Section 3 Special Focus

**89**

**Chapter 6**

**Abstract**

**1. Introduction**

the same system.

Heart Rate Variability as

Metabolic Disease

*and Julián Torres-Jácome*

Biomarker for Prognostic of

*Benjamín López-Silva, Martha Lucía Ita-Amador* 

**Keywords:** Poincaré plot, heart rate variability, metabolism, biomarker

and the interaction with other cyclic systems [2].

the prognosis and diagnosis of metabolic diseases.

*Alondra Albarado-Ibañez, Rosa Elena Arroyo-Carmona,* 

*Daniela Alexandra Bernabé-Sánchez, Marissa Limón-Cantú,* 

Lifestyle emerging diseases like obesity, metabolic syndrome (MeS), and diabetes mellitus are considered high-risk factors for lethal arrhythmias and side effects. A Poincaré plot is constructed with the time series of RR and PP electrocardiogram (ECG) intervals, using two stages: the new phase and the old phase. We proposed this diagram of two dimensions, a way to quantify and observe the regularity of events in space and time. Therefore, the heart rate variability (HRV) can be used as a biomarker for early prognostic and diagnostic of several metabolic diseases; additionally, this biomarker is obtained by a noninvasive tool like the electrocardiogram.

The biological phenomena could be explained by classical physics, and most of these phenomena are characterized by cycles. Usually, the time period required to "complete a cycle" is not constant. The study of time period fluctuations represents a way to assess interactions between other systems and the intrinsic properties of

The light/dark cycle (circadian rhythm) and the cyclic seasons that divide the year by changes on weather, ecology, and hour of daylight allowed the evolution of life on earth [1]. These series of events have influenced the organisms inducing cycles that are essential for life (hormonal, organ function, behavior, production of neurotransmitters, reproduction, and others); all cycles are fluctuations related to several biological phenomena. The study and knowledge of the fluctuations of biological phenomena are valuable to analyze the intrinsic properties of a system

The quantification of biological variability has been used to study several physiological phenomena, among them, fluctuations on the heart rate using the RR interval period of the electrocardiogram (ECG). The heart rate variability (HRV) is a useful health indicator [3], and in this chapter we detail how this tool is used for

#### **Chapter 6**

## Heart Rate Variability as Biomarker for Prognostic of Metabolic Disease

*Alondra Albarado-Ibañez, Rosa Elena Arroyo-Carmona, Daniela Alexandra Bernabé-Sánchez, Marissa Limón-Cantú, Benjamín López-Silva, Martha Lucía Ita-Amador and Julián Torres-Jácome*

#### **Abstract**

Lifestyle emerging diseases like obesity, metabolic syndrome (MeS), and diabetes mellitus are considered high-risk factors for lethal arrhythmias and side effects. A Poincaré plot is constructed with the time series of RR and PP electrocardiogram (ECG) intervals, using two stages: the new phase and the old phase. We proposed this diagram of two dimensions, a way to quantify and observe the regularity of events in space and time. Therefore, the heart rate variability (HRV) can be used as a biomarker for early prognostic and diagnostic of several metabolic diseases; additionally, this biomarker is obtained by a noninvasive tool like the electrocardiogram.

**Keywords:** Poincaré plot, heart rate variability, metabolism, biomarker

#### **1. Introduction**

The biological phenomena could be explained by classical physics, and most of these phenomena are characterized by cycles. Usually, the time period required to "complete a cycle" is not constant. The study of time period fluctuations represents a way to assess interactions between other systems and the intrinsic properties of the same system.

The light/dark cycle (circadian rhythm) and the cyclic seasons that divide the year by changes on weather, ecology, and hour of daylight allowed the evolution of life on earth [1]. These series of events have influenced the organisms inducing cycles that are essential for life (hormonal, organ function, behavior, production of neurotransmitters, reproduction, and others); all cycles are fluctuations related to several biological phenomena. The study and knowledge of the fluctuations of biological phenomena are valuable to analyze the intrinsic properties of a system and the interaction with other cyclic systems [2].

The quantification of biological variability has been used to study several physiological phenomena, among them, fluctuations on the heart rate using the RR interval period of the electrocardiogram (ECG). The heart rate variability (HRV) is a useful health indicator [3], and in this chapter we detail how this tool is used for the prognosis and diagnosis of metabolic diseases.

#### **2. Biological variability**

All organisms present dynamic and complex oscillations in their function. The time between every oscillation is called period, and it represents biological rhythms. These rhythms regulate all physiological processes with periods of milliseconds as neuronal activity, seconds as the heart rate, hours as hormone release, monthly as the ovarian cycle, and annually as the growth and migration. The biological rhythms are present in all levels of biological organization at the molecular, organelle, cell, and tissue levels; these organizations are present in vertebrates, invertebrates, and plants (**Figure 1**).

The study of period variations is essential because these fluctuations represent the interaction of the cycles with other systems or alterations on the intrinsic properties of the same system, individual and interspecific variability [4].

The periods can oscillate only under the influence of an external periodic signal originating exogenous rhythms; these allow changes in the variability of biological rhythm associated with external environmental synchronizer [5]. However, when the light/darkness external synchronizer is removed, a self-sustaining oscillation is shown, so it is said that the system has an autonomous endogenous rhythm.

The biological rhythms with a periodicity of 24 h are denominated circadian rhythms (*circa* = about, *diem* = a day). These circadian rhythms develop an endogen rhythm with one period close to 24 h under constant darkness, the free running, but can be synchronized again with the light and darkness; this phenomenon is called circadian entrainment. The circadian rhythms of longer period are infradians, such as the menstrual cycle, while shorter periods are ultradians, such as cardiac frequency, the autonomic system regulation, electrical activity of neurons, and secretion of hormones, among others (see **Figure 1**).

#### **Figure 1.**

*Variability biologic system. All organisms develop the variability of biological systems for environmental adaptation. Physiological and metabolic processes depend on the interaction between the central and peripheral rhythms.*

**91**

in the heart rate [2].

*Heart Rate Variability as Biomarker for Prognostic of Metabolic Disease*

metabolic, enzymatic, molecular, and physiological process.

The autonomous nervous system (sympathetic and parasympathetic) regulates the cardiovascular system that involves the heart rate variability. The relevance that the intermittent oscillations of peripheral clocks modulate the variability of the central clock is fundamental for health process. The coordination and communication among peripheral and central clock are essential for

The heart rate (HR) is determined by the activity of the sinoatrial node. The electrical activity propagates to the atria and then to the atrioventricular node, and, finally, the electrical activity reaches the ventricles triggering its contraction from apex to base. Any change in the origin and propagation of this electrical activity is denominated arrhythmia. The contraction and relaxation of cardiac tissue is a process named heartbeat. It is a cyclical event, the beats per minute produce the heart rate. Heart rate is a parameter that serves to diagnose some health problems in patients. When HR is increased, it is called tachycardia and the decrease of HR is called bradycardia. Tachycardia is related to exercise, emotions, the fight or flight phenomenon, among other activities. Bradycardia is related to sleep and rest. To measure HR there are several methods that are used in the clinic, like pulse taking,

The interaction of the organisms with its environment causes changes in the metabolic requirements of the multiple tissues that are depending on the circulating blood to supply oxygen and nutrients and remove metabolic waste, i.e., age, physical conditioning or exercise, behavior (emotions, pathologies, spice that is being studied, activity that takes place when the HR is taken, hemorrhages, heart attacks, addictions). In response to these demands, the heart adapts its interbeat intervals. These intervals vary thanks to the intrinsic properties of the heart (spontaneous activity of the sinoatrial node [6] and atrial and ventricular electrical properties along with extracellular matrix composition) and especially the influence of the autonomic nervous system (ANS), a communication pathway between the heart and the whole body. This system modulates the spontaneous activity of the sino-

The ANS regulates heart rate, visceral activities, and glandular functions to keep homeostasis. The ANS innervation on the heart can be divided in sympathetic (SNS) and parasympathetic (PNS) nervous system. They both have opposing effects on the heart activity. The sympathetic nervous system is responsible for the "fight or run" response, increasing the myocardium contractile properties and the rate of spontaneous activity of the sinoatrial node (SAN), the natural pacemaker of the heart, augmenting the heart rate. On the other hand, the parasympathetic nervous system has an inhibitory effect on the peacemaker and atrioventricular node (NAV) activity (see **Figure 2**), adjusting to rest states by means of a decrease

Sympathetic innervation secretes norepinephrine, a neurotransmitter that links to β1 receptors on the cardiac sarcolemma activating G proteins. This union induces a conformational change that dissociates the αs subunit activating adenylyl cyclase. The activated adenylyl cyclase catalyzes the conversion of ATP to AMPc, which joins directly to ionic channels responsible for the hyperpolarization activated pacemaker

atrial node and conduction system of the heart (**Figure 2**).

*DOI: http://dx.doi.org/10.5772/intechopen.88766*

auscultation, and electrocardiography.

**4. Heart rate variability**

**3. Heart rate**

The autonomous nervous system (sympathetic and parasympathetic) regulates the cardiovascular system that involves the heart rate variability. The relevance that the intermittent oscillations of peripheral clocks modulate the variability of the central clock is fundamental for health process. The coordination and communication among peripheral and central clock are essential for metabolic, enzymatic, molecular, and physiological process.

#### **3. Heart rate**

*Autonomic Nervous System Monitoring - Heart Rate Variability*

All organisms present dynamic and complex oscillations in their function. The time between every oscillation is called period, and it represents biological rhythms. These rhythms regulate all physiological processes with periods of milliseconds as neuronal activity, seconds as the heart rate, hours as hormone release, monthly as the ovarian cycle, and annually as the growth and migration. The biological rhythms are present in all levels of biological organization at the molecular, organelle, cell, and tissue levels; these organizations are present in vertebrates, inverte-

The study of period variations is essential because these fluctuations represent

The periods can oscillate only under the influence of an external periodic signal originating exogenous rhythms; these allow changes in the variability of biological rhythm associated with external environmental synchronizer [5]. However, when the light/darkness external synchronizer is removed, a self-sustaining oscillation is

The biological rhythms with a periodicity of 24 h are denominated circadian rhythms (*circa* = about, *diem* = a day). These circadian rhythms develop an endogen rhythm with one period close to 24 h under constant darkness, the free running, but can be synchronized again with the light and darkness; this phenomenon is called circadian entrainment. The circadian rhythms of longer period are infradians, such as the menstrual cycle, while shorter periods are ultradians, such as cardiac frequency, the autonomic system regulation, electrical activity of neurons, and

*Variability biologic system. All organisms develop the variability of biological systems for environmental adaptation. Physiological and metabolic processes depend on the interaction between the central and peripheral* 

the interaction of the cycles with other systems or alterations on the intrinsic properties of the same system, individual and interspecific variability [4].

shown, so it is said that the system has an autonomous endogenous rhythm.

secretion of hormones, among others (see **Figure 1**).

**2. Biological variability**

brates, and plants (**Figure 1**).

**90**

**Figure 1.**

*rhythms.*

The heart rate (HR) is determined by the activity of the sinoatrial node. The electrical activity propagates to the atria and then to the atrioventricular node, and, finally, the electrical activity reaches the ventricles triggering its contraction from apex to base. Any change in the origin and propagation of this electrical activity is denominated arrhythmia. The contraction and relaxation of cardiac tissue is a process named heartbeat. It is a cyclical event, the beats per minute produce the heart rate. Heart rate is a parameter that serves to diagnose some health problems in patients. When HR is increased, it is called tachycardia and the decrease of HR is called bradycardia. Tachycardia is related to exercise, emotions, the fight or flight phenomenon, among other activities. Bradycardia is related to sleep and rest. To measure HR there are several methods that are used in the clinic, like pulse taking, auscultation, and electrocardiography.

#### **4. Heart rate variability**

The interaction of the organisms with its environment causes changes in the metabolic requirements of the multiple tissues that are depending on the circulating blood to supply oxygen and nutrients and remove metabolic waste, i.e., age, physical conditioning or exercise, behavior (emotions, pathologies, spice that is being studied, activity that takes place when the HR is taken, hemorrhages, heart attacks, addictions). In response to these demands, the heart adapts its interbeat intervals.

These intervals vary thanks to the intrinsic properties of the heart (spontaneous activity of the sinoatrial node [6] and atrial and ventricular electrical properties along with extracellular matrix composition) and especially the influence of the autonomic nervous system (ANS), a communication pathway between the heart and the whole body. This system modulates the spontaneous activity of the sinoatrial node and conduction system of the heart (**Figure 2**).

The ANS regulates heart rate, visceral activities, and glandular functions to keep homeostasis. The ANS innervation on the heart can be divided in sympathetic (SNS) and parasympathetic (PNS) nervous system. They both have opposing effects on the heart activity. The sympathetic nervous system is responsible for the "fight or run" response, increasing the myocardium contractile properties and the rate of spontaneous activity of the sinoatrial node (SAN), the natural pacemaker of the heart, augmenting the heart rate. On the other hand, the parasympathetic nervous system has an inhibitory effect on the peacemaker and atrioventricular node (NAV) activity (see **Figure 2**), adjusting to rest states by means of a decrease in the heart rate [2].

Sympathetic innervation secretes norepinephrine, a neurotransmitter that links to β1 receptors on the cardiac sarcolemma activating G proteins. This union induces a conformational change that dissociates the αs subunit activating adenylyl cyclase. The activated adenylyl cyclase catalyzes the conversion of ATP to AMPc, which joins directly to ionic channels responsible for the hyperpolarization activated pacemaker

#### **Figure 2.**

*Heart rate variability sources. The time interval variations between consecutive heartbeats are result of the interaction between the autonomous nervous system modulation and intrinsic properties of the heart regulating its function.*

current (If) increasing the SAN depolarization rate. This stimulus also increases the opening probability and the inward calcium current enhancing the strength of cardiac contraction. Parasympathetic innervation releases acetylcholine, a neurotransmitter that binds to M2 receptors on the cardiac sarcolemma activating inhibitory G proteins, inducing a conformational change in Gi protein that dissociates the αi subunit inhibiting adenylyl cyclase leading to a decrease in the formation of AMPc, thus decreasing the SAN depolarization rate. The dynamical interaction between SNS and PNS enables the heart to fulfill the organism requirements in the short and long term.

