healthy subjects randomly selected with no prior symptoms of autonomic or cardiovascular disorder, ages between 20 and 30 years old. The data was collected with duration of 30 min including 10 min of adjustment, 10 min of rest (baseline) and 10 min of mental arithmetic testing. As a type of mental stress test, participants were needed to conduct an internet arithmetic test for 10 min in order to evaluate HRV under stress conditions such as time constraint. Lead II ECG setup with three electrodes were placed on the skin of the subject. For PPG signal, the wristband was placed on the left wrist. The subject was asked to sit down and make sure they are familiarized with the procedure. The ECG and PPG were recorded simultaneously after device

was setup. The data was imported to the MATLAB software for the signal

The recorded PPG and ECG signals were then pre-processed to extract the HRV

better alternative for portable heart monitoring device.

interference and drying or dropping-off of electrodes [8].

**2. Method**

processing (**Figure 2**).

**2.1 Signal processing**

**Figure 2.** *Experiment setup.*

**31**

using MATLAB software (**Figure 3**).

measurement that can be further used for mental stress assessment.

In deriving the HRV signal, appropriate QRS algorithms need to be applied to detect the peaks and its R wave, to obtain the interval of RR, and to find acceptable interpolation and resampling to produce a consistently sampled tachogram. By using the ECG signal, the resultant HRV could have several errors in the HRV signal due to drift, electromagnetic and biological disturbance, and the complicated morphology of the ECG signal [6].

Therefore, a simple recording system in deriving the HRV signal is needed. PPG which is an electro-optical technique that detect the changes of blood volume in the microvascular bed of the tissue is believed able to overcome the problem that faced by ECG signal and has been suggested as an alternative method to derive the HRV signal [7].

The PPG sensor's system is equipped with a light source and a detector, it also developed with red and infrared (IR) light-emitting diodes (LEDs) that commonly used as the light source. The light intensity of the PPG sensor monitor has been changed via the reflection from or transmission through the tissue. **Figure 1** shows the signal from ECG and PPG signal. Derivation HRV signal from ECG is calculated from R-R interval, while the calculation of HRV signal from PPG signal is used inter-beat interval (IBI) or pulse interval (PPI) [8].

The light traveling through biological tissue passes many materials, including pigments in the skin, bone, and arterial and venous blood. The changes of blood flow mainly occur in the arteries and arterioles (but not in the veins). For example, during the systolic phase of the cardiac cycle, the arteries contain more blood volume than the diastolic phase. PPG sensors optically detect changes in the blood flow volume, for instance, changes in the detected light intensity in the microvascular bed of tissue through the reflection from or transmission through the tissue [9].

**Figure 1.** *ECG and PPG signals.*

#### *Heart Rate Variability Recording System Using Photoplethysmography Sensor DOI: http://dx.doi.org/10.5772/intechopen.89901*

As previously discussed, both ECG and PPG system are able to provide information on cardiovascular activities. While ECG system allow better depiction of real cardiac movement through the measurement of the electrical signals produced by the action potential of the tissue, PPG allow adequate cardiovascular measurements such as heart rate and cardiac output only through pulsatile flow of blood in the arteries. Several studies have shown that the cardiovascular parameters collected through PPG systems are highly correlative and comparable to the measurements taken through standard ECG system [8, 10, 11]. This proves that despite not being able to illustrate exact cardiac waveforms or ectopic beats, PPG could serve as better alternative for portable heart monitoring device.

In terms of measurement accuracy, there are several factors to be considered to ensure the reliability of data collection. Topographical factor such as position of sensor placement on the body plays an important factor since different area of the body constitutes different accuracy of perfusion readings. The most accurate perfusion readings are recorded in earlobe; however, the wrist does allow perfusion readings with appropriate accuracy [9]. PPG watch is not subjected to electrical interference and drying or dropping-off of electrodes [8].

Therefore, this study proposes a PPG recording system for heart rate variability measurement that can be further used for mental stress assessment.

#### **2. Method**

HRV is a common measurement that can be extracted from the physiological measurement and helps to monitor the psychological stress [5]. It is because, HRV has direct connection with the autonomic nervous system (ANS) where any changes that occurred in human body can be directly detected by the HRV. The common methods to get the HRV are by using the ECG. However, there are several difficulties to record the ECG signal. First, it requires at least three surface electrodes to be placed on the skin to get single lead channel [6]. This clearly shows bulky of wires are needed for the recording and might cause distraction and uncomfortable feeling to the patient. Furthermore, it requires several times to set

In deriving the HRV signal, appropriate QRS algorithms need to be applied to detect the peaks and its R wave, to obtain the interval of RR, and to find acceptable interpolation and resampling to produce a consistently sampled tachogram. By using the ECG signal, the resultant HRV could have several errors in the HRV signal due to drift, electromagnetic and biological disturbance, and the complicated mor-

Therefore, a simple recording system in deriving the HRV signal is needed. PPG which is an electro-optical technique that detect the changes of blood volume in the microvascular bed of the tissue is believed able to overcome the problem that faced by ECG signal and has been suggested as an alternative method to derive the HRV

The PPG sensor's system is equipped with a light source and a detector, it also developed with red and infrared (IR) light-emitting diodes (LEDs) that commonly used as the light source. The light intensity of the PPG sensor monitor has been changed via the reflection from or transmission through the tissue. **Figure 1** shows the signal from ECG and PPG signal. Derivation HRV signal from ECG is calculated from R-R interval, while the calculation of HRV signal from PPG signal is used

The light traveling through biological tissue passes many materials, including pigments in the skin, bone, and arterial and venous blood. The changes of blood flow mainly occur in the arteries and arterioles (but not in the veins). For example, during the systolic phase of the cardiac cycle, the arteries contain more blood volume than the diastolic phase. PPG sensors optically detect changes in the blood flow volume, for instance, changes in the detected light intensity in the microvascular bed of tissue through the reflection from or transmission through

up the ECG before start the recording.

*Autonomic Nervous System Monitoring - Heart Rate Variability*

inter-beat interval (IBI) or pulse interval (PPI) [8].

phology of the ECG signal [6].

signal [7].

the tissue [9].

**Figure 1.**

**30**

*ECG and PPG signals.*

ECG and PPG signal has been collected from 12 healthy subjects randomly selected with no prior symptoms of autonomic or cardiovascular disorder, ages between 20 and 30 years old. The data was collected with duration of 30 min including 10 min of adjustment, 10 min of rest (baseline) and 10 min of mental arithmetic testing. As a type of mental stress test, participants were needed to conduct an internet arithmetic test for 10 min in order to evaluate HRV under stress conditions such as time constraint. Lead II ECG setup with three electrodes were placed on the skin of the subject. For PPG signal, the wristband was placed on the left wrist. The subject was asked to sit down and make sure they are familiarized with the procedure. The ECG and PPG were recorded simultaneously after device was setup. The data was imported to the MATLAB software for the signal processing (**Figure 2**).

#### **2.1 Signal processing**

The recorded PPG and ECG signals were then pre-processed to extract the HRV using MATLAB software (**Figure 3**).

**Figure 2.** *Experiment setup.*

contains superimposition of several waves (P, QRS, and T waves) as seen in **Figure 3** [16]. After initial denoising using BPF, the waveform undergoes differentiation process to obtain slope information overcome baseline drift. The next step is to perform signal squaring to emphasize higher frequency signal components (QRS waves) while attenuating components of low frequency. Resultant signal obtained through the squaring phase was then smoothed using moving average filter with a moving window integrator at 80 ms. A thresholding process is required to ensure that only the true QRS complex detected and the adaptive thresholds have been set

*Heart Rate Variability Recording System Using Photoplethysmography Sensor*

*DOI: http://dx.doi.org/10.5772/intechopen.89901*

The N-N interval was then computed and outliers presented in the signal was removed. Some of the data segment loss through the outlier extraction method was substituted by a new data segment using a linear interpolation method that resulted in NN intervals with nonequivalent moment sampling. However, the use of irregularly sampled NN intervals during HRV analysis characteristics such as frequency and TF analysis would cause generation of additional harmonic components and

Therefore, the HRV signals were resampled at standard sampling frequency of

The following HRV features (**Table 1**) were computed based on the guidelines

4 Hz [17]. Finally, the NN interval was passed through detrending process to

provided by Task Force of The European Society of Cardiology (ESC) [18].

for the classification of the locations of the detected R points.

artifacts in (**Figure 4**) [16].

overcome irregular trends.

**2.2 HRV feature extraction**

**Figure 4.**

**33**

*Overview of HRV signal processing using Pan and Tompkins algorithm.*

#### *2.1.1 HRV derived using PPG*

The PPG signal began with the band pass filter to attenuate noises contained in the signals. The band-pass filter was made of cascaded lowpass and high-pass filters. The cut-off frequencies that have been used 5 and 11 Hz. The low pass filter (LPF) eliminates the noise from other part of body, such as the muscle noise and also 50 Hz power line noise. The high pass filter (HPF) which is used to remove the motion artifacts [12].

After that, the PPG signal undergo the slope sum function (SSF). This method is to enhance the systolic peak of the PPG pulse and to suppress the balance of the pressure waveform by using equation in Eq. (1) [13].

$$\text{SSF} = \sum\_{k=i-w}^{i} \Delta x\_k, \quad \text{where} \quad \Delta x\_k = \begin{cases} \Delta\_k \colon \Delta\_k > 0 \\ 0 \colon \Delta\_k \le 0 \end{cases} \tag{1}$$

where w and sk are the length of the analyzing window and the filtered PPG signal, respectively. The SSF algorithm initialize the localization of the onset and offset of SSF then the pulse peak is identified as the local maxima within the range. The SSF signal produced coincides completely with the PPG pulse onset and offset and the pulse peaks appeared within the range of SSF pulse [14].

#### *2.1.2 HRV derived using ECG*

For ECG processing, Pan and Tompkins algorithm was implemented to get the HRV signal [15]. The Pan and Tompkins procedure are more complex as ECG signal *Heart Rate Variability Recording System Using Photoplethysmography Sensor DOI: http://dx.doi.org/10.5772/intechopen.89901*

contains superimposition of several waves (P, QRS, and T waves) as seen in **Figure 3** [16]. After initial denoising using BPF, the waveform undergoes differentiation process to obtain slope information overcome baseline drift. The next step is to perform signal squaring to emphasize higher frequency signal components (QRS waves) while attenuating components of low frequency. Resultant signal obtained through the squaring phase was then smoothed using moving average filter with a moving window integrator at 80 ms. A thresholding process is required to ensure that only the true QRS complex detected and the adaptive thresholds have been set for the classification of the locations of the detected R points.

The N-N interval was then computed and outliers presented in the signal was removed. Some of the data segment loss through the outlier extraction method was substituted by a new data segment using a linear interpolation method that resulted in NN intervals with nonequivalent moment sampling. However, the use of irregularly sampled NN intervals during HRV analysis characteristics such as frequency and TF analysis would cause generation of additional harmonic components and artifacts in (**Figure 4**) [16].

Therefore, the HRV signals were resampled at standard sampling frequency of 4 Hz [17]. Finally, the NN interval was passed through detrending process to overcome irregular trends.

#### **2.2 HRV feature extraction**

The following HRV features (**Table 1**) were computed based on the guidelines provided by Task Force of The European Society of Cardiology (ESC) [18].

**Figure 4.** *Overview of HRV signal processing using Pan and Tompkins algorithm.*

*2.1.1 HRV derived using PPG*

**Figure 3.**

*2.1.2 HRV derived using ECG*

**32**

pressure waveform by using equation in Eq. (1) [13].

*Autonomic Nervous System Monitoring - Heart Rate Variability*

*k*¼*i*�*w*

and the pulse peaks appeared within the range of SSF pulse [14].

SSF <sup>¼</sup> <sup>X</sup> *i*

The PPG signal began with the band pass filter to attenuate noises contained in the signals. The band-pass filter was made of cascaded lowpass and high-pass filters. The cut-off frequencies that have been used 5 and 11 Hz. The low pass filter (LPF) eliminates the noise from other part of body, such as the muscle noise and also 50 Hz power line noise. The high pass filter (HPF) which is used to remove the motion artifacts [12]. After that, the PPG signal undergo the slope sum function (SSF). This method is to enhance the systolic peak of the PPG pulse and to suppress the balance of the

*Pan and Tompkins algorithm for ECG signal analysis and slope sum function (SSF) for PPG signal analysis.*

<sup>Δ</sup>*xk*, where <sup>Δ</sup>*xk* <sup>¼</sup> <sup>Δ</sup>*Sk*: <sup>Δ</sup>*Sk*><sup>0</sup>

where w and sk are the length of the analyzing window and the filtered PPG signal, respectively. The SSF algorithm initialize the localization of the onset and offset of SSF then the pulse peak is identified as the local maxima within the range. The SSF signal produced coincides completely with the PPG pulse onset and offset