Since the heartbeat is a cyclic phenomenon that repeats continually as a result of the interaction between spontaneous SAN activity [7], passive and active properties of the myocardium, conduction system, and ANS influence, it can be regarded as a result of the interaction of multiple coupled systems that oscillate. This complex nonlinear interaction reflects on interbeat interval variability; such phenomenon is called heart rate variability.

The interbeat intervals are usually assessed as the time between the R-wave peaks of the ECG signal (RR time series). This registry is consequence of the spatial and temporal sum of the electrical activity of the whole heart, and each wave is characteristic of specific electrical events. The R wave is representative of the QRS complex, which is the result of the ventricular transmural depolarization heterogeneity [8]. In view on the fact that the time from the start of the depolarizing wave at the sinoatrial node to the ventricle depolarization can account as other oscillation sources, inter-beat interval indicated by the PP interval (depolarization of the atria) can provide some insights that can be concealed by the RR interval (**Figure 2**).

HRV analysis is a valuable noninvasive method to quantify modifications caused by aging, disease progression, and other physiologic or pathologic changes. These alterations influence the oscillating systems or the way they couple, as sympathetic and parasympathetic heartbeat modulation besides intrinsic properties of the heart

**93**

**Figure 3.**

*Heart Rate Variability as Biomarker for Prognostic of Metabolic Disease*

that rely on extracellular matrix, sarcolemma composition (ionic channel density and kinetics, gap junction density, lipid composition), myocyte size, adipocyte, and fibroblast distribution. The etiology of these alterations is often related to metabolic

To analyze HRV using the ECG signal, we used the R waves of the QRS complex. The evaluation of the time between an R-wave peak (R1) and the next immediate R-wave peak (R2), the time interval between the appearance of an R wave and the next (t1–2) will be called heart period. The RR intervals are organized in chronological order, with an organized set of numbers. This set will be called the "heart

The heart rate (number of beats per unit of time) can be estimated as the inverse of the time period. When the frequency is stable, it is always the same, so are the period and the time series. When the time series is plotted against its order appearance, a time series graph is obtained [10]. The times series values determine the shape of the graph. When the frequency is constant, the graph is a parallel line to the time axis. And in the case that the frequency has variations (HRV), the graph is

The time series has all the information of the variability of system; then, to determine that two time series are similar, numerical values were allocated to this variability. The first tool used was RR time series spectral analysis, this technique is based on the use of all periodic signals consisting of sums of sine and cosine functions with different frequencies and amplitudes, with the purpose to determine which frequencies are involved in the formation of the time series [10]. The frequencies obtained by this mathematical tool have been associated to the nervous system, breathing, and other physiological functions. The frequencies and power spectrum of the different components of the time series are the parameters used to

The disadvantage (if it can be considered as one) of using this method is that not everybody is expert in Fourier series; therefore it's difficult to analyze, interpret, and perform. The second tool we use is the Poincaré plots. They require graphing

*Heart rate variability analysis. (A) Time series with chronological order and (B) Poincaré plot of time series.*

*DOI: http://dx.doi.org/10.5772/intechopen.88766*

diseases [9].

**5. Poincaré plots**

activity time series."

like in **Figure 3**.

quantify the variability with this method [11].

**5.1 Time series**

that rely on extracellular matrix, sarcolemma composition (ionic channel density and kinetics, gap junction density, lipid composition), myocyte size, adipocyte, and fibroblast distribution. The etiology of these alterations is often related to metabolic diseases [9].

#### **5. Poincaré plots**

#### **5.1 Time series**

*Autonomic Nervous System Monitoring - Heart Rate Variability*

current (If) increasing the SAN depolarization rate. This stimulus also increases the opening probability and the inward calcium current enhancing the strength of cardiac contraction. Parasympathetic innervation releases acetylcholine, a neurotransmitter that binds to M2 receptors on the cardiac sarcolemma activating inhibitory G proteins, inducing a conformational change in Gi protein that dissociates the αi subunit inhibiting adenylyl cyclase leading to a decrease in the formation of AMPc, thus decreasing the SAN depolarization rate. The dynamical interaction between SNS and PNS enables the heart to fulfill the organism requirements in the

*Heart rate variability sources. The time interval variations between consecutive heartbeats are result of the interaction between the autonomous nervous system modulation and intrinsic properties of the heart regulating* 

Since the heartbeat is a cyclic phenomenon that repeats continually as a result of the interaction between spontaneous SAN activity [7], passive and active properties of the myocardium, conduction system, and ANS influence, it can be regarded as a result of the interaction of multiple coupled systems that oscillate. This complex nonlinear interaction reflects on interbeat interval variability; such phenomenon is

The interbeat intervals are usually assessed as the time between the R-wave peaks of the ECG signal (RR time series). This registry is consequence of the spatial and temporal sum of the electrical activity of the whole heart, and each wave is characteristic of specific electrical events. The R wave is representative of the QRS complex, which is the result of the ventricular transmural depolarization heterogeneity [8]. In view on the fact that the time from the start of the depolarizing wave at the sinoatrial node to the ventricle depolarization can account as other oscillation sources, inter-beat interval indicated by the PP interval (depolarization of the atria) can provide some insights that can be concealed by the RR interval (**Figure 2**).

HRV analysis is a valuable noninvasive method to quantify modifications caused by aging, disease progression, and other physiologic or pathologic changes. These alterations influence the oscillating systems or the way they couple, as sympathetic and parasympathetic heartbeat modulation besides intrinsic properties of the heart

**92**

short and long term.

**Figure 2.**

*its function.*

called heart rate variability.

To analyze HRV using the ECG signal, we used the R waves of the QRS complex. The evaluation of the time between an R-wave peak (R1) and the next immediate R-wave peak (R2), the time interval between the appearance of an R wave and the next (t1–2) will be called heart period. The RR intervals are organized in chronological order, with an organized set of numbers. This set will be called the "heart activity time series."

The heart rate (number of beats per unit of time) can be estimated as the inverse of the time period. When the frequency is stable, it is always the same, so are the period and the time series. When the time series is plotted against its order appearance, a time series graph is obtained [10]. The times series values determine the shape of the graph. When the frequency is constant, the graph is a parallel line to the time axis. And in the case that the frequency has variations (HRV), the graph is like in **Figure 3**.

The time series has all the information of the variability of system; then, to determine that two time series are similar, numerical values were allocated to this variability. The first tool used was RR time series spectral analysis, this technique is based on the use of all periodic signals consisting of sums of sine and cosine functions with different frequencies and amplitudes, with the purpose to determine which frequencies are involved in the formation of the time series [10]. The frequencies obtained by this mathematical tool have been associated to the nervous system, breathing, and other physiological functions. The frequencies and power spectrum of the different components of the time series are the parameters used to quantify the variability with this method [11].

The disadvantage (if it can be considered as one) of using this method is that not everybody is expert in Fourier series; therefore it's difficult to analyze, interpret, and perform. The second tool we use is the Poincaré plots. They require graphing

**Figure 3.** *Heart rate variability analysis. (A) Time series with chronological order and (B) Poincaré plot of time series.*

the time series as follows: the first RR1 interval is assigned as x value, and then the value of the RR2 interval is assigned as y; this ordered pair is plotted on a Cartesian coordinate axis. Now we consider RR2 as x, and RR3 as y to plot it. After that the rest of the RR intervals are graphed in the same way, where the x is the RR(i) interval and the y is the interval RR(i+1); we plot each RR interval against the next immediate one. The resulting graphs are converted into spot stains; this chart is known as Poincaré plot (**Figure 3**). Now the question is how to quantify the spots. Before we give an answer to the problem, we will first describe the advantages of Poincaré plot.

#### **5.2 Advantages of the Poincaré plots**

First of all, it is important to mention that the heart rate will be 1/RRi; consequently the analysis of the interval variations gives us information of the heart rate variation. That is, the Poincaré plots give us information about changes in heart rate even if this parameter is not explicitly represented in this graph. As above mentioned, the frequency (F) is the inverse of the time period (T), hence F\*T = 1.

In the plot we trace the identity straight line RR(i+1) = RR(i), this line divides the plane into three parts: one where the RR(i+1) is equal to RR(i) (blue line in **Figure 3**), another where RR(i+1) > RR(i) which is the top of the identity line. And the third where RR(i+1) < RR(i) which is the part that is under the identity line (**Figure 3**). Therefore, just by looking at the point localization, we can say that the next interval has a higher value, i.e., the frequency is less. In other words, when the points fall above the identity straight line, the period i + 1 is greater and the further the point is from the identity line, the value of the period i + 1 will be greater; otherwise when the points are under the identity graph, the period i + 1 will be smaller, and the frequency will be higher [3].

#### **5.3 SDD1 and SDD2 calculation**

The distance between the points and the identity straight line tells us what the instantaneous (or sequential) changes of the RR interval will look like, as mentioned in the short term [12]. As an example, we will mention that when the distance from the points to the identity straight line is zero RR(i+1) = RR(i), there are no changes in the interval, but if this distance becomes greater, the variation between RR is greater. These distances are called D1i; the D1i distances that are above the identity straight line will be positive and those below will be negative in such a way that the average of these distances are zero, but the standard deviation of these distances (SDD1) will be different from zero, and this parameter will be used to characterize the width of the Poincaré plot. The width or SDD1 will be used to determine the variability of D1; this parameter is related to the short-term variability of the RR, and this relates to the interaction of the sympathetic system and the heart. To calculate SDD1 all distances from points to the identity line are calculated the average and standard deviation [3, 13]. Thus it is found that \_\_\_\_\_\_\_\_\_\_\_\_ \_

$$\text{Average auu sannuaru wevaluon [3, 15]. 1 runs u is 0-mmu unau}$$

$$D\_{1i} = \sqrt{\left(\frac{RR\_i - RR\_{i+1}}{2}\right)^2} \tag{1}$$

**95**

*Heart Rate Variability as Biomarker for Prognostic of Metabolic Disease*

distance is called D2j (Eq. (2) and **Figure 3**). All D2j distances are added and divided by the number of distances to get the average; the standard deviation to the latter is

The RR variability changes can be obtained based on the RR time series without

By obtaining the values of SDD1 and SDD2, we quantify the variability of the heart rate in the short and long term. This data defines the coefficient of variability

Using the Poincaré plots, the quantification of the variability in the heart rate is determined by calculating SDD1, SDD2, and the SDD1/SDD2 ratio. The advantage of this method is that the calculation of these parameters is clearly arithmetic, and just by looking at the Poincaré, plot you have an idea of how the variability is given.

A biological marker or biomarker is any substance, structure, or process that is objectively measured and evaluated as an indicator of normal biological processes. The biomarkers in the medical science field play essential role for disease detection, pathogenic responses, and therapeutic intervention. These markers are observational side products with potential utility in clinical and research studies [14]. Additionally they are used in new treatment strategies for clinical management. The biomarker field opens the opportunity to originate new knowledge in the complex

Metabolic alterations cause metabolic diseases as result of changes in chemical reactions in the organism by several enzyme deficit, developing alterations like lipid metabolism disorders. These diseases are associated with synthesis and degradation of fatty acids. The principal and general symptoms of metabolism injury are

For the last decade, the cardiovascular diseases have been the first cause of death worldwide, and the deadly arrhythmias have increased in the industrialized countries; this fact is related to lifestyle and metabolic alterations such as sedentarism and diet [15, 16]. Obesity and metabolic syndrome are disorders associated with

The metabolic syndrome has been described as a cluster of several signs like abdominal obesity, hyperglycemia, dyslipidemia, and high blood pressure (**Figure 4**). These factors predispose to develop cardiovascular diseases, and each component is strongly correlated with CVD. An opportune diagnosis is necessary to know the progression of MeS and predisposition to develop lethal risks. HRV analysis is a tool to assess cardiac function in patients with several pathologic conditions.

lethargy, weight alteration, inflammatory process, seizures, and jaundice.

*RRj* is the average value of the sum of all RR intervals.

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ <sup>2</sup>( 2¯ *RRj* <sup>−</sup>*RRj* <sup>−</sup> *RRj*+1 \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ 2

)

2

(2)

*DOI: http://dx.doi.org/10.5772/intechopen.88766*

*<sup>D</sup>*2*j* = √

called SDD2.

where ¯

as SDD1/SDD2.

**6. Biomarkers**

health scheme.

**7. Metabolic disease**

**8. Metabolic syndrome**

metabolic modifications.

using explicitly the time:

All D1i distances are added and divided by the number of distances to get the average; the standard deviation to the latter is called SDD1. SDD1 is a parameter that characterizes short-term variability.

Secondly, calculate the distance from all points to the perpendicular line that crosses the identity line at the coordinate point of the mean value (RRm, RRm). This distance is called D2j (Eq. (2) and **Figure 3**). All D2j distances are added and divided by the number of distances to get the average; the standard deviation to the latter is called SDD2.

The RR variability changes can be obtained based on the RR time series without using explicitly the time: \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

$$\begin{aligned} \text{The RR variability changes can be obtained based on the RR time series without using explicitly the time:}\\ D2j &= \sqrt{2\left(\frac{2\overline{R}\overline{R}\_j - RR\_j - RR\_{j+1}}{2}\right)^2} \\ \text{ (2) } \end{aligned} \tag{2}$$

where ¯ *RRj* is the average value of the sum of all RR intervals.