For ECG processing, Pan and Tompkins algorithm was implemented to get the HRV signal [15]. The Pan and Tompkins procedure are more complex as ECG signal

0 : Δ*Sk* ≤0

(1)

n


**Table 1.**

*Selected HRV features for extraction.*

#### *2.2.1 Time-domain features*

In this study, the time domain has been analyzed from HRV signal. Besides that, HRV features were extracted which are standard deviation of the normal-to-normal intervals (SDNN), standard deviation of the average of normal-to-normal intervals (SDANN) and root mean square successive difference (RMSSD). SDNN, SDANN and RMSSD were calculated by using equations in Eq. (2), Eq. (3) and Eq. (4) respectively.

$$\text{SDNN} = \sqrt{\frac{1}{N-1} \sum\_{n=1}^{N} \left[ RR\_n - mean(RR) \right]^2} \tag{2}$$

g v, ð Þ¼ τ Γ βð Þ þ jπν

*Heart Rate Variability Recording System Using Photoplethysmography Sensor*

*DOI: http://dx.doi.org/10.5772/intechopen.89901*

**2.3 Multiscale HRV comparison and correlation analysis**

elimination.

**3. Result and discussion**

**3.1 Signal processing**

relevant discussions of the findings.

HRV is shown in **Figures 7** and **8**.

*3.1.1 HRV derived using PPG*

**Figure 5.**

**35**

*The output of pulse peak detection from PPG signals using SSF.*

where Γ defines as gamma function and β is a real positive number between 0 and 1 that regulates the trade-off between component resolution and cross-cutting

In order to investigate the statistical significance (p-value < 0.05), Spearman's

This chapter presented the results obtained through pre-processing, feature extraction of HRV and multiscale comparison and correlation analysis along with

The results of each pre-processing phase for HRV assessment and the resulting PPG signal HRV are shown in **Figures 5** and **6**, whereas the resulting ECG signal

**Figure 5** presented the attenuation of the PPG signal pulses after the application of the SSF conversion. The pulse peaks became more distinct throughout the entire signal duration using SSF conversion as lower ectopic beats were also amplified to

correlation is conducted between HRV features of multiple length under both resting and stress conditions. It is performed to determine the correlation between the HRV features produced through PPG signal in comparing with standardized ECG signal. A nonparametric Wilcoxon signed-rank test was performed to observe

the difference between resting (baseline) and arithmetic stress test.

2 *=*Γ<sup>2</sup>

ð Þβ (6)

where N is total window length and NN is normal-to-normal time interval.

$$\text{SDANN} = \sqrt{\frac{1}{N\_{t-1}} \sum\_{n=1}^{N\_t} \left[ RR\_n - mean(RR) \right]^2} \tag{3}$$

where N5 is 5 min window length and NN is normal-to-normal time interval.

$$R \text{MSSD} = \sqrt{\frac{1}{N-2} \sum\_{n=3}^{N} \left[ I(n) - I(n-1) \right]^2} \tag{4}$$

where N is total window length.

#### *2.2.2 Frequency-domain features*

For this research, AR using the Burg estimation technique has been used to optimize forward and backward prediction errors. The power spectrum of the AR technique using the Burg estimation can be calculated as follows,

$$\mathbf{P\_{Burg}}(\mathbf{f}) = \frac{\hat{\mathbf{e}}\_{\mathbf{p}}}{\left| \mathbf{1} + \sum\_{l=1}^{\mathbf{p}} \hat{\mathbf{a}}\_{\mathbf{p}}(\mathbf{l}) \mathbf{e}^{-2j\mathbf{f}\mathbf{l}} \right|^{2}} \tag{5}$$

where êp represents the sum of both forward and backward prediction errors or the total least square error while p denotes the model order and â (l) indicate pth order of the AR coefficient.

#### *2.2.3 Nonlinear time-frequency features*

Nonlinear analysis was performed using Modified B-distribution (MBD) as the technique is capable of providing high resolution TF distribution without crossterms for HRV analysis. [16]. The kernel for the MBD as follows,

*Heart Rate Variability Recording System Using Photoplethysmography Sensor DOI: http://dx.doi.org/10.5772/intechopen.89901*

$$\mathbf{g}(\mathbf{v}, \mathbf{r}) = \Gamma(\mathfrak{P} + \mathbf{j}\mathfrak{w})^2 / \Gamma^2(\mathfrak{P}) \tag{6}$$

where Γ defines as gamma function and β is a real positive number between 0 and 1 that regulates the trade-off between component resolution and cross-cutting elimination.

#### **2.3 Multiscale HRV comparison and correlation analysis**

In order to investigate the statistical significance (p-value < 0.05), Spearman's correlation is conducted between HRV features of multiple length under both resting and stress conditions. It is performed to determine the correlation between the HRV features produced through PPG signal in comparing with standardized ECG signal. A nonparametric Wilcoxon signed-rank test was performed to observe the difference between resting (baseline) and arithmetic stress test.

#### **3. Result and discussion**

*2.2.1 Time-domain features*

*Selected HRV features for extraction.*

**Table 1.**

*SDNN* ¼

*SDANN* ¼

*RMSSD* ¼

where N is total window length.

*2.2.2 Frequency-domain features*

order of the AR coefficient.

**34**

*2.2.3 Nonlinear time-frequency features*

*N* � 1

1 *Ns*�<sup>1</sup>

*N* � 2

r

r

technique using the Burg estimation can be calculated as follows,

terms for HRV analysis. [16]. The kernel for the MBD as follows,

PBurgð Þ¼ <sup>f</sup> ^ep

r

Nonlinear analysis Shannon entropy: LF, HF, LF/HF, Total(O); Renyi

*Autonomic Nervous System Monitoring - Heart Rate Variability*

In this study, the time domain has been analyzed from HRV signal. Besides that, HRV features were extracted which are standard deviation of the normal-to-normal intervals (SDNN), standard deviation of the average of normal-to-normal intervals (SDANN) and root mean square successive difference (RMSSD). SDNN, SDANN and RMSSD were calculated by using equations in Eq. (2), Eq. (3) and Eq. (4) respectively.

**Processing method HRV features No. of features** Time analysis HR, SDNN, SDANN, RMSSD, HTI, NN50, pNN50 7 Frequency analysis VLF, LF, HF, LF/HF, LFnu, HFnu, TP 7

entropy: LF, HF, LF/HF, Total(O)

X*<sup>N</sup> n*¼1

where N is total window length and NN is normal-to-normal time interval.

X*Ns n*¼1

where N5 is 5 min window length and NN is normal-to-normal time interval.

X*<sup>N</sup> n*¼3

For this research, AR using the Burg estimation technique has been used to optimize forward and backward prediction errors. The power spectrum of the AR

<sup>1</sup> <sup>þ</sup> <sup>P</sup><sup>p</sup>

where êp represents the sum of both forward and backward prediction errors or the total least square error while p denotes the model order and â (l) indicate pth

Nonlinear analysis was performed using Modified B-distribution (MBD) as the technique is capable of providing high resolution TF distribution without cross-

<sup>l</sup>¼<sup>1</sup>^apð Þ<sup>l</sup> <sup>e</sup>�2jfl � � �

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1

½ � *RRn* � *mean RR* ð Þ <sup>2</sup>

Total 22

½ � *RRn* � *mean RR* ð Þ <sup>2</sup>

½ � *I n*ð Þ� *I n*ð Þ � <sup>1</sup> <sup>2</sup>

�

<sup>2</sup> (5)

(2)

8

(3)

(4)

This chapter presented the results obtained through pre-processing, feature extraction of HRV and multiscale comparison and correlation analysis along with relevant discussions of the findings.

#### **3.1 Signal processing**

The results of each pre-processing phase for HRV assessment and the resulting PPG signal HRV are shown in **Figures 5** and **6**, whereas the resulting ECG signal HRV is shown in **Figures 7** and **8**.

#### *3.1.1 HRV derived using PPG*

**Figure 5** presented the attenuation of the PPG signal pulses after the application of the SSF conversion. The pulse peaks became more distinct throughout the entire signal duration using SSF conversion as lower ectopic beats were also amplified to

**Figure 5.** *The output of pulse peak detection from PPG signals using SSF.*

**Figure 6.** *The HRV signal obtained from pulse peak detected in SSF signal.*

match ordinary pulse peaks that facilitate peak detection during thresholding method. **Figure 6** showed the resulting HRV signal that was obtained after removal, resampling and detrending of the outlier.

**Figure 8**) after smoothing procedure was then used for feature analysis. Smoothing process which comprised of the outlier removal, resampling and detrending.

*Heart Rate Variability Recording System Using Photoplethysmography Sensor*

*DOI: http://dx.doi.org/10.5772/intechopen.89901*

The analysis discussed in this section focuses mainly on the HRV features extracted using PPG method. Generally, the features selected have been associated

The HRV signal obtained under resting and stress conditions were subsequently plotted in **Figure 9** which also showed the HRV obtained with time excerpts of 10 min duration. In addition, different lengths of HRV excerpts carry different

*Samples of PPG-derived HRV for 10 min from same sample between resting and stress condition.*

relatively consistent magnitude.

*The HRV signal obtained from RR peak detected.*

with significant reactivity under stress conditions.

**3.2 HRV feature extraction**

**Figure 8.**

*3.2.1 Time-domain features*

**Figure 9.**

**37**

While evaluating the algorithm necessary for the HRV signal acquisition, it can be said that the pre-processing of the HRV signal recorded using the PPG system is simpler, as the signal contained only one type of wave (blood pulse) compared to the ECG signals usually containing a combination of three waves (P, QRS and T waves). Despite that, the HRV signal produced through both recordings do have

#### *3.1.2 HRV derived using ECG*

**Figure 7** showed the changes in the ECG signal throughout the Pan and Tompkins algorithm processes. It can be seen that the algorithm was able to detect the R-R intervals throughout the signal excerpt. This method was chosen due to the simplicity and efficacy of this algorithm in QRS detection among adult subjects with 99.3% accuracy rate [15]. The subsequent HRV signal produced (illustrated in

**Figure 7.** *The output of QRS peak detection from ECG signals using Pan and Tompkins algorithm.*

*Heart Rate Variability Recording System Using Photoplethysmography Sensor DOI: http://dx.doi.org/10.5772/intechopen.89901*

**Figure 8.** *The HRV signal obtained from RR peak detected.*

match ordinary pulse peaks that facilitate peak detection during thresholding method. **Figure 6** showed the resulting HRV signal that was obtained after removal,

*The output of QRS peak detection from ECG signals using Pan and Tompkins algorithm.*

**Figure 7** showed the changes in the ECG signal throughout the Pan and Tompkins algorithm processes. It can be seen that the algorithm was able to detect the R-R intervals throughout the signal excerpt. This method was chosen due to the simplicity and efficacy of this algorithm in QRS detection among adult subjects with 99.3% accuracy rate [15]. The subsequent HRV signal produced (illustrated in

resampling and detrending of the outlier.

*The HRV signal obtained from pulse peak detected in SSF signal.*

*Autonomic Nervous System Monitoring - Heart Rate Variability*

*3.1.2 HRV derived using ECG*

**Figure 6.**

**Figure 7.**

**36**

**Figure 8**) after smoothing procedure was then used for feature analysis. Smoothing process which comprised of the outlier removal, resampling and detrending.

While evaluating the algorithm necessary for the HRV signal acquisition, it can be said that the pre-processing of the HRV signal recorded using the PPG system is simpler, as the signal contained only one type of wave (blood pulse) compared to the ECG signals usually containing a combination of three waves (P, QRS and T waves). Despite that, the HRV signal produced through both recordings do have relatively consistent magnitude.

#### **3.2 HRV feature extraction**

The analysis discussed in this section focuses mainly on the HRV features extracted using PPG method. Generally, the features selected have been associated with significant reactivity under stress conditions.

#### *3.2.1 Time-domain features*

The HRV signal obtained under resting and stress conditions were subsequently plotted in **Figure 9** which also showed the HRV obtained with time excerpts of 10 min duration. In addition, different lengths of HRV excerpts carry different

**Figure 9.**

*Samples of PPG-derived HRV for 10 min from same sample between resting and stress condition.*

weightage of information on the HRV of the sample. Longer HRV excerpts allow better visualization of fluctuations in the HRV measurements in both conditions. However, it is difficult to distinguish the difference of HRV changes between resting and stress testing through visual inspection only.