By obtaining the values of SDD1 and SDD2, we quantify the variability of the heart rate in the short and long term. This data defines the coefficient of variability as SDD1/SDD2.

Using the Poincaré plots, the quantification of the variability in the heart rate is determined by calculating SDD1, SDD2, and the SDD1/SDD2 ratio. The advantage of this method is that the calculation of these parameters is clearly arithmetic, and just by looking at the Poincaré, plot you have an idea of how the variability is given.

#### **6. Biomarkers**

*Autonomic Nervous System Monitoring - Heart Rate Variability*

**5.2 Advantages of the Poincaré plots**

frequency will be higher [3].

**5.3 SDD1 and SDD2 calculation**

the time series as follows: the first RR1 interval is assigned as x value, and then the value of the RR2 interval is assigned as y; this ordered pair is plotted on a Cartesian coordinate axis. Now we consider RR2 as x, and RR3 as y to plot it. After that the rest of the RR intervals are graphed in the same way, where the x is the RR(i) interval and the y is the interval RR(i+1); we plot each RR interval against the next immediate one. The resulting graphs are converted into spot stains; this chart is known as Poincaré plot (**Figure 3**). Now the question is how to quantify the spots. Before we give an answer to the problem, we will first describe the advantages of Poincaré

First of all, it is important to mention that the heart rate will be 1/RRi; consequently the analysis of the interval variations gives us information of the heart rate variation. That is, the Poincaré plots give us information about changes in heart rate even if this parameter is not explicitly represented in this graph. As above mentioned, the frequency (F) is the inverse of the time period (T), hence F\*T = 1.

In the plot we trace the identity straight line RR(i+1) = RR(i), this line divides the plane into three parts: one where the RR(i+1) is equal to RR(i) (blue line in **Figure 3**), another where RR(i+1) > RR(i) which is the top of the identity line. And the third where RR(i+1) < RR(i) which is the part that is under the identity line (**Figure 3**). Therefore, just by looking at the point localization, we can say that the next interval has a higher value, i.e., the frequency is less. In other words, when the points fall above the identity straight line, the period i + 1 is greater and the further the point is from the identity line, the value of the period i + 1 will be greater; otherwise when the points are under the identity graph, the period i + 1 will be smaller, and the

The distance between the points and the identity straight line tells us what the instantaneous (or sequential) changes of the RR interval will look like, as mentioned in the short term [12]. As an example, we will mention that when the distance from the points to the identity straight line is zero RR(i+1) = RR(i), there are no changes in the interval, but if this distance becomes greater, the variation between RR is greater. These distances are called D1i; the D1i distances that are above the identity straight line will be positive and those below will be negative in such a way that the average of these distances are zero, but the standard deviation of these distances (SDD1) will be different from zero, and this parameter will be used to characterize the width of the Poincaré plot. The width or SDD1 will be used to determine the variability of D1; this parameter is related to the short-term variability of the RR, and this relates to the interaction of the sympathetic system and the heart. To calculate SDD1 all distances from points to the identity line are calculated

\_\_\_\_\_\_\_\_\_\_\_\_ ( \_ *RRi* − *RRi*+1 2 ) 2

All D1i distances are added and divided by the number of distances to get the average; the standard deviation to the latter is called SDD1. SDD1 is a parameter that

Secondly, calculate the distance from all points to the perpendicular line that crosses the identity line at the coordinate point of the mean value (RRm, RRm). This

(1)

the average and standard deviation [3, 13]. Thus it is found that

*<sup>D</sup>*<sup>1</sup>*i* = √

characterizes short-term variability.

**94**

plot.

A biological marker or biomarker is any substance, structure, or process that is objectively measured and evaluated as an indicator of normal biological processes. The biomarkers in the medical science field play essential role for disease detection, pathogenic responses, and therapeutic intervention. These markers are observational side products with potential utility in clinical and research studies [14]. Additionally they are used in new treatment strategies for clinical management. The biomarker field opens the opportunity to originate new knowledge in the complex health scheme.

#### **7. Metabolic disease**

Metabolic alterations cause metabolic diseases as result of changes in chemical reactions in the organism by several enzyme deficit, developing alterations like lipid metabolism disorders. These diseases are associated with synthesis and degradation of fatty acids. The principal and general symptoms of metabolism injury are lethargy, weight alteration, inflammatory process, seizures, and jaundice.

#### **8. Metabolic syndrome**

For the last decade, the cardiovascular diseases have been the first cause of death worldwide, and the deadly arrhythmias have increased in the industrialized countries; this fact is related to lifestyle and metabolic alterations such as sedentarism and diet [15, 16]. Obesity and metabolic syndrome are disorders associated with metabolic modifications.

The metabolic syndrome has been described as a cluster of several signs like abdominal obesity, hyperglycemia, dyslipidemia, and high blood pressure (**Figure 4**). These factors predispose to develop cardiovascular diseases, and each component is strongly correlated with CVD. An opportune diagnosis is necessary to know the progression of MeS and predisposition to develop lethal risks. HRV analysis is a tool to assess cardiac function in patients with several pathologic conditions.

However, relationships between HRV and cardiac rhythm with changes in MeS have not been found, improving considerably the prognostic and diagnostic of MeS, as well as the side effects.

The ECG is a biomarker for early diagnosis of metabolic diseases [3], and to asses HRV, a five random minute interval must be measured and analyzed. When more time is analyzed, the characteristic SDD1 and SDD2 will be lost [3]. In humans, the MeS showed changes in RR intervals; SDD1 or short-term variability was modified in young adults, while in woman and elderly human, the alterations were vagal as sympathovagal balance (SDD1 and SDD2 [17]).

Also, spectral analysis with Fourier transform was used for the 24-h ECG record; this analysis showed that in human, the high frequencies (HF 0.15–0.40 Hz), which represents sympathetic modulation, were lower only in women with metabolic syndrome [18] and at low frequencies (LF 0.04–0.15 Hz), which represent parasympathetic modulation, heart rate was not altered by MeS. Furthermore, individual components of the MeS were highly correlated with imbalance cardiac autonomic system; the obesity modifies sympathetic nervous system [19]; hyperglycemia alters parasympathetic system [20]; and microalbuminuria, dyslipidemia, and hypertension do not alter neither of them but decrease LF/HF index (see **Figure 4**) [21, 22].

Rats with obesity and hypertension presented similar cardiovascular changes as humans: a decrease in parasympathetic system without any increase in sympathetic modulation [23], and only temporary alterations in sympathetic nervous system were reported in rats with high sucrose diet, insulin resistance, and visceral fat (epididymal fat) [24]. However, the rats with high sucrose diet showed higher LF than control [25], and also the heart rate was decreased showing sinus bradycardia and a threefold increase of heart rate variability, SDD1 15 ± 0.4, and SDD2 69 ± 1, compared with control animals 5.5 ± 0.1 and 26 ± 0.1, respectively. In addition, sinoatrial node doubled its variability as shown in the SDD1/SDD2 index = 0.25 for control condition and MeS: SDD1/SDD2 = 0.55 [26]. In genetically modified rats, cardiac alterations were observed independently on individual characteristic of MeS (see **Figure 4**).

#### **Figure 4.**

*The metabolic syndrome increases the heart rate variability. (A) The cluster signs of MeS increase the risk to develop cardiovascular diseases (CVD) and diabetes mellitus. (B) ECG of control and MeS rats, showing lower heart rate in MeS rats. (C) Poincaré plots exhibiting lower balance between parasympathetic and sympathetic systems. (D) Fourier analysis indicating that lower frequencies predominate in MeS rat RR time series.*

**97**

*Heart Rate Variability as Biomarker for Prognostic of Metabolic Disease*

The cardiac arrhythmias in diabetes mellitus are due to structural and functional remodeling, which are alterations in the architecture of the heart that include fibrosis, fat deposition, hypertrophy, modification in the utilization, and production of energy. In addition, electrical activity remodeling includes failure in electrical conduction, dysregulation in ion channels and gap junctions [27], and all these changes are added to the autonomic imbalance between the sympathetic and parasympathetic nervous systems until it becomes cardiac autonomic neuropathy (CAN), which is recognized as a risk for development of atrial fibrillation and sud-

In order to realize clinical diagnosis of CAN, the performing cardiac autonomic reflex test or neuropathy Ewing's battery is recommended. It consists of the assessment of the HRV in rest condition, while standing, during paced deep breathing, during sustained muscle contraction with the use of a handgrip dynamometer (handgrip exercise), and during and after a provoked increase in intrathoracic/

Unfortunately, these tests have limitations: patients must be aware so they can perform each of the tests, and it is necessary to suspend medications that could alter the results of the test (e.g., the avoidance of medications that cause hypotension,

Due to these disadvantages, the measurement of HRV has been used as an alternative for CAN diagnosis in recent years because it is a noninvasive test, it does not provoke pain in the patient, the analysis is performed in a short time, it is reliable, and it is a low-cost technique. In addition, this methodology allows the HRV analysis to be performed in less time because it is not necessary to have specialized knowledge in statistics or mathematics since the values of SDD1, SDD2, and SDD1/SDD2 are obtained by means of relatively simple arithmetic calculations and it does not need specialized software to perform them [3]. Another improvement is that the Poincaré plot analysis can be done with only 100 RR intervals, which excludes the use of a Holter registry without reducing the reliability and

Several authors have reported a decrease in HRV in patients with DM types 1 and 2 regardless of the method used to measure it (frequency-domain HRV or time domain). The decrease in HRV in diabetic patients is associated with an early phase of the evolution of CAN. There is a loss of parasympathetic function with a relative increase of sympathetic function causing an imbalance of the sympathetic/parasympathetic tone (without parasympathetic denervation). The patient experiments an increase in resting heart rate. In the next stage, sympathetic denervation takes place increasing the risk of arrhythmias [33]. Despite the existence of a large number of studies on HRV in diabetic patients, we still do not have a relationship that allows us to know the stage of damage in which the

On the other hand, we have validated the use of HRV and the measurement of SDD1, SDD2, and the Poincaré SDD1/SDD2 index (Eqs. (1) and (2)) as a biomarker for diagnosis and prognosis of cardiac autonomic neuropathy. For this purpose, a model of type 1 diabetes pharmacologically induced by STZ was used. This model was developed in CD1 mice in which the progress of disease is allowed for 10 weeks without insulin administration (the time compared with human 8 years of disease progression), which produces a decrease in the values of SDD1 (1 vs. 0.9), SDD2 (1.3 vs. 0.8), and SDD1/SDD2 (0.8 vs. 1.1) compared to the control (**Figure 6**) [32]. In this stage of the disease, no decrease in heart rate was reported, which suggests that CAN was in the early stages. However, after a time period equivalent to

abdominal pressure (maneuver of Valsalva) (see **Figure 5**) [29, 30].

such as diuretics, tricyclic antidepressants, and vasodilators) [31].

*DOI: http://dx.doi.org/10.5772/intechopen.88766*

den cardiac death (see **Figure 5**)[28].

sensitivity of the test [32].

autonomic nervous system is found.

**9. Diabetes mellitus**

#### **9. Diabetes mellitus**

*Autonomic Nervous System Monitoring - Heart Rate Variability*

sympathovagal balance (SDD1 and SDD2 [17]).

characteristic of MeS (see **Figure 4**).

well as the side effects.

However, relationships between HRV and cardiac rhythm with changes in MeS have not been found, improving considerably the prognostic and diagnostic of MeS, as

The ECG is a biomarker for early diagnosis of metabolic diseases [3], and to asses HRV, a five random minute interval must be measured and analyzed. When more time is analyzed, the characteristic SDD1 and SDD2 will be lost [3]. In humans, the MeS showed changes in RR intervals; SDD1 or short-term variability was modified in young adults, while in woman and elderly human, the alterations were vagal as

Also, spectral analysis with Fourier transform was used for the 24-h ECG record; this analysis showed that in human, the high frequencies (HF 0.15–0.40 Hz), which represents sympathetic modulation, were lower only in women with metabolic syndrome [18] and at low frequencies (LF 0.04–0.15 Hz), which represent parasympathetic modulation, heart rate was not altered by MeS. Furthermore, individual components of the MeS were highly correlated with imbalance cardiac autonomic system; the obesity modifies sympathetic nervous system [19]; hyperglycemia alters parasympathetic system [20]; and microalbuminuria, dyslipidemia, and hypertension do not alter neither of them but decrease LF/HF index (see **Figure 4**) [21, 22]. Rats with obesity and hypertension presented similar cardiovascular changes as humans: a decrease in parasympathetic system without any increase in sympathetic modulation [23], and only temporary alterations in sympathetic nervous system were reported in rats with high sucrose diet, insulin resistance, and

visceral fat (epididymal fat) [24]. However, the rats with high sucrose diet showed higher LF than control [25], and also the heart rate was decreased showing sinus bradycardia and a threefold increase of heart rate variability, SDD1 15 ± 0.4, and SDD2 69 ± 1, compared with control animals 5.5 ± 0.1 and 26 ± 0.1, respectively. In addition, sinoatrial node doubled its variability as shown in the SDD1/SDD2 index = 0.25 for control condition and MeS: SDD1/SDD2 = 0.55 [26]. In genetically modified rats, cardiac alterations were observed independently on individual

*The metabolic syndrome increases the heart rate variability. (A) The cluster signs of MeS increase the risk to develop cardiovascular diseases (CVD) and diabetes mellitus. (B) ECG of control and MeS rats, showing lower heart rate in MeS rats. (C) Poincaré plots exhibiting lower balance between parasympathetic and sympathetic systems. (D) Fourier analysis indicating that lower frequencies predominate in MeS rat RR time series.*

**96**

**Figure 4.**

The cardiac arrhythmias in diabetes mellitus are due to structural and functional remodeling, which are alterations in the architecture of the heart that include fibrosis, fat deposition, hypertrophy, modification in the utilization, and production of energy. In addition, electrical activity remodeling includes failure in electrical conduction, dysregulation in ion channels and gap junctions [27], and all these changes are added to the autonomic imbalance between the sympathetic and parasympathetic nervous systems until it becomes cardiac autonomic neuropathy (CAN), which is recognized as a risk for development of atrial fibrillation and sudden cardiac death (see **Figure 5**)[28].