**3.3 Multiscale HRV comparison and correlation analysis**

*DOI: http://dx.doi.org/10.5772/intechopen.89901*

*Heart Rate Variability Recording System Using Photoplethysmography Sensor*

Based on this finding, it can be seen that most of the HRV features extracted using the PPG device produced similar measurements as the ECG, especially for the TA and FA features. However, for more sophisticated measurements, such as nonlinear TF characteristics, the correlation between the two techniques was less important, particularly for smaller HRV characteristics. This could be due to the fact that PPG waveform mainly reflects the central artery properties which means factors such as artery stiffness may attenuate the signal and resulted in differences of NN intervals obtained between different individuals [19]. The PPG signals are also influenced by other parasympathetic activity such as temperature variations

**Time analysis** HR\* **0.964 0.970**

**Frequency analysis** VLF **0.918** 0.491

**Nonlinear analysis** ShEn LF\* **0.800 0.818**

*In bold, Spearman's correlation coefficient (rho) greater than 0.6 and resulted correlation significant (prho < 0.05); and based on results, the time domain HRV features (except HTI) maintained a significantly high correlation coefficient. Frequency domain features at 10 minutes showed consistent significant correlation with the equivalent standard HRV features during both resting and stress phases. For non-linear analysis, Shanon Entropy measurements (ShEn LF, ShEn HF, ShEn LFHF and ShEn O) showed to be highly correlate with standard excerpt for HRV features at 10 minutes. HR—mean of heart rate; SDNN—standard deviation of NN intervals; RMSSD—root mean square of the successive differences; SDANN—standard deviation of average NN intervals; NN50—NN intervals differing by more than 50 ms; pNN50—percentage of NN50 count; HTI—HRV triangular index; VLF—very low frequency; LF—low frequency; HF high frequency; TP—total power; Lfnu—low frequency normalized unit; Hfnu—high frequency normalized unit; ShEn*

*LF—Shannon entropy measurements; and ReEn—Renyi entropy measurements.*

*Correlation between multi-length HRV features with standard of 10 min.*

*\*Correlation is significant at the 0.01 level (2-tailed).*

**Table 2.**

**39**

**Features** *r* **Rest** *r* **Stress**

SDNN\* **0.893 0.920** RMSSD\* **0.793 0.801** SDANN\* **0.909 0.964** NN50\* **0.659 0.907** pNN50\* **0.716 0.851** HTI\* **0.800 0.773**

LF\* **0.773 0.764** HF\* **0.845 0.718** LF/HF\* **0.936 0.873** TP **0.827** 0.364 LFnu\* **0.936 0.873** Hfnu\* **0.936 0.864**

ShEn HF\* **0.645 0.836** ShEn LFHF\* **0.727 0.818** ShEn O\* **0.709 0.773** ReEn LF �0.064 0.255 ReEn HF\* **0.873 0.909** ReEn LFHF\* **0.873 0.791** ReEn O\* **0.909 0.945**

#### *3.2.2 Frequency-domain features*

The PSD can be classified into three components which are VLF band between 0.0033 and 0.04 Hz, LF band between 0.04 and 0.15 Hz and HF band between 0.15 and 0.4 Hz [18].

Based on the findings in **Figure 10**, the LF components increases during stress testing while HF components relatively decreases.

#### *3.2.3 Nonlinear time-frequency features*

For the plotted **Figure 11**, it was observed that more complex changes experienced during stress testing in 10 min. TFD plot was able to provide supplementary visualization of more complex changes within the HRV features during stress phase. Next, the changes within VLF and LF frequency bands were also more noticeable in TFD analysis.

#### **Figure 10.**

*Samples of PSD generated from PPG-derived HRV for 10 min from same sample between resting and stress condition.*

#### **Figure 11.**

*Samples of TFD generated from PPG-derived HRV for 10 min from same sample between resting and stress condition.*

#### **3.3 Multiscale HRV comparison and correlation analysis**

weightage of information on the HRV of the sample. Longer HRV excerpts allow better visualization of fluctuations in the HRV measurements in both conditions. However, it is difficult to distinguish the difference of HRV changes between

The PSD can be classified into three components which are VLF band between 0.0033 and 0.04 Hz, LF band between 0.04 and 0.15 Hz and HF band between 0.15

Based on the findings in **Figure 10**, the LF components increases during stress

For the plotted **Figure 11**, it was observed that more complex changes experienced during stress testing in 10 min. TFD plot was able to provide supplementary visualization of more complex changes within the HRV features during stress phase. Next, the changes within VLF and LF frequency bands were also more noticeable in

*Samples of PSD generated from PPG-derived HRV for 10 min from same sample between resting and stress*

*Samples of TFD generated from PPG-derived HRV for 10 min from same sample between resting and stress*

resting and stress testing through visual inspection only.

*Autonomic Nervous System Monitoring - Heart Rate Variability*

testing while HF components relatively decreases.

*3.2.3 Nonlinear time-frequency features*

*3.2.2 Frequency-domain features*

and 0.4 Hz [18].

TFD analysis.

**Figure 10.**

*condition.*

**Figure 11.**

*condition.*

**38**

Based on this finding, it can be seen that most of the HRV features extracted using the PPG device produced similar measurements as the ECG, especially for the TA and FA features. However, for more sophisticated measurements, such as nonlinear TF characteristics, the correlation between the two techniques was less important, particularly for smaller HRV characteristics. This could be due to the fact that PPG waveform mainly reflects the central artery properties which means factors such as artery stiffness may attenuate the signal and resulted in differences of NN intervals obtained between different individuals [19]. The PPG signals are also influenced by other parasympathetic activity such as temperature variations


*In bold, Spearman's correlation coefficient (rho) greater than 0.6 and resulted correlation significant (prho < 0.05); and based on results, the time domain HRV features (except HTI) maintained a significantly high correlation coefficient. Frequency domain features at 10 minutes showed consistent significant correlation with the equivalent standard HRV features during both resting and stress phases. For non-linear analysis, Shanon Entropy measurements (ShEn LF, ShEn HF, ShEn LFHF and ShEn O) showed to be highly correlate with standard excerpt for HRV features at 10 minutes. HR—mean of heart rate; SDNN—standard deviation of NN intervals; RMSSD—root mean square of the successive differences; SDANN—standard deviation of average NN intervals; NN50—NN intervals differing by more than 50 ms; pNN50—percentage of NN50 count; HTI—HRV triangular index; VLF—very low frequency; LF—low frequency; HF high frequency; TP—total power; Lfnu—low frequency normalized unit; Hfnu—high frequency normalized unit; ShEn LF—Shannon entropy measurements; and ReEn—Renyi entropy measurements. \*Correlation is significant at the 0.01 level (2-tailed).*

#### **Table 2.**

*Correlation between multi-length HRV features with standard of 10 min.*

[20] and could significantly changes due to factors such as body age, vascular age, physical status, sleeping hours, physical activities [21].

correlate to value produced with standard HRV excerpt. In the future, methods such as machine learning may be applied to test the accuracy between the use different PPG specifications such as measurement site, probe contact force and LED wavelengths which affect the reliability of its recordings or between different

This study was supported by the Research University Funding from Universiti

experimental protocol such as type of stressor and subject conditions.

*Heart Rate Variability Recording System Using Photoplethysmography Sensor*

*DOI: http://dx.doi.org/10.5772/intechopen.89901*

Noor Aimie-Salleh\*, Nurul Aliaa Abdul Ghani, Nurhafiezah Hasanudin

Universiti Teknologi Malaysia, Johor Bahru, Malaysia

provided the original work is properly cited.

\*Address all correspondence to: aimie@biomedical.utm.my

School of Biomedical Engineering and Health Sciences, Faculty of Engineering,

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Teknologi Malaysia, Malaysia (FRGS: R.J130000.7851.5F219).

**Acknowledgements**

**Author details**

**41**

and Siti Nur Shakiroh Shafie

Correlation analysis was performed to assess the interdependence between PPG-derived HRV and ECG-derived HRV as shown in **Table 2**.

In general, HRV features resulted less correlated in resting than during stress conditions. This is most likely due to the fact that HRV showed a more depressed dynamic during stress phase. Other than that, HRV features such as HR, NN50, TP, VLF, LF, HF, Lfnu, Hfnu, LF/HF, and Renyi entropy (LF, HF and Total(O)) has also showed significant correlation between the values measured for HRV excerpts collected using PPG and ECG. This prove that PPG is able to produce HRV signal with equivalent significant to HRV signal produced by ECG during stress testing [8, 22]. Besides, it can be deduced that HR, RMSSD, LF/HF, Lfnu and Hfnu features showed consistent characteristics as valid surrogate of the standard HRV which means regardless of length of HRV signal (between 1 and 10 min), these features would produce values that high correlate to value produced with standard HRV excerpt.

This study intends to investigate if there is different length of HRV excerpts provide valid measurement of HRV indices with comparison to standard 5-min excerpt for detection of mental stress. Although many studies have shown that HRV analyzes provide a reliable quantification technique for mental stress, it is hard to compare the precision of each method as their experimental design (i.e., duration of HRV characteristics) differs. Although it was claimed that the excerpt of 5-min HRV is the *gold standard* [18], the growing demand for wearable devices to instantly evaluate mental stress has increased interest in HRV computing characteristics shorter than the 5-min HRV standard [2]. In order to investigate the utility of various length of HRV excerpts in quantifying HRV features, 22 features were extracted at each time interval. The agreement between features at each time interval was compared with standard 5-min excerpt under both resting and stress phases. Overall, TA features (except HTI) conform significantly across all excerpts in correlation to standard excerpt while FA features (i.e., VLF, LF, HF, and TP) showed significant correlation across excerpts longer than 3 min while LFnu, HFnu and LF/HF showed consistent high correlation for all excerpts. As for timefrequency analysis, Shannon entropy measurements showed significant correlation for signal excerpts longer than 4 min while for Renyi entropy, only HF and Total(O) measurements showed significant correlation throughout all time excerpts.

Despite that, the limitation of these analyses is that correlation coefficient is blind to the possibility of bias caused by the difference in the mean or standard deviation between two measurements [23].

#### **4. Conclusion**

In comparison to conventional ECG, a correlation assessment between HRV characteristics obtained by PPG was also performed to observe any variation between the extracted measurements and analyze whether the PPG system is sufficiently robust to obtain HRV characteristics according to clinical standards. For this research, an ultra-short and short-term HRV feature was presumed to be a valid surrogate of the equivalent standard HRV if the feature sustained at a high correlation (i.e., rho > 0.6 and prho < 0.05) with the equivalent 5-min standard feature over all time scales and produced consistent trend and significant difference (pvalue < 0.05) during the rest and stress phase. Therefore, it can be deduced that HR, RMSSD, LF/HF, LFnu and HFnu features showed consistent characteristics as valid surrogate of the standard HRV which means regardless of length of HRV signal (between 1 and 10 min), these features would produce values that high

*Heart Rate Variability Recording System Using Photoplethysmography Sensor DOI: http://dx.doi.org/10.5772/intechopen.89901*

correlate to value produced with standard HRV excerpt. In the future, methods such as machine learning may be applied to test the accuracy between the use different PPG specifications such as measurement site, probe contact force and LED wavelengths which affect the reliability of its recordings or between different experimental protocol such as type of stressor and subject conditions.

### **Acknowledgements**

[20] and could significantly changes due to factors such as body age, vascular age,

Correlation analysis was performed to assess the interdependence between

In general, HRV features resulted less correlated in resting than during stress conditions. This is most likely due to the fact that HRV showed a more depressed dynamic during stress phase. Other than that, HRV features such as HR, NN50, TP, VLF, LF, HF, Lfnu, Hfnu, LF/HF, and Renyi entropy (LF, HF and Total(O)) has also showed significant correlation between the values measured for HRV excerpts collected using PPG and ECG. This prove that PPG is able to produce HRV signal with equivalent significant to HRV signal produced by ECG during stress testing [8, 22]. Besides, it can be deduced that HR, RMSSD, LF/HF, Lfnu and Hfnu features showed consistent characteristics as valid surrogate of the standard HRV which means regardless of length of HRV signal (between 1 and 10 min), these features would produce values that high correlate to value produced with standard HRV excerpt. This study intends to investigate if there is different length of HRV excerpts provide valid measurement of HRV indices with comparison to standard 5-min excerpt for detection of mental stress. Although many studies have shown that HRV analyzes provide a reliable quantification technique for mental stress, it is hard to compare the precision of each method as their experimental design (i.e., duration of HRV characteristics) differs. Although it was claimed that the excerpt of 5-min HRV is the *gold standard* [18], the growing demand for wearable devices to instantly evaluate mental stress has increased interest in HRV computing characteristics shorter than the 5-min HRV standard [2]. In order to investigate the utility of various length of HRV excerpts in quantifying HRV features, 22 features were extracted at each time interval. The agreement between features at each time interval was compared with standard 5-min excerpt under both resting and stress phases. Overall, TA features (except HTI) conform significantly across all excerpts in correlation to standard excerpt while FA features (i.e., VLF, LF, HF, and TP) showed significant correlation across excerpts longer than 3 min while LFnu, HFnu

and LF/HF showed consistent high correlation for all excerpts. As for time-

measurements showed significant correlation throughout all time excerpts.

deviation between two measurements [23].