In order to realize clinical diagnosis of CAN, the performing cardiac autonomic reflex test or neuropathy Ewing's battery is recommended. It consists of the assessment of the HRV in rest condition, while standing, during paced deep breathing, during sustained muscle contraction with the use of a handgrip dynamometer (handgrip exercise), and during and after a provoked increase in intrathoracic/ abdominal pressure (maneuver of Valsalva) (see **Figure 5**) [29, 30].

Unfortunately, these tests have limitations: patients must be aware so they can perform each of the tests, and it is necessary to suspend medications that could alter the results of the test (e.g., the avoidance of medications that cause hypotension, such as diuretics, tricyclic antidepressants, and vasodilators) [31].

Due to these disadvantages, the measurement of HRV has been used as an alternative for CAN diagnosis in recent years because it is a noninvasive test, it does not provoke pain in the patient, the analysis is performed in a short time, it is reliable, and it is a low-cost technique. In addition, this methodology allows the HRV analysis to be performed in less time because it is not necessary to have specialized knowledge in statistics or mathematics since the values of SDD1, SDD2, and SDD1/SDD2 are obtained by means of relatively simple arithmetic calculations and it does not need specialized software to perform them [3]. Another improvement is that the Poincaré plot analysis can be done with only 100 RR intervals, which excludes the use of a Holter registry without reducing the reliability and sensitivity of the test [32].

Several authors have reported a decrease in HRV in patients with DM types 1 and 2 regardless of the method used to measure it (frequency-domain HRV or time domain). The decrease in HRV in diabetic patients is associated with an early phase of the evolution of CAN. There is a loss of parasympathetic function with a relative increase of sympathetic function causing an imbalance of the sympathetic/parasympathetic tone (without parasympathetic denervation). The patient experiments an increase in resting heart rate. In the next stage, sympathetic denervation takes place increasing the risk of arrhythmias [33]. Despite the existence of a large number of studies on HRV in diabetic patients, we still do not have a relationship that allows us to know the stage of damage in which the autonomic nervous system is found.

On the other hand, we have validated the use of HRV and the measurement of SDD1, SDD2, and the Poincaré SDD1/SDD2 index (Eqs. (1) and (2)) as a biomarker for diagnosis and prognosis of cardiac autonomic neuropathy. For this purpose, a model of type 1 diabetes pharmacologically induced by STZ was used. This model was developed in CD1 mice in which the progress of disease is allowed for 10 weeks without insulin administration (the time compared with human 8 years of disease progression), which produces a decrease in the values of SDD1 (1 vs. 0.9), SDD2 (1.3 vs. 0.8), and SDD1/SDD2 (0.8 vs. 1.1) compared to the control (**Figure 6**) [32].

In this stage of the disease, no decrease in heart rate was reported, which suggests that CAN was in the early stages. However, after a time period equivalent to

#### **Figure 5.**

*Relations between alterations in DM and cardiac arrhythmias. The alterations in the architecture of heart tissue and functions produce a decrease in HRV during diabetes, which increment the risk of arrhythmias.*

**Figure 6.**

*Poincaré plots of interval RR of ECG. The HRV in conditions of control vs. 8 years of DM development. The influence of the ANS allowed maintaining the balance of an elliptical shape.*

15 human years of DM induction without hypoglycemic treatment CAN, a decrease in HRV is developed (**Figure 6**). Additionally, in this second stage, mice showed denervation in the pacemaker tissue [13]. We conclude that the use of HRV and Poincaré plots could detect CAN even in early stages of the disease, and therefore it will allow introducing therapeutic maneuvers to control the symptoms and delay the damage to the ANS due to DM.

**99**

*Heart Rate Variability as Biomarker for Prognostic of Metabolic Disease*

The periodic oscillations in biological phenomena are quantified with the purpose to use them as a health indicator (biomarker) in mammalian. By means of the ECG interval analysis, HRV is quantified using RR and PP time series. Poincaré plots were constructed, and three indicators were obtained: SDD1, SDD2, and SDD1/ SDD2 index. The behavior of these indicators is related with health or metabolic disease. In MeS, a sympathovagal imbalance was reported, and the parasympathetic system showed alterations with a twofold increase in SDD2 indicator. Furthermore, the three indicators were decreased by DM. These biomarkers have the advantages of being based on a noninvasive tool, being objective, and being obtained by easy arithmetic calculus. In addition, the shape of the Poincaré plots offers qualitative

, Rosa Elena Arroyo-Carmona2

, Martha Lucía Ita-Amador3

\*Address all correspondence to: jtorresjacome@gmail.com

provided the original work is properly cited.

1 Physiology Institute, Benemérita Universidad Autónoma de Puebla, Puebla,

2 Faculty of Chemistry Sciences, Benemérita Universidad Autónoma de Puebla,

3 Nororiental Complex, Faculty of Medicine, Benemérita Universidad Autónoma de

© 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,

, Marissa Limón-Cantú1

,

,

and Julián Torres-Jácome1

\*

*DOI: http://dx.doi.org/10.5772/intechopen.88766*

information by only looking at it.

**10. Conclusions**

**Author details**

Mexico

Puebla, Mexico

Alondra Albarado-Ibañez1

Benjamín López-Silva1

Puebla, Puebla, Mexico

Daniela Alexandra Bernabé-Sánchez1

### **10. Conclusions**

*Autonomic Nervous System Monitoring - Heart Rate Variability*

15 human years of DM induction without hypoglycemic treatment CAN, a decrease in HRV is developed (**Figure 6**). Additionally, in this second stage, mice showed denervation in the pacemaker tissue [13]. We conclude that the use of HRV and Poincaré plots could detect CAN even in early stages of the disease, and therefore it will allow introducing therapeutic maneuvers to control the symptoms and delay

*Poincaré plots of interval RR of ECG. The HRV in conditions of control vs. 8 years of DM development. The* 

*Relations between alterations in DM and cardiac arrhythmias. The alterations in the architecture of heart tissue and functions produce a decrease in HRV during diabetes, which increment the risk of arrhythmias.*

*influence of the ANS allowed maintaining the balance of an elliptical shape.*

**98**

**Figure 6.**

**Figure 5.**

the damage to the ANS due to DM.

The periodic oscillations in biological phenomena are quantified with the purpose to use them as a health indicator (biomarker) in mammalian. By means of the ECG interval analysis, HRV is quantified using RR and PP time series. Poincaré plots were constructed, and three indicators were obtained: SDD1, SDD2, and SDD1/ SDD2 index. The behavior of these indicators is related with health or metabolic disease. In MeS, a sympathovagal imbalance was reported, and the parasympathetic system showed alterations with a twofold increase in SDD2 indicator. Furthermore, the three indicators were decreased by DM. These biomarkers have the advantages of being based on a noninvasive tool, being objective, and being obtained by easy arithmetic calculus. In addition, the shape of the Poincaré plots offers qualitative information by only looking at it.

#### **Author details**

Alondra Albarado-Ibañez1 , Rosa Elena Arroyo-Carmona2 , Daniela Alexandra Bernabé-Sánchez1 , Marissa Limón-Cantú1 , Benjamín López-Silva1 , Martha Lucía Ita-Amador3 and Julián Torres-Jácome1 \*

1 Physiology Institute, Benemérita Universidad Autónoma de Puebla, Puebla, Mexico

2 Faculty of Chemistry Sciences, Benemérita Universidad Autónoma de Puebla, Puebla, Mexico

3 Nororiental Complex, Faculty of Medicine, Benemérita Universidad Autónoma de Puebla, Puebla, Mexico

\*Address all correspondence to: jtorresjacome@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**

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[13] Albarado-Ibanez A, Arroyo-Carmona RE, Sanchez-Hernandez R, Ramos-Ortiz G, Frank A, Garcia-Gudino D, et al. The role of the autonomic nervous system on cardiac rhythm during the evolution of diabetes mellitus using heart rate variability as a biomarker. Journal of Diabetes Research. 2019;**2019**:10

[14] Strimbu K, Tavel JA. What are biomarkers? Current Opinion in HIV and AIDS. 2010;**5**(6):463-466

[15] Tadic M, Ivanovic B, Celic V, Cuspidi C. Are the metabolic syndrome, blood pressure pattern, and their interaction responsible for the right ventricular remodeling? Blood Pressure Monitoring. 2013;**18**(4):195-202

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Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.

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*Heart Rate Variability as Biomarker for Prognostic of Metabolic Disease DOI: http://dx.doi.org/10.5772/intechopen.88766*

[16] Isik T, Kurt M, Uyarel H. Metabolic syndrome without overt diabetes is associated with prolonged proarrhythmogenic electrocardiographic parameters. Anadolu Kardiyoloji Dergisi. 2012;**12**(6):529

[17] Xhyheri B, Manfrini O, Mazzolini M, Pizzi C, Bugiardini R. Heart rate variability today. Progress in Cardiovascular Diseases. 2012;**55**(3):321-331

[18] Camm AJ, Malik M, Bigger JT, Breithardt G, Cerutti S, Cohen RJ, et al. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. European Heart Journal. 1996;**17**(3):354-381

[19] Min KB, Min JY, Paek D, Cho SI. The impact of the components of metabolic syndrome on heart rate variability: Using the NCEP-ATP III and IDF definitions. Pacing and Clinical Electrophysiology. 2008;**31**(5):584-591

[20] Stein PK, Barzilay JI, Domitrovich PP, Chaves PM, Gottdiener JS, Heckbert SR, et al. The relationship of heart rate and heart rate variability to non-diabetic fasting glucose levels and the metabolic syndrome: The Cardiovascular Health Study. Diabetic Medicine. 2007;**24**(8):855-863

[21] Liao DP, Carnethon M, Evans GW, Cascio WE, Heiss G. Lower heart rate variability is associated with the development of coronary heart disease in individuals with diabetes: TThe Atherosclerosis Risk in Communities (ARIC) Study. Diabetes. 2002;**51**(12):3524-3531

[22] Liao DP, Sloan RP, Cascio WE, Folsom AR, Liese AD, Evans GW, et al. Multiple metabolic syndrome is associated with lower heart rate variability: The atherosclerosis risk in communities study. Diabetes Care. 1998;**21**(12):2116-2122

[23] Grassi G, Arenare F, Quarti-Trevano F, Seravalle G, Mancia G. Heart rate, sympathetic cardiovascular influences, and the metabolic syndrome. Progress in Cardiovascular Diseases. 2009;**52**(1):31-37

[24] da Silva RJ, Bernardes N, Brito JD, Sanches IC, Irigoyen MC, De Angelis K. Simvastatin-induced cardiac autonomic control improvement in fructose-fed female rats. Clinics. 2011;**66**(10):1793-1796

[25] Albarado-Ibañez A, Hiriart M, Hoeflich AF, Torres-Jácome J. Assessment the change on rhythm cardiac produces by the metabolic syndrome in rats: Using nonlinear methods. In: Conference Nonlinear. 2018;**1**(1):2016-6

[26] Albarado-Ibanez A, Avelino-Cruz JE, Velasco M, Torres-Jacome J, Hiriart M. Metabolic syndrome remodels electrical activity of the sinoatrial node and produces arrhythmias in rats. PLoS One. 2013;**8**(11):e76534

[27] Grisanti LA. Diabetes and arrhythmias: Pathophysiology, mechanisms and therapeutic outcomes. Frontiers in Physiology. 2018;**9**:1669

[28] Agarwal SK, Norby FL, Whitsel EA, Soliman EZ, Chen LY, Loehr LR, et al. Cardiac autonomic dysfunction and incidence of atrial fibrillation results from 20 years follow-up. Journal of the American College of Cardiology. 2017;**69**(3):291-299

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**100**

1997;**30**:168-175

*Autonomic Nervous System Monitoring - Heart Rate Variability*

[9] Acharya UR, Faust O, Sree SV, Ghista DN, Dua S, Joseph P, et al. An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes. Computer

[10] Singh D, Vinod K, Saxena SC. Sampling frequency of the RR interval time series for spectral analysis of heart rate variability. Journal of Medical Engineering & Technology.

[11] Malliani A, Lombardi F, Pagani M. Power spectrum analysis of heart-ratevariability-a tool to explore neural regulatory mechanisms. British Heart

Methods in Biomechanics and Biomedical Engineering.