**4. Conclusion**

**40**

frequency analysis, Shannon entropy measurements showed significant correlation for signal excerpts longer than 4 min while for Renyi entropy, only HF and Total(O)

Despite that, the limitation of these analyses is that correlation coefficient is blind to the possibility of bias caused by the difference in the mean or standard

In comparison to conventional ECG, a correlation assessment between HRV characteristics obtained by PPG was also performed to observe any variation between the extracted measurements and analyze whether the PPG system is sufficiently robust to obtain HRV characteristics according to clinical standards. For this research, an ultra-short and short-term HRV feature was presumed to be a valid surrogate of the equivalent standard HRV if the feature sustained at a high correlation (i.e., rho > 0.6 and prho < 0.05) with the equivalent 5-min standard feature over all time scales and produced consistent trend and significant difference (pvalue < 0.05) during the rest and stress phase. Therefore, it can be deduced that HR, RMSSD, LF/HF, LFnu and HFnu features showed consistent characteristics as valid surrogate of the standard HRV which means regardless of length of HRV signal (between 1 and 10 min), these features would produce values that high

physical status, sleeping hours, physical activities [21].

*Autonomic Nervous System Monitoring - Heart Rate Variability*

PPG-derived HRV and ECG-derived HRV as shown in **Table 2**.

This study was supported by the Research University Funding from Universiti Teknologi Malaysia, Malaysia (FRGS: R.J130000.7851.5F219).

### **Author details**

Noor Aimie-Salleh\*, Nurul Aliaa Abdul Ghani, Nurhafiezah Hasanudin and Siti Nur Shakiroh Shafie School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

\*Address all correspondence to: aimie@biomedical.utm.my

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **References**

[1] Munla N, Khalil M, Shahin A, Mourad A. Driver stress level detection using HRV analysis. In: International Conference on Advances in Biomedical Engineering, ICABME. 2015. pp. 61-64

[2] Pecchia L, Castaldo R, Montesinos L, Melillo P. Are ultra-short heart rate variability features good surrogates of short-term ones? State-of-the-art review and recommendations. Healthcare Technology Letters. 2018;**5**(3):94-100. DOI: 10.1049/htl.2017.0090

[3] Thayer JF, Åhs F, Fredrikson M, Sollers JJ, Wager TD. A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health. Neuroscience & Biobehavioral Reviews. 2012;**36**(2):747-756. DOI: 10.1016/j.neubiorev.2011.11.009

[4] Mccraty R, Shaffer F. Heart rate variability: New perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk. Global Advances in Health and Medicine. 2015;**4**(1):46-61. DOI: 10.7453/gahmj.2014.073

[5] Salahuddin L, Desok K. Detection of acute stress by heart rate variability using a prototype mobile ECG sensor. In: Proceedings - International Conference on Hybrid Information Technology, ICHIT, (November). 2006. pp. 1-25

[6] Bolanos M, Nazeran H, Haltiwanger E. Comparison of heart rate variability signal features derived from electrocardiography and photoplethysmography in healthy individuals. In: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. 2006. pp. 4289-4294

[7] Peng RC, Zhou XL, Lin WH, Zhang YT. Extraction of heart rate variability from smartphone photoplethysmograms. Computational and Mathematical Methods in Medicine. 2015;**1**:1-11. DOI: 10.1155/ 2015/516826

[14] Jang D-G, Farooq U, Park S-H, Hahn M. A robust method for pulse peak determination in a digital volume pulse waveform with a wandering baseline. IEEE Transactions on

*DOI: http://dx.doi.org/10.5772/intechopen.89901*

*Heart Rate Variability Recording System Using Photoplethysmography Sensor*

Alexandria A. Advances in

2007. pp. 17-20

photopletysmography signal analysis for biomedical applications. Sensors. 2018; **18**(6):1894. DOI: 10.3390/s18061894

[22] Chang FC, Chang CK, Chiu C, Hsu SF, Lin YD. Variations of HRV analysis in different approaches. In: 2007 Computers in Cardiology; IEEE.

[23] Castaldo R, Melillo P, Bracale U, Caserta M, Triassi M, Pecchia L. Acute mental stress assessment via short term HRV analysis in healthy adults: A systematic review with meta-analysis. Biomedical Signal Processing and Control. 2015, 2015;**18**:370-377. DOI:

10.1016/j.bspc.2015.02.012

Biomedical Circuits and Systems. 2014;

[15] Pan J, Tompkins JW. A real-time QRS detection algorithm. IEEE Transactions on Biomedical

Engineering. 1985;**32**:230-236. DOI:

[16] Malarvili BK, Aimie S, Pranshul S. In: Ismail I, editor. Introduction to ECG Signal Processing using MATLAB. 1st ed. Johor Bahru, Malaysia: Penerbit

[17] Chen H-K, Hu Y-F, Lin S-F. Methodological considerations in calculating heart rate variability based on wearable device heart rate samples. Computers in Biology and Medicine. 2018;**102**:396-401. DOI: 10.1016/j.

compbiomed.2018.08.023

Standards of measurement,

eurheartj.a014868

14-25. DOI: 10.2174/ 157340312801215782

6):337-340. DOI: 10.1111/ j.0954-6820.1968.tb10487.x

**43**

[18] Malik M, Thomas Bigger J, John Camm A, Kleiger RE, Malliani A, Moss AJ, et al. Heart rate variability:

physiological interpretation, and clinical use. European Heart Journal. 1996;**17**: 354-381. DOI: 10.1093/oxfordjournals.

[19] Elgendi M. On the analysis of fingertip photoplethysmogram signals. Current Cardiology Reviews. 2012;**8**(1):

[20] Heyman F, Ahlberg N-E. Effect of rapid distension of large arteries and veins on the vascular tone of the fingers. Acta Medica Scandinavica. 2009;**183**(1–

[21] Moraes J, Rocha M, Vasconcelos G, Vasconcelos Filho J, de Albuquerque V,

**8**(5):729-737. DOI: 10.1109/ TBCAS.2013.2295102

10.1109/TBME.1985.325532

UTM Press; 2018

[8] Selvaraj N, Jaryal A, Santhosh J, Deepak KK, Anand S. Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography. Journal of Medical Engineering & Technology. 2008;**32**(6):479-484. DOI: 10.1080/03091900701781317

[9] Tamura T, Maeda Y, Sekine M, Yoshida M. Wearable photoplethysmographic sensors—Past and present. Electronics. 2014;**3**(2): 282-302. DOI: 10.3390/ electronics3020282