2013;**16**(2):222-234

2004;**28**(6):263-272

Journal. 1994;**71**(1):1-2

[13] Albarado-Ibanez A, Arroyo-Carmona RE, Sanchez-

Research. 2019;**2019**:10

[14] Strimbu K, Tavel JA. What are biomarkers? Current Opinion in HIV

and AIDS. 2010;**5**(6):463-466

[15] Tadic M, Ivanovic B, Celic V,

Monitoring. 2013;**18**(4):195-202

Cuspidi C. Are the metabolic syndrome, blood pressure pattern, and their interaction responsible for the right ventricular remodeling? Blood Pressure

[12] Brennan M, Palaniswami M, Kamen P. Do existing measures of Poincare plot geometry reflect nonlinear

features of heart rate variability? IEEE Transactions on Biomedical Engineering. 2001;**48**(11):1342-1347

Hernandez R, Ramos-Ortiz G, Frank A, Garcia-Gudino D, et al. The role of the autonomic nervous system on cardiac rhythm during the evolution of diabetes mellitus using heart rate variability as a biomarker. Journal of Diabetes

[1] Dardente H, Wood S, Ebling F, de Miera CS. An integrative view of mammalian seasonal neuroendocrinology. Journal of Neuroendocrinology. 2019;**31**(5):1-17

**References**

[2] Gordan R, Gwathmey JK, Xie LH. Autonomic and endocrine control of cardiovascular function. World Journal of Cardiology. 2015;**7**(4):204-214

Lopez-Serrano AL, Albarado-Ibanez A, Mendoza-Lucero FM, Medel-Cajica D, Lopez-Mayorga RM, et al. Heart rate variability as early biomarker for the evaluation of diabetes mellitus progress. Journal Diabetes Research.

[4] Aschoff J. Activity in anticipation

[5] Sharma VK, Chandrashekaran MK.

[6] Yaniv Y, Ahmet I, Liu J, Lyashkov AE,

[7] Bergfeldt L, Haga Y. Power spectral and Poincare plot characteristics in sinus node dysfunction. Journal of Applied Physiology (Bethesda, MD:

[8] Antzelevitch C, Shimizu W, Yan GX,

dispersion. Journal of Electrocardiology.

biological clocks. Current Science.

and in succession of a daily meal. Bollettino della Società Italiana di Biologia Sperimentale.

Zeitgebers (time cues) for

Guiriba TR, Okamoto Y, et al. Synchronization of sinoatrial node pacemaker cell clocks and its autonomic modulation impart complexity to heart beating intervals. Heart Rhythm.

1985). 2003;**94**(6):2217-2224

Sicouri S. Cellular basis for QT

2005;**89**(7):1136-1146

2014;**11**(7):1210-1219

[3] Arroyo-Carmona RE,

2016;**2016**:8483537

1991;**67**(3):213-228

**Chapter 7**

Cycle

**Abstract**

tem function (RMSSD, PNN50, and HF ms2

variability, homeostasis, life cycle

twenty-first postnatal day (P21) [6].

**1. Introduction**

**103**

Evolution of Parasympathetic

Modulation throughout the Life

*Moacir Fernandes de Godoy and Michele Lima Gregório*

Based on the largest data set ever available for analysis of heart rate variability (HRV) variables, in healthy individuals, it was possible to determine the evolutionary behavior of three representative components of parasympathetic nervous sys-

cycle: newborns, children and adolescents, young adults, and, finally, middle-aged adults. A near-parabolic and nonsynchronous behavior was observed among the different variables evaluated, with low values at first, then progressive elevation, and later fall, approximating the values of the newborns to the values of middleaged adults and suggesting that the autonomic nervous system, at least relatively to its parasympathetic component, undergoes a growing maturation that is completed in the young adult and later suffers a progressive degeneration, completing the life cycle. This fact should be considered when comparing the analysis between healthy

**Keywords:** autonomic nervous system, parasympathetic nervous system, heart rate

The autonomic nervous system (ANS) is a division of the peripheral nervous system and, based on anatomy and physiology, has three subdivisions: sympathetic nervous system (SNS), parasympathetic nervous system (PNS), and enteric nervous system (ENS). SNS has thoracolumbar distribution, and PNS has a craniosacral distribution, while ENS is the major part of the peripheral nervous system being found throughout the gastrointestinal tract, extending from the esophagus to the

ANS has the responsibility to ensure that homeostasis be maintained in the face of disturbances produced by both the external and internal environment [5]. In the heart of rats, ANS begins its development on the embryonic 18.5 day until the

Sympathetic neurons are located in the paravertebral ganglia, have long axonal projections to the organs, and produce excitatory effects mediated by the noradrenergic transmitter norepinephrine (NE). Conversely, parasympathetic neurons are located in ganglia near or on the surface of organs, have shorter axonal projections, and produce inhibitory effects mediated by the cholinergic transmitter

individuals and those with different states of pathological impairment.

rectum, and is also present in the pancreas and in the gallbladder [1–4].

), in different age groups of the life

[30] Bissinger A. Cardiac autonomic neuropathy: Why should cardiologists care about that? Journal of Diabetes Research. 2017;**5374176**:1-9

[31] Kuehl M, Stevens MJ. Cardiovascular autonomic neuropathies as complications of diabetes mellitus. Nature Reviews Endocrinology. 2012;**8**(7):405-416

[32] Nussinovitch U, Cohen O, Kaminer K, Ilani J, Nussinovitch N. Evaluating reliability of ultra-short ECG indices of heart rate variability in diabetes mellitus patients. Journal of Diabetes and its Complications. 2012;**26**(5):450-453

[33] Vinik E, Silva MP, Vinik AI. Measuring the relationship of quality of life and health status, including tumor burden, symptoms, and biochemical measures in patients with neuroendocrine Tumors. Endocrinology and Metabolism Clinics. 2011;**40**(1):97

#### **Chapter 7**

*Autonomic Nervous System Monitoring - Heart Rate Variability*

[30] Bissinger A. Cardiac autonomic neuropathy: Why should cardiologists care about that? Journal of Diabetes

Cardiovascular autonomic neuropathies as complications of diabetes mellitus. Nature Reviews Endocrinology.

Research. 2017;**5374176**:1-9

[31] Kuehl M, Stevens MJ.

[32] Nussinovitch U, Cohen O, Kaminer K, Ilani J, Nussinovitch N. Evaluating reliability of ultra-short ECG indices of heart rate variability in diabetes mellitus patients. Journal of Diabetes and its Complications.

[33] Vinik E, Silva MP, Vinik AI. Measuring the relationship of quality of life and health status, including tumor burden, symptoms, and biochemical measures in patients with neuroendocrine Tumors. Endocrinology and Metabolism Clinics. 2011;**40**(1):97

2012;**8**(7):405-416

2012;**26**(5):450-453

**102**

## Evolution of Parasympathetic Modulation throughout the Life Cycle

*Moacir Fernandes de Godoy and Michele Lima Gregório*

#### **Abstract**

Based on the largest data set ever available for analysis of heart rate variability (HRV) variables, in healthy individuals, it was possible to determine the evolutionary behavior of three representative components of parasympathetic nervous system function (RMSSD, PNN50, and HF ms2 ), in different age groups of the life cycle: newborns, children and adolescents, young adults, and, finally, middle-aged adults. A near-parabolic and nonsynchronous behavior was observed among the different variables evaluated, with low values at first, then progressive elevation, and later fall, approximating the values of the newborns to the values of middleaged adults and suggesting that the autonomic nervous system, at least relatively to its parasympathetic component, undergoes a growing maturation that is completed in the young adult and later suffers a progressive degeneration, completing the life cycle. This fact should be considered when comparing the analysis between healthy individuals and those with different states of pathological impairment.

**Keywords:** autonomic nervous system, parasympathetic nervous system, heart rate variability, homeostasis, life cycle

#### **1. Introduction**

The autonomic nervous system (ANS) is a division of the peripheral nervous system and, based on anatomy and physiology, has three subdivisions: sympathetic nervous system (SNS), parasympathetic nervous system (PNS), and enteric nervous system (ENS). SNS has thoracolumbar distribution, and PNS has a craniosacral distribution, while ENS is the major part of the peripheral nervous system being found throughout the gastrointestinal tract, extending from the esophagus to the rectum, and is also present in the pancreas and in the gallbladder [1–4].

ANS has the responsibility to ensure that homeostasis be maintained in the face of disturbances produced by both the external and internal environment [5]. In the heart of rats, ANS begins its development on the embryonic 18.5 day until the twenty-first postnatal day (P21) [6].

Sympathetic neurons are located in the paravertebral ganglia, have long axonal projections to the organs, and produce excitatory effects mediated by the noradrenergic transmitter norepinephrine (NE). Conversely, parasympathetic neurons are located in ganglia near or on the surface of organs, have shorter axonal projections, and produce inhibitory effects mediated by the cholinergic transmitter

acetylcholine (ACh). The enteric nervous system provides the intrinsic innervation of the gut, controlling different aspects of the gut function, such as motility [4].

possible. Thus, by searching the available databases (PubMed, Google Scholar, Cochrane Library, ScienceDirect, Wiley Online Library, SciELO, LILACS, and Thesis Banks of Brazilian Universities, among others) and following the PRISMA 2009 flow diagram [15], articles evaluating the values of heart rate variability (**Flow Diagram**) were included, and after, those directly related to the parasympathetic component of ANS, in the time domains (RMSSD and PNN50) and in the frequency

*Evolution of Parasympathetic Modulation throughout the Life Cycle*

the length of the time series, patient position, and analysis equipment, were selected but provided that the data were always collected from individuals specifically considered to be healthy. Based on this criterion, it is noteworthy that the individuals, who in the original work were cataloged as being from the general population, were not considered to be healthy because there are known comorbidities in this type of

Values with evident evidence of extreme outliers (three or more standard deviations below the first quartile or above the third quartile, from the set of values

), in humans, regardless of age and gender and also regardless of

domain (HF ms<sup>2</sup>

**Flow diagram**

**105**

sample, and so, they were not included.

*DOI: http://dx.doi.org/10.5772/intechopen.89456*

collected for a given variable) were excluded.

Although ANS can actually function autonomously, the central nervous system can contribute to a significant regulatory effect [3].

Heart rate variability (HRV) analysis is a practical, noninvasive, reproducible, and cost-effective resource that has been widely applied to study the autonomic behavior of the human organism, being particularly useful for the evaluation of sympathetic and parasympathetic components, although with regard to sympathetic behavior, there is still controversy about the mechanisms involved [7].

Higher vagally mediated heart rate variability is associated with better autonomic balance, better health outcomes, and flexible physiological responses. In contrast, lower HRV is associated with disease and all-cause mortality [8].

In [9], some reference values for normality of HRV variables are suggested, although highlighting that "As no comprehensive investigations of all HRV indices in large normal populations have yet been performed, some of the normal values […] were obtained from studies involving small number of subjects."

The reference values for normality cited and recommended in the Task Force were taken from the work of Bigger et al. (1995). The authors were based on only 274 individuals considered healthy and restricted to be 40–69 years old [10].

The aim of this chapter is restricted to the parasympathetic division of ANS. For the evaluation of this component, there is a well-established consensus that some variables, such as the root mean square of the successive RR interval differences (RMSSD), the percent of normal RR intervals that differed by more than 50 ms (PNN50) both in the time domain, and the absolute power of the high-frequency band component (HF ms2 ), in the frequency domain, specifically represent vagal modulation, presenting both diagnostic and prognostic properties [11–12].

Generally speaking, heart rate variability analysis has become the most used noninvasive tool to evaluate autonomic control mechanisms and to predict mortality risk in several clinical conditions, including coronary artery disease, heart failure, diabetes, and hypertension [13].

According to Goldberger et al. [14], there was some evidence that age influenced the responsiveness of the HRV parameters with changing parasympathetic effect. They studied 29 normal volunteers (15 women; mean age 39 12 years) after βadrenergic blockade with intravenous propranolol. Five-minute ECG recordings were made during graded infusions of phenylephrine and nitroprusside to achieve baroreflex-mediated increases and decreases in parasympathetic effect, respectively. There was some evidence that age influenced the responsiveness of the HRV parameters with changing parasympathetic effect, with significant association for RMSSD and PNN50.

Despite the significant amount of studies in the literature dealing with the HRV and autonomic regulation subject, there is a lack of studies with large series, addressing several variables in different age ranges, from birth to the elderly adult. So, we will evaluate the contribution of these three variables in the study of parasympathetic autonomic behavior throughout the life cycle based on the evaluation of a significant amount of data (835,902 in total) extracted from the literature regarding heart rate variability variables and admittedly related to the parasympathetic nervous system being 53,882 results from healthy individuals.

#### **2. Method**

The inclusion criterion was quite broad in view of the proposed objective, which was to establish reference values, based on the largest amount of information

*Evolution of Parasympathetic Modulation throughout the Life Cycle DOI: http://dx.doi.org/10.5772/intechopen.89456*

possible. Thus, by searching the available databases (PubMed, Google Scholar, Cochrane Library, ScienceDirect, Wiley Online Library, SciELO, LILACS, and Thesis Banks of Brazilian Universities, among others) and following the PRISMA 2009 flow diagram [15], articles evaluating the values of heart rate variability (**Flow Diagram**) were included, and after, those directly related to the parasympathetic component of ANS, in the time domains (RMSSD and PNN50) and in the frequency domain (HF ms<sup>2</sup> ), in humans, regardless of age and gender and also regardless of the length of the time series, patient position, and analysis equipment, were selected but provided that the data were always collected from individuals specifically considered to be healthy. Based on this criterion, it is noteworthy that the individuals, who in the original work were cataloged as being from the general population, were not considered to be healthy because there are known comorbidities in this type of sample, and so, they were not included.

Values with evident evidence of extreme outliers (three or more standard deviations below the first quartile or above the third quartile, from the set of values collected for a given variable) were excluded.

**Flow diagram**

acetylcholine (ACh). The enteric nervous system provides the intrinsic innervation of the gut, controlling different aspects of the gut function, such as motility [4]. Although ANS can actually function autonomously, the central nervous system

Heart rate variability (HRV) analysis is a practical, noninvasive, reproducible, and cost-effective resource that has been widely applied to study the autonomic behavior of the human organism, being particularly useful for the evaluation of sympathetic and parasympathetic components, although with regard to sympathetic behavior, there is still controversy about the mechanisms involved [7]. Higher vagally mediated heart rate variability is associated with better autonomic balance, better health outcomes, and flexible physiological responses. In contrast, lower HRV is associated with disease and all-cause mortality [8].

In [9], some reference values for normality of HRV variables are suggested, although highlighting that "As no comprehensive investigations of all HRV indices in large normal populations have yet been performed, some of the normal values

The reference values for normality cited and recommended in the Task Force were taken from the work of Bigger et al. (1995). The authors were based on only 274 individuals considered healthy and restricted to be 40–69 years old [10].

The aim of this chapter is restricted to the parasympathetic division of ANS. For the evaluation of this component, there is a well-established consensus that some variables, such as the root mean square of the successive RR interval differences (RMSSD), the percent of normal RR intervals that differed by more than 50 ms (PNN50) both in the time domain, and the absolute power of the high-frequency

), in the frequency domain, specifically represent vagal

[…] were obtained from studies involving small number of subjects."

modulation, presenting both diagnostic and prognostic properties [11–12].

Generally speaking, heart rate variability analysis has become the most used noninvasive tool to evaluate autonomic control mechanisms and to predict mortality risk in several clinical conditions, including coronary artery disease, heart fail-

According to Goldberger et al. [14], there was some evidence that age influenced the responsiveness of the HRV parameters with changing parasympathetic effect. They studied 29 normal volunteers (15 women; mean age 39 12 years) after βadrenergic blockade with intravenous propranolol. Five-minute ECG recordings were made during graded infusions of phenylephrine and nitroprusside to achieve baroreflex-mediated increases and decreases in parasympathetic effect, respectively. There was some evidence that age influenced the responsiveness of the HRV parameters with changing parasympathetic effect, with significant association for

Despite the significant amount of studies in the literature dealing with the HRV

The inclusion criterion was quite broad in view of the proposed objective, which

was to establish reference values, based on the largest amount of information

and autonomic regulation subject, there is a lack of studies with large series, addressing several variables in different age ranges, from birth to the elderly adult. So, we will evaluate the contribution of these three variables in the study of parasympathetic autonomic behavior throughout the life cycle based on the evaluation of a significant amount of data (835,902 in total) extracted from the literature regarding heart rate variability variables and admittedly related to the parasympa-

thetic nervous system being 53,882 results from healthy individuals.

can contribute to a significant regulatory effect [3].

*Autonomic Nervous System Monitoring - Heart Rate Variability*

band component (HF ms2

RMSSD and PNN50.

**2. Method**

**104**

ure, diabetes, and hypertension [13].


**Table 1.**

*Distribution of the literature data evaluated, in terms of the variable studied, highlighting the sample of interest (healthy individuals) and its size in relation to the total amount obtained.*


The statistical analysis (p-values, t-test unpaired, two-tailed, Welch correction) comparing the mean values for each variable along the age ranges is showed below.

**Group RMSSD PNN50 HF** 1 versus 2 P < 0.0001 P < 0.0001 P < 0.0001 1 versus 3 P < 0.0001 P < 0.0001 P < 0.0001 1 versus 4 P < 0.0001 P < 0.0001 P < 0.0001 2 versus 3 P = 0.0024 P < 0.0001 P < 0.0001 2 versus 4 P < 0.0001 P < 0.0001 P < 0.0001 3 versus 4 P < 0.0001 P < 0.0001 P < 0.0001

**Mean SD Mean SD Mean SD**

**Group Age range RMSSD PNN50 HF**

*Evolution of Parasympathetic Modulation throughout the Life Cycle*

*DOI: http://dx.doi.org/10.5772/intechopen.89456*

 Newborns 11.6 0.9 1.4 3.7 66.7 85.5 Up to 20 52.0 18.0 25.7 11.6 1124.0 710.8 20–40 53.1 22.2 19.9 12.9 2067.2 1144.7 40–70 28.2 11.8 6.9 0.3 236.3 248.5

*Mean and standard deviation of the variables studied according to the different age groups.*

As can be observed, the P-values were extremely robust indicating significant

Figures were constructed showing the behavior of each variable along the progressive increase in chronological age, from the healthy newborn group (subgroup 1) to children and adolescents (subgroup 2) and young adults (subgroup 3), until

*Mean evolutionary behavior of RMSSS values for the different age groups studied. RMSSD (root mean square of the successive RR interval differences in ms; 1, healthy newborns subgroup; 2, children and adolescents (up to 20 years) subgroup; 3, young adults (20–40 years) subgroup; 4, middle-aged adults (40–70 years) subgroup.*

extreme differences for all comparisons.

**Table 3.**

**Figure 1.**

**107**

reaching the middle-aged adults (subgroup 4).

F**igures 1–3** graphically demonstrate this behavior.

#### **Table 2.**

*Mean and standard deviation of the analyzed age groups and respective amounts of data analyzed, by studied variable..*

**Table 1** informs the studied variable, its domain, and the amount of values collected in the literature.

RMSSD (root mean square of the successive RR intervals differences, in ms; PNN50 (percent of normal RR intervals that differed by more than 50 ms in %); HF (absolute power of the high-frequency band; 0.15–0.40 Hz, in ms2 ).

Groupings were made by age range to precisely characterize the evolutionary behavior of the parasympathetic system throughout the life cycle. The amounts of data evaluated for each group and their average ages and standard deviations are shown in **Table 2**.

From all included studies, the mean and the standard deviation values of each variable of interest were extracted. The overall mean value was obtained by weighted average. The global standard deviation was obtained from the individual mean set of each study. As the collected values were the means and standard deviations, the existence of normality was assumed. The values from the different age groups were compared with the aid of the unpaired t-test assuming that the standard deviations of each group were not similar to each other (Welch correction). GraphPad InStat version 3.00 software was used to obtain P-values. A PDF file containing all the 335 references used to mounting the database can be solicited to the correspondent author. The large number of references would make it impossible to include them directly in the present text.

#### **3. Results**

**Table 3** summarizes the results obtained.

RMSSD (root mean square of the successive RR intervals differences in ms; PNN50 (percent of normal RR intervals that differed by more than 50 ms), HF (absolute power of the high-frequency band; 0.15–0.40 Hz); SD, standard deviation.


*Evolution of Parasympathetic Modulation throughout the Life Cycle DOI: http://dx.doi.org/10.5772/intechopen.89456*

**Table 3.**

**Table 1** informs the studied variable, its domain, and the amount of values

*Mean and standard deviation of the analyzed age groups and respective amounts of data analyzed, by studied*

**Domain Variable Total group General population + diseased Healthy** Time RMSSD ms 208,657 183,155 25,502 Time PNN50 49,400 35,043 14,357 Frequency HF ms2 159,894 145,871 14,023

*Distribution of the literature data evaluated, in terms of the variable studied, highlighting the sample of interest*

**Age range (years) Age mean SD RMSSD (ms) PNN50 (%) HF(ms<sup>2</sup>**

Newborns [0 a 3 days] 234 78 272 Up to 20 13.29 4.64 4,419 2,790 4,346 20–40 25.21 4.88 8,459 1,031 5,721 40–70 52.74 7.56 12,390 10,468 3,684 Totals 25,502 14,357 14,023

*(healthy individuals) and its size in relation to the total amount obtained.*

*Autonomic Nervous System Monitoring - Heart Rate Variability*

RMSSD (root mean square of the successive RR intervals differences, in ms; PNN50 (percent of normal RR intervals that differed by more than 50 ms in %); HF

Groupings were made by age range to precisely characterize the evolutionary behavior of the parasympathetic system throughout the life cycle. The amounts of data evaluated for each group and their average ages and standard deviations are

From all included studies, the mean and the standard deviation values of each

variable of interest were extracted. The overall mean value was obtained by weighted average. The global standard deviation was obtained from the individual mean set of each study. As the collected values were the means and standard deviations, the existence of normality was assumed. The values from the different age groups were compared with the aid of the unpaired t-test assuming that the standard deviations of each group were not similar to each other (Welch correction). GraphPad InStat version 3.00 software was used to obtain P-values. A PDF file containing all the 335 references used to mounting the database can be solicited to the correspondent author. The large number of references would make it impos-

RMSSD (root mean square of the successive RR intervals differences in ms; PNN50 (percent of normal RR intervals that differed by more than 50 ms), HF (absolute power of the high-frequency band; 0.15–0.40 Hz); SD, standard deviation.

).

**)**

(absolute power of the high-frequency band; 0.15–0.40 Hz, in ms2

sible to include them directly in the present text.

**Table 3** summarizes the results obtained.

collected in the literature.

**Table 1.**

**Table 2.**

*variable..*

shown in **Table 2**.

**3. Results**

**106**

*Mean and standard deviation of the variables studied according to the different age groups.*

The statistical analysis (p-values, t-test unpaired, two-tailed, Welch correction) comparing the mean values for each variable along the age ranges is showed below.


As can be observed, the P-values were extremely robust indicating significant extreme differences for all comparisons.

Figures were constructed showing the behavior of each variable along the progressive increase in chronological age, from the healthy newborn group (subgroup 1) to children and adolescents (subgroup 2) and young adults (subgroup 3), until reaching the middle-aged adults (subgroup 4).

F**igures 1–3** graphically demonstrate this behavior.

#### **Figure 1.**

*Mean evolutionary behavior of RMSSS values for the different age groups studied. RMSSD (root mean square of the successive RR interval differences in ms; 1, healthy newborns subgroup; 2, children and adolescents (up to 20 years) subgroup; 3, young adults (20–40 years) subgroup; 4, middle-aged adults (40–70 years) subgroup.*

decline progressively until the age of 70 (**Figure 2**), which would graphically be a "positively skewed tent" behavior. Finally, the HF variable rises from birth to about 40 years, when it begins to decline until 70 years of age being graphically a "nega-

We did not find significant studies on heart rate variability in healthy individuals over 70s, probably because above that age, the vast majority of the individuals already have some pathological impairment. Yes, it would exist for the general population, but that was not the focus at this moment. Therefore, a complete definition of HRV behavior in that older group, based on a significant sample like

The significant amount of data obtained, together with the extremely significant difference between the values in the different age groups, strongly indicates that this was not a casual finding but a true expression of parasympathetic autonomic

This is a relevant finding as it sheds new light on the knowledge of normal values in different age groups, since the current gold standard is still established by the Task Force data, based on only 274 cases and exclusively on the age range of

Like every other complex system, in accordance with Chaos Theory, ANS, at

nonsynchronous behavior for the main variables that evaluates it using heart rate variability, and this fact should be considered in the comparative analysis between healthy individuals and those with different grades of pathological impairment. Based on the largest data set ever available for healthy individuals, the found values can be proposed as reference standards for future studies about heart rate

The authors would like to thank the Brazilian CNPq (National Council for Scientific and Technological Development) [Processes 308759/2015-0 and 308555/ 2018-0] and to FAPESP (São Paulo Research Foundation) [Process 2017/125297] for

least in its parasympathetic component, exhibits a near-parabolic and

tively skewed tent" behavior (**Figure 3**).

*DOI: http://dx.doi.org/10.5772/intechopen.89456*

behavior.

40–69 years.

**5. Conclusion**

variability.

**Acknowledgements**

the financial support.

**Conflict of interest**

**109**

The authors declare no conflict of interest.

that used here for the other age groups, was not yet possible.

*Evolution of Parasympathetic Modulation throughout the Life Cycle*

#### **Figure 2.**

*Mean evolutionary behavior of PNN50 values for the different age groups studied.PNN50% ((percent of normal R-R intervals that differed by more than 50 ms); 1, healthy newborns subgroup; 2, children and adolescents (up to 20 years) subgroup; 3, young adults (20–40 years) subgroup; 4, middle-aged adults (40–70 years) subgroup.*

#### **Figure 3.**

*Mean evolutionary behavior of HF ms<sup>2</sup> values for the different age groups studied. HF ms<sup>2</sup> (absolute power of the high-frequency band; 0.15–0.40 Hz); 1, healthy newborns subgroup; 2, children and adolescents (up to 20 years) subgroup; 3: Young adults (20–40 years) subgroup; 4, middle-aged adults (40–70 years) subgroup.*

#### **4. Discussion**

It is well known that the heart rate variability declines with age. Bonnemeier et al. (2003) [16] obtained 24h recordings from 166 healthy volunteers (85 men and 81 women) aged 20–70 years. They found the most dramatic HRV parameter decrease between the second and third decades. Almeida-Santos et al. (2016) [17] obtained 24h ECG recordings of 1743 subjects of 40–100years of age. They found a linear decline in SDNN, SDANN, and SDNN index. Curiously, they described Ushaped pattern for RMSSD and pNN50 with aging, decreasing from 40 to 60 and then increasing after age 70.

The present study adds new information about this evolutionary behavior. It was quite clear that parasympathetic autonomic development in healthy individuals is peculiar, being reduced at birth, presenting a progressive elevation up to about 20 years of age (for the three variables studied), and typically, after that initial elevation, two different patterns of behavior occur. The RMSSD variable arises a little more until around 40 years of age when it then begins to decline progressively (**Figure 1**), which we might call as a "'negatively skewed tent' behavior." The PNN50 variable, once reaching its maximum levels around the age of 20, begins to

*Evolution of Parasympathetic Modulation throughout the Life Cycle DOI: http://dx.doi.org/10.5772/intechopen.89456*

decline progressively until the age of 70 (**Figure 2**), which would graphically be a "positively skewed tent" behavior. Finally, the HF variable rises from birth to about 40 years, when it begins to decline until 70 years of age being graphically a "negatively skewed tent" behavior (**Figure 3**).

We did not find significant studies on heart rate variability in healthy individuals over 70s, probably because above that age, the vast majority of the individuals already have some pathological impairment. Yes, it would exist for the general population, but that was not the focus at this moment. Therefore, a complete definition of HRV behavior in that older group, based on a significant sample like that used here for the other age groups, was not yet possible.