[10] Lu G, Yang F, Taylor JA, Stein JF. A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects. Journal of Medical Engineering & Technology. 2009;**33**(8):634-641. DOI: 10.3109/ 03091900903150998

[11] Gil E, Orini M, Bailón R, Vergara JM, Mainardi L, Laguna P. Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during nonstationary conditions. Physiological Measurement. 2010;**31**(9):1271-1290. DOI: 10.1088/0967-3334/31/9/015

[12] Awodeyi AE, Alty SR, Ghavami M. On the filtering of photoplethysmography signals. In: IEEE International Conference on Bioinformatics and Bioengineering. 2014. pp. 175-178

[13] Jang D, Park S, Hahn M, Park S. A real-time pulse peak detection algorithm for the photoplethysmogram. IEEE. 2014;**2**(1):45-49. DOI: 10.12720/ ijeee.2.1.45-49

*Heart Rate Variability Recording System Using Photoplethysmography Sensor DOI: http://dx.doi.org/10.5772/intechopen.89901*

[14] Jang D-G, Farooq U, Park S-H, Hahn M. A robust method for pulse peak determination in a digital volume pulse waveform with a wandering baseline. IEEE Transactions on Biomedical Circuits and Systems. 2014; **8**(5):729-737. DOI: 10.1109/ TBCAS.2013.2295102

**References**

[1] Munla N, Khalil M, Shahin A, Mourad A. Driver stress level detection using HRV analysis. In: International Conference on Advances in Biomedical Engineering, ICABME. 2015. pp. 61-64

DOI: 10.1049/htl.2017.0090

[3] Thayer JF, Åhs F, Fredrikson M, Sollers JJ, Wager TD. A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health. Neuroscience & Biobehavioral Reviews. 2012;**36**(2):747-756. DOI: 10.1016/j.neubiorev.2011.11.009

[4] Mccraty R, Shaffer F. Heart rate variability: New perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk. Global Advances in Health and Medicine. 2015;**4**(1):46-61. DOI:

[5] Salahuddin L, Desok K. Detection of acute stress by heart rate variability using a prototype mobile ECG sensor. In: Proceedings - International Conference on Hybrid Information Technology, ICHIT, (November). 2006.

Haltiwanger E. Comparison of heart rate variability signal features derived from

photoplethysmography in healthy individuals. In: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings.

[7] Peng RC, Zhou XL, Lin WH, Zhang YT. Extraction of heart rate

10.7453/gahmj.2014.073

[6] Bolanos M, Nazeran H,

electrocardiography and

2006. pp. 4289-4294

pp. 1-25

**42**

[2] Pecchia L, Castaldo R, Montesinos L, Melillo P. Are ultra-short heart rate variability features good surrogates of short-term ones? State-of-the-art review and recommendations. Healthcare Technology Letters. 2018;**5**(3):94-100.

*Autonomic Nervous System Monitoring - Heart Rate Variability*

variability from smartphone

and Mathematical Methods in Medicine. 2015;**1**:1-11. DOI: 10.1155/

10.1080/03091900701781317

Yoshida M. Wearable

282-302. DOI: 10.3390/ electronics3020282

03091900903150998

[11] Gil E, Orini M, Bailón R, Vergara JM, Mainardi L, Laguna P. Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during nonstationary conditions. Physiological Measurement. 2010;**31**(9):1271-1290. DOI: 10.1088/0967-3334/31/9/015

[12] Awodeyi AE, Alty SR, Ghavami M. On the filtering of

International Conference on Bioinformatics and Bioengineering.

2014. pp. 175-178

ijeee.2.1.45-49

photoplethysmography signals. In: IEEE

[13] Jang D, Park S, Hahn M, Park S. A real-time pulse peak detection algorithm for the photoplethysmogram. IEEE. 2014;**2**(1):45-49. DOI: 10.12720/

[9] Tamura T, Maeda Y, Sekine M,

photoplethysmographic sensors—Past and present. Electronics. 2014;**3**(2):

[10] Lu G, Yang F, Taylor JA, Stein JF. A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects. Journal of Medical Engineering & Technology. 2009;**33**(8):634-641. DOI: 10.3109/

2015/516826

photoplethysmograms. Computational

[8] Selvaraj N, Jaryal A, Santhosh J, Deepak KK, Anand S. Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography. Journal of Medical Engineering & Technology. 2008;**32**(6):479-484. DOI:

[15] Pan J, Tompkins JW. A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering. 1985;**32**:230-236. DOI: 10.1109/TBME.1985.325532

[16] Malarvili BK, Aimie S, Pranshul S. In: Ismail I, editor. Introduction to ECG Signal Processing using MATLAB. 1st ed. Johor Bahru, Malaysia: Penerbit UTM Press; 2018

[17] Chen H-K, Hu Y-F, Lin S-F. Methodological considerations in calculating heart rate variability based on wearable device heart rate samples. Computers in Biology and Medicine. 2018;**102**:396-401. DOI: 10.1016/j. compbiomed.2018.08.023

[18] Malik M, Thomas Bigger J, John Camm A, Kleiger RE, Malliani A, Moss AJ, et al. Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. European Heart Journal. 1996;**17**: 354-381. DOI: 10.1093/oxfordjournals. eurheartj.a014868

[19] Elgendi M. On the analysis of fingertip photoplethysmogram signals. Current Cardiology Reviews. 2012;**8**(1): 14-25. DOI: 10.2174/ 157340312801215782

[20] Heyman F, Ahlberg N-E. Effect of rapid distension of large arteries and veins on the vascular tone of the fingers. Acta Medica Scandinavica. 2009;**183**(1– 6):337-340. DOI: 10.1111/ j.0954-6820.1968.tb10487.x

[21] Moraes J, Rocha M, Vasconcelos G, Vasconcelos Filho J, de Albuquerque V, Alexandria A. Advances in photopletysmography signal analysis for biomedical applications. Sensors. 2018; **18**(6):1894. DOI: 10.3390/s18061894

[22] Chang FC, Chang CK, Chiu C, Hsu SF, Lin YD. Variations of HRV analysis in different approaches. In: 2007 Computers in Cardiology; IEEE. 2007. pp. 17-20

[23] Castaldo R, Melillo P, Bracale U, Caserta M, Triassi M, Pecchia L. Acute mental stress assessment via short term HRV analysis in healthy adults: A systematic review with meta-analysis. Biomedical Signal Processing and Control. 2015, 2015;**18**:370-377. DOI: 10.1016/j.bspc.2015.02.012

**45**

Section 2

Heart Rate Variability and

Hypothermia

## Section 2