The significant amount of data obtained, together with the extremely significant difference between the values in the different age groups, strongly indicates that this was not a casual finding but a true expression of parasympathetic autonomic behavior.

This is a relevant finding as it sheds new light on the knowledge of normal values in different age groups, since the current gold standard is still established by the Task Force data, based on only 274 cases and exclusively on the age range of 40–69 years.

#### **5. Conclusion**

Like every other complex system, in accordance with Chaos Theory, ANS, at least in its parasympathetic component, exhibits a near-parabolic and nonsynchronous behavior for the main variables that evaluates it using heart rate variability, and this fact should be considered in the comparative analysis between healthy individuals and those with different grades of pathological impairment.

Based on the largest data set ever available for healthy individuals, the found values can be proposed as reference standards for future studies about heart rate variability.

#### **Acknowledgements**

The authors would like to thank the Brazilian CNPq (National Council for Scientific and Technological Development) [Processes 308759/2015-0 and 308555/ 2018-0] and to FAPESP (São Paulo Research Foundation) [Process 2017/125297] for the financial support.

#### **Conflict of interest**

The authors declare no conflict of interest.

**4. Discussion**

**Figure 3.**

**108**

**Figure 2.**

*(40–70 years) subgroup.*

then increasing after age 70.

It is well known that the heart rate variability declines with age. Bonnemeier et al. (2003) [16] obtained 24h recordings from 166 healthy volunteers (85 men and 81 women) aged 20–70 years. They found the most dramatic HRV parameter decrease between the second and third decades. Almeida-Santos et al. (2016) [17] obtained 24h ECG recordings of 1743 subjects of 40–100years of age. They found a linear decline in SDNN, SDANN, and SDNN index. Curiously, they described Ushaped pattern for RMSSD and pNN50 with aging, decreasing from 40 to 60 and

*Mean evolutionary behavior of HF ms<sup>2</sup> values for the different age groups studied. HF ms<sup>2</sup> (absolute power of the high-frequency band; 0.15–0.40 Hz); 1, healthy newborns subgroup; 2, children and adolescents (up to 20 years) subgroup; 3: Young adults (20–40 years) subgroup; 4, middle-aged adults (40–70 years) subgroup.*

*Mean evolutionary behavior of PNN50 values for the different age groups studied.PNN50% ((percent of normal R-R intervals that differed by more than 50 ms); 1, healthy newborns subgroup; 2, children and adolescents (up to 20 years) subgroup; 3, young adults (20–40 years) subgroup; 4, middle-aged adults*

*Autonomic Nervous System Monitoring - Heart Rate Variability*

The present study adds new information about this evolutionary behavior. It was quite clear that parasympathetic autonomic development in healthy individuals is peculiar, being reduced at birth, presenting a progressive elevation up to about 20 years of age (for the three variables studied), and typically, after that initial elevation, two different patterns of behavior occur. The RMSSD variable arises a little more until around 40 years of age when it then begins to decline progressively (**Figure 1**), which we might call as a "'negatively skewed tent' behavior." The PNN50 variable, once reaching its maximum levels around the age of 20, begins to

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pp. 71-81

DVDY.24597

10.1002/cphy.c150037

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[8] Gerardo GM, Williams DP, Kessler M, et al. Body mass index and parasympathetic nervous system

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*Evolution of Parasympathetic Modulation throughout the Life Cycle*

reactivity and recovery following graded exercise. American Journal of Human Biology. 2019;**31**:e23208. DOI:

[9] Heart rate variability. Standards of

interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. European Heart Journal. 1996;**17**:

[10] Bigger JT Jr, Fleiss JL, Steinman RC, Rolnitzky LM, Schneider WJ, Stein PK. RR variability in healthy, middle age persons compared with patients with chronic coronary heart disease or recent acute myocardial infarction. Circulation.

[11] Vanderlei LC, Pastre CM, Hoshi RA, Carvalho TD, Godoy MF. Basic notions of heart rate variability and its clinical applicability. Revista Brasileira de Cirurgia Cardiovascular. 2009;**24**(2):

[12] Shafer F, Ginsberg JP. An overview of heart rate variability metrics and norms. Frontiers in Public Health. 2017; **5**:258. DOI: 10.3389/ fpubh. 2017.00258

[13] Lombardi F, Huikuri H, Schmidt G,

variability: Easy to measure, difficult to interpret. On behalf of the e-rhythm study Group of European Heart Rhythm Association. Heart Rhythm. 2018; **15**(10):1559-1560. DOI: 10.1016 /j.

[14] Goldberger JJ, Challapalli S, Tung R, Parker MA, Kadish AH. Relationship of heart rate variability to parasympathetic effect. Circulation. 2001;**1983**(103):

[15] Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group.

Malik M. Short-term heart rate

hrthm.2018.05.023

1977-1983

measurement, physiological

10.1002/ajhb.23208

354-381

1995;**91**:1936-1943

205-217. DOI: 10.1590/ s0102-76382009000200018

[2] Kim C-H, Development KK-S. Differentiation of autonomic neurons.

Janeiro: Editora Elsevier; 2007

In: Robertson D, Biaggioni I, Burnstock G, Low PA, Paton JFR, editors. Primer on the Autonomic Nervous System. 3nd ed. Amsterdam: Elsevier; 2012. pp. 3-8. DOI: 10.1016/

B978-0-12-386525-0.00001-9

[3] Abel PW, Piascik MT. Introduction to autonomic nervous system drugs. In: Pharmacology and Therapeutics for Dentistry. Seventh ed. Elsevier; 2016.

[4] Ganz J. Gut feelings: Studying enteric nervous system development, function, and disease in the zebrafish model system. Developmental Dynamics. 2018;**247**:268-278. DOI: 10.1002/

[5] Wehrwein EA, Orer HS, Barman SM. Overview of the anatomy, physiology, and pharmacology of the autonomic nervous system. Comprehensive Physiology. 2016;**6**:1239-1278. DOI:

[6] Fregoso SP, Hoover DB. Development of cardiac parasympathetic neurons, glial

[7] Kiyono K, Hayano J, Watanabe E, Yamamoto Y. Heart rate variability (HRV) and sympathetic nerve activity. In: Iwase S, Hayano J, Orimo S, editors. Clinical Assessment of the Autonomic Nervous System. First ed. Japan: Springer; 2017. pp. 147-161. DOI: 10.1007/978-4-431-56012-8\_9

#### **Author details**

Moacir Fernandes de Godoy1,2\* and Michele Lima Gregório2

1 Department of Cardiology and Cardiovascular Surgery, Sao Jose do Rio Preto Medical School – Famerp, Sao Jose do Rio Preto, SP, Brazil

2 Transdisciplinary Nucleus for Chaos and Complexity Studies – NUTECC – Sao Jose do Rio Preto Medical School – Famerp, Sao Jose do Rio Preto, SP, Brazil

\*Address all correspondence to: mf60204@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.

*Evolution of Parasympathetic Modulation throughout the Life Cycle DOI: http://dx.doi.org/10.5772/intechopen.89456*

#### **References**

[1] Rang HP, Dale MM, Ritter JM, Flower RJ. Farmacologia. 6th ed. Rio de Janeiro: Editora Elsevier; 2007

[2] Kim C-H, Development KK-S. Differentiation of autonomic neurons. In: Robertson D, Biaggioni I, Burnstock G, Low PA, Paton JFR, editors. Primer on the Autonomic Nervous System. 3nd ed. Amsterdam: Elsevier; 2012. pp. 3-8. DOI: 10.1016/ B978-0-12-386525-0.00001-9

[3] Abel PW, Piascik MT. Introduction to autonomic nervous system drugs. In: Pharmacology and Therapeutics for Dentistry. Seventh ed. Elsevier; 2016. pp. 71-81

[4] Ganz J. Gut feelings: Studying enteric nervous system development, function, and disease in the zebrafish model system. Developmental Dynamics. 2018;**247**:268-278. DOI: 10.1002/ DVDY.24597

[5] Wehrwein EA, Orer HS, Barman SM. Overview of the anatomy, physiology, and pharmacology of the autonomic nervous system. Comprehensive Physiology. 2016;**6**:1239-1278. DOI: 10.1002/cphy.c150037

[6] Fregoso SP, Hoover DB. Development of cardiac parasympathetic neurons, glial cells, and regional cholinergic innervation of the mouse heart. Neuroscience. 2012;**221**:28-36

[7] Kiyono K, Hayano J, Watanabe E, Yamamoto Y. Heart rate variability (HRV) and sympathetic nerve activity. In: Iwase S, Hayano J, Orimo S, editors. Clinical Assessment of the Autonomic Nervous System. First ed. Japan: Springer; 2017. pp. 147-161. DOI: 10.1007/978-4-431-56012-8\_9

[8] Gerardo GM, Williams DP, Kessler M, et al. Body mass index and parasympathetic nervous system

reactivity and recovery following graded exercise. American Journal of Human Biology. 2019;**31**:e23208. DOI: 10.1002/ajhb.23208

[9] 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. European Heart Journal. 1996;**17**: 354-381

[10] Bigger JT Jr, Fleiss JL, Steinman RC, Rolnitzky LM, Schneider WJ, Stein PK. RR variability in healthy, middle age persons compared with patients with chronic coronary heart disease or recent acute myocardial infarction. Circulation. 1995;**91**:1936-1943

[11] Vanderlei LC, Pastre CM, Hoshi RA, Carvalho TD, Godoy MF. Basic notions of heart rate variability and its clinical applicability. Revista Brasileira de Cirurgia Cardiovascular. 2009;**24**(2): 205-217. DOI: 10.1590/ s0102-76382009000200018

[12] Shafer F, Ginsberg JP. An overview of heart rate variability metrics and norms. Frontiers in Public Health. 2017; **5**:258. DOI: 10.3389/ fpubh. 2017.00258

[13] Lombardi F, Huikuri H, Schmidt G, Malik M. Short-term heart rate variability: Easy to measure, difficult to interpret. On behalf of the e-rhythm study Group of European Heart Rhythm Association. Heart Rhythm. 2018; **15**(10):1559-1560. DOI: 10.1016 /j. hrthm.2018.05.023

[14] Goldberger JJ, Challapalli S, Tung R, Parker MA, Kadish AH. Relationship of heart rate variability to parasympathetic effect. Circulation. 2001;**1983**(103): 1977-1983

[15] Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group.

**Author details**

**110**

Moacir Fernandes de Godoy1,2\* and Michele Lima Gregório2

*Autonomic Nervous System Monitoring - Heart Rate Variability*

Medical School – Famerp, Sao Jose do Rio Preto, SP, Brazil

\*Address all correspondence to: mf60204@gmail.com

provided the original work is properly cited.

1 Department of Cardiology and Cardiovascular Surgery, Sao Jose do Rio Preto

2 Transdisciplinary Nucleus for Chaos and Complexity Studies – NUTECC – Sao Jose do Rio Preto Medical School – Famerp, Sao Jose do Rio Preto, SP, Brazil

© 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,

Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine. 2009;**6**(7):e1000097. DOI: 10.1371/ journal.pmed1000097

[16] Bonnemeier H, Richardt G, Potratz J, Wiegand UK, Brandes A, Kluge N, et al. Circadian profile of cardiac autonomic nervous modulation in healthy subjects: Differing effects of aging and gender on heart rate variability. Journal of Cardiovascular Electrophysiology. 2003;**14**:791-799. DOI: 10.1046/j.1540-8167.2003.03078.x

[17] Almeida-Santos MA, Barreto-Filho JA, Oliveira JL, Reis FP, da Cunha Oliveira CC, Sousa AC. Aging, heart rate variability and patterns of autonomic regulation of the heart. Archives of Gerontology and Geriatrics. 2016;**63**: 1-8. DOI: 10.1016/j.archger.2015.11.011

**113**

**Chapter 8**

**Abstract**

**1. Introduction**

The Role of Magnetic Resonance

Medical imaging of the nervous system is the methodology used to achieve pictures of parts of the nervous system for therapeutic uses to recognize the ailments. Magnetic resonance imaging (MRI) is a kind of medical imaging tool that utilizes solid magnetic fields and radio waves to deliver point-by-point pictures of the inside of the body. There are large number of imaging methodologies done each week around the world. Medical imaging is developing rapidly due to developments in image acquisition tools including functional MRI and hybrid imaging modalities. This chapter abridged the role of magnetic resonance imaging (MRI) in autonomic nervous system monitoring. This chapter also summarizes the image interpretation

The nervous system is divided into two parts, the central (CNS) and peripheral (PNS) part. The CNS includes the majority of the neural tissues and comprises the brain and spinal cord. PNS comprises all the structures outside the CNS and includes the special sense, spinal and cranial, and autonomic nervous system (ANS) [1–4]. The nervous system is composed mostly of the axons of sensual and motor neurons that permit between the CNS and the body. The autonomic sensory system (ANS) is divided into the peripheral sensory parts that provision the muscles and organs and influence the capacity of inner organs [5–7]. This system is considered as a regulatory framework that stimulates the action of those organs and muscles. This system manages in essence capacities, for example, the pulse, absorption, optical reaction, pee, and voluptuous stimulation [8–11]. This framework is the essential instrument responsible for the battle or flight reaction. Inside the mind, the central nerves manage this system. Autonomic capacities incorporate control of breath, heart guideline (the cardiovascular control focus), vasomotor action (the vasomotor focus), and certain reflex activities, for example, hacking, wheezing, gulping, and heaving [11–14]. This system is then subdivided into different zones that are connected additionally to ANS and sensory structures outside to the cerebrum. The central nerve over the cerebrum trunk goes as an integrator for autonomic capacities, accepting ANS administrative contribution from the limbic framework to do as such. The ANS has three subdivisions: the thoughtful sensory, the parasympathetic

Imaging (MRI) in Autonomic

Nervous System Monitoring

*Yousif Mohamed Y. Abdallah and Nouf H. Abuhadi*

challenges in diagnosing autonomic nervous system disorders.

**Keywords:** medical, imaging, autonomic nervous system

#### **Chapter 8**

Preferred reporting items for systematic

*Autonomic Nervous System Monitoring - Heart Rate Variability*

reviews and meta-analyses: The PRISMA statement. PLoS Medicine. 2009;**6**(7):e1000097. DOI: 10.1371/

[16] Bonnemeier H, Richardt G, Potratz J, Wiegand UK, Brandes A, Kluge N, et al. Circadian profile of cardiac autonomic nervous modulation in healthy subjects: Differing effects of

aging and gender on heart rate variability. Journal of Cardiovascular Electrophysiology. 2003;**14**:791-799. DOI: 10.1046/j.1540-8167.2003.03078.x

[17] Almeida-Santos MA, Barreto-Filho JA, Oliveira JL, Reis FP, da Cunha Oliveira CC, Sousa AC. Aging, heart rate variability and patterns of autonomic regulation of the heart. Archives of Gerontology and Geriatrics. 2016;**63**: 1-8. DOI: 10.1016/j.archger.2015.11.011

journal.pmed1000097

**112**

## The Role of Magnetic Resonance Imaging (MRI) in Autonomic Nervous System Monitoring

*Yousif Mohamed Y. Abdallah and Nouf H. Abuhadi*

#### **Abstract**

Medical imaging of the nervous system is the methodology used to achieve pictures of parts of the nervous system for therapeutic uses to recognize the ailments. Magnetic resonance imaging (MRI) is a kind of medical imaging tool that utilizes solid magnetic fields and radio waves to deliver point-by-point pictures of the inside of the body. There are large number of imaging methodologies done each week around the world. Medical imaging is developing rapidly due to developments in image acquisition tools including functional MRI and hybrid imaging modalities. This chapter abridged the role of magnetic resonance imaging (MRI) in autonomic nervous system monitoring. This chapter also summarizes the image interpretation challenges in diagnosing autonomic nervous system disorders.

**Keywords:** medical, imaging, autonomic nervous system

#### **1. Introduction**

The nervous system is divided into two parts, the central (CNS) and peripheral (PNS) part. The CNS includes the majority of the neural tissues and comprises the brain and spinal cord. PNS comprises all the structures outside the CNS and includes the special sense, spinal and cranial, and autonomic nervous system (ANS) [1–4]. The nervous system is composed mostly of the axons of sensual and motor neurons that permit between the CNS and the body. The autonomic sensory system (ANS) is divided into the peripheral sensory parts that provision the muscles and organs and influence the capacity of inner organs [5–7]. This system is considered as a regulatory framework that stimulates the action of those organs and muscles. This system manages in essence capacities, for example, the pulse, absorption, optical reaction, pee, and voluptuous stimulation [8–11]. This framework is the essential instrument responsible for the battle or flight reaction. Inside the mind, the central nerves manage this system. Autonomic capacities incorporate control of breath, heart guideline (the cardiovascular control focus), vasomotor action (the vasomotor focus), and certain reflex activities, for example, hacking, wheezing, gulping, and heaving [11–14]. This system is then subdivided into different zones that are connected additionally to ANS and sensory structures outside to the cerebrum. The central nerve over the cerebrum trunk goes as an integrator for autonomic capacities, accepting ANS administrative contribution from the limbic framework to do as such. The ANS has three subdivisions: the thoughtful sensory, the parasympathetic

sensory, and the enteric anxious system. [15–18] Some researchers exclude the enteric sensory as a component of this organization. [8] The thoughtful sensory organization frequently includes "fight or flight" framework, although the parasympathetic sensory organization regularly includes the "rest and digest" or "feed and breed" framework. Most of the time, both of these frameworks have "inverse" activities where one framework actuates a physiological reaction and the other hinders it [19–23]. A more established improvement of thoughtful and parasympathetic structures as "excitatory" and "inhibitory" was toppled because of the numerous exemptions found. In ANS, there are many constrainers and excitatory neurotransmitters, which locate among neural cells.

The non-noradrenergic system affects the gut and the lungs [24, 25]. Magnetic resonance imaging (MRI) is a medicinal imaging method utilized to frame photos of the life systems and the functional procedures of the body. MRI machines utilize solid magnetic fields and RF pulse to create pictures of the structures of the body. MRI does not use ionizing radiation like CT, PET, and other scanners. MRI is a restorative utilization of nuclear magnetic resonance (NMR) [26–28]. This technique can be utilized for NMR spectroscopy. Although the risks of conventional radiography are presently very much protected in utmost medicinal settings, an MRI examination may at present be viewed as a superior decision than a CT exam. MRI is generally utilized in emergency clinics and facilities for therapeutic determination. An MRI may produce diverse data in contrast to CT scan. There might be dangers and inconvenience related to MRI scans. In contrast to CT filters, this procedure commonly is more intense and risky. In the 1970–80s, MRI has demonstrated to be a flexible imaging method. Although MRI is utmost unmistakably utilized in analytic prescription and biological researches, it additionally might be utilized to make pictures of inorganic particles. The supported increment sought after for MRI inside wellbeing frameworks has prompted worries about cost adequacy and overdiagnosis [29–32].

#### **2. Anatomy of autonomic nervous system (ANS)**

The ANS is partitioned into the thoughtful and the parasympathetic sensory system. The thoughtful division starts in the thoracic spines and ends up in the L2–3. The parasympathetic division includes both cranial (III, IX, X) and sacral (S2–4) nerves (**Figure 1**) [33, 34].

The thoughtful sensory system consists of neural cells that appear beyond T1 and continue to level L2/3. There are a few areas whereupon preganglionic neurons can be able neurotransmitters because of their postganglionic neuron stability.

These ganglia assign the postganglionic neurons beside which innervation of goal structures pursues. Instances regarding splanchnic (instinctive) nerves are as follows:


**115**

**2.1 Sensory neurons**

*Autonomic nervous system [1, 3, 8].*

**Figure 1.**

machine neurons about the ANS (**Figure 2**) [45, 46].

*The Role of Magnetic Resonance Imaging (MRI) in Autonomic Nervous System Monitoring*

The sensory part is taken outdoors concerning necessary instinctive true neurons determined in the hem sensory dictation (PNS), of cranial real ganglia: the geniculate, petrosal, or nodose ganglia, annexed one at a time after cranial nerves. These tactile neurons are responsible of organization of the degrees of charcoal dioxide, oxygen, or grit between the blood, blood boat ounce yet the artificial business enterprise about the belly and intestine content [41–44]. The nTS gets the performance beside an adjacent chemosensory focus, the area postrema, who recognizes poisons among the blood yet the cerebrospinal melted and is necessary because synthetically instigated spewing and restrictive style repugnance (the intelligence as ensures so a life as has been harmed through sustenance in no way connection such again). These instinctive tactile data constantly then unknowingly regulate the labor regarding the

*DOI: http://dx.doi.org/10.5772/intechopen.89593*

*The Role of Magnetic Resonance Imaging (MRI) in Autonomic Nervous System Monitoring DOI: http://dx.doi.org/10.5772/intechopen.89593*

**Figure 1.** *Autonomic nervous system [1, 3, 8].*

#### **2.1 Sensory neurons**

*Autonomic Nervous System Monitoring - Heart Rate Variability*

neurotransmitters, which locate among neural cells.

**2. Anatomy of autonomic nervous system (ANS)**

neural ligature of the thoughtful band

(S2–4) nerves (**Figure 1**) [33, 34].

2.Thoracic splanchnic nerves

hypogastric plexus [35–40]

sensory, and the enteric anxious system. [15–18] Some researchers exclude the enteric sensory as a component of this organization. [8] The thoughtful sensory organization frequently includes "fight or flight" framework, although the parasympathetic sensory organization regularly includes the "rest and digest" or "feed and breed" framework. Most of the time, both of these frameworks have "inverse" activities where one framework actuates a physiological reaction and the other hinders it [19–23]. A more established improvement of thoughtful and parasympathetic structures as "excitatory" and "inhibitory" was toppled because of the numerous exemptions found. In ANS, there are many constrainers and excitatory

The non-noradrenergic system affects the gut and the lungs [24, 25]. Magnetic resonance imaging (MRI) is a medicinal imaging method utilized to frame photos of the life systems and the functional procedures of the body. MRI machines utilize solid magnetic fields and RF pulse to create pictures of the structures of the body. MRI does not use ionizing radiation like CT, PET, and other scanners. MRI is a restorative utilization of nuclear magnetic resonance (NMR) [26–28]. This technique can be utilized for NMR spectroscopy. Although the risks of conventional radiography are presently very much protected in utmost medicinal settings, an MRI examination may at present be viewed as a superior decision than a CT exam. MRI is generally utilized in emergency clinics and facilities for therapeutic determination. An MRI may produce diverse data in contrast to CT scan. There might be dangers and inconvenience related to MRI scans. In contrast to CT filters, this procedure commonly is more intense and risky. In the 1970–80s, MRI has demonstrated to be a flexible imaging method. Although MRI is utmost unmistakably utilized in analytic prescription and biological researches, it additionally might be utilized to make pictures of inorganic particles. The supported increment sought after for MRI inside wellbeing frameworks has prompted worries about cost adequacy and overdiagnosis [29–32].

The ANS is partitioned into the thoughtful and the parasympathetic sensory system. The thoughtful division starts in the thoracic spines and ends up in the L2–3. The parasympathetic division includes both cranial (III, IX, X) and sacral

The thoughtful sensory system consists of neural cells that appear beyond T1 and continue to level L2/3. There are a few areas whereupon preganglionic neurons can be able neurotransmitters because of their postganglionic neuron stability. These ganglia assign the postganglionic neurons beside which innervation of goal structures pursues. Instances regarding splanchnic (instinctive) nerves are as

1.Cervical cardiovascular nerves then thoracic instinctive nerves, which are

3.Lumbar splanchnic nerves, which are neural connection of the prevertebral

4.Sacral splanchnic nerves, which are neural concretion of the second quantity

**114**

follows:

ganglia

The sensory part is taken outdoors concerning necessary instinctive true neurons determined in the hem sensory dictation (PNS), of cranial real ganglia: the geniculate, petrosal, or nodose ganglia, annexed one at a time after cranial nerves. These tactile neurons are responsible of organization of the degrees of charcoal dioxide, oxygen, or grit between the blood, blood boat ounce yet the artificial business enterprise about the belly and intestine content [41–44]. The nTS gets the performance beside an adjacent chemosensory focus, the area postrema, who recognizes poisons among the blood yet the cerebrospinal melted and is necessary because synthetically instigated spewing and restrictive style repugnance (the intelligence as ensures so a life as has been harmed through sustenance in no way connection such again). These instinctive tactile data constantly then unknowingly regulate the labor regarding the machine neurons about the ANS (**Figure 2**) [45, 46].

**Figure 2.** *Sensory neurons [1, 3, 8].*

**117**

*The Role of Magnetic Resonance Imaging (MRI) in Autonomic Nervous System Monitoring*

Autonomic nerves travel in accordance with organs via the entire body. The true portion on the of the autonomic nerves remaining achieves the spinal piece at definitive spinal fragments. The neural signal travel from the autonomic system to the other body part through number pf the nerves that distributed throughout the

Thoughtful and parasympathetic divisions regularly work contrary to one another. Yet, this resistance is better named reciprocal in nature as opposed to hostile. The thoughtful partition regularly works in activities needing fast reactions. The thoughtful framework is regularly the "battle or flight" framework, while the other framework is frequently the "rest and summary" or "feed and breed" framework [49–51]. In any case, numerous cases of thoughtful and parasympathetic movement cannot be credited to "battle" or "rest" circumstances. For example, adjustable over out of a leaning again and placing role would contain an unsustainable decline between circulatory pressure notwithstanding a compensatory rise within the blood vessel's thoughtful tonus. Another mannequin is the steady, second-to-second tweak of the bough with the aid of thoughtful then parasympathetic impacts, so an aspect on the respiratory cycle. When all is said and done, these two frameworks ought to be viewed as for all time tweaking imperative capacities, in normally hostile design, to accomplish homeostasis. Higher living beings keep up their honesty by means of homeostasis, which depends on negative criticism guideline, which, thusly, ordinarily relies upon the autonomic anxious system [52–55]. Some run-of-the-mill activities of the thoughtful and parasympathetic sensory systems are recorded

Sudomotor or perspiring changes can likewise be highlights of autonomic brokenness, inferring changes in perspiring not related legitimately to side effects of orthostatic narrow mindedness or on the other hand presyncope [56–58].

Patients may report either expanded or on the other hand over the top perspiring or diminished perspiration yield and warmth narrow mindedness, either internationally, segmentally, or on the other hand sketchy in appropriation. Numerous patients with distal perspiration misfortune report expanded perspiration yield, which may happen as a compensatory reaction is unaffected territories, for example, the head and upper-middle, yet which is seen by the patient as unnecessary perspiring [59]. Sudomotor brokenness might be because of anomalies in focal control instruments (as in the different framework decay), or all the more generally in patients with autonomic fringe neuropathy, either as a disconnected variation from the norm of postganglionic thoughtful nerve strands just in hypohidrosis or worldwide anhidrosis, or as a component of an increasingly summed up autonomic neuropathy, either essential (immune system autonomic neuropathy) or auxiliary (amyloidosis, diabetic fringe neuropathy, or little fiber tangible neuropathy because of

*DOI: http://dx.doi.org/10.5772/intechopen.89593*

**3. Physiology of autonomic nervous system**

**4. Pathology of autonomic nervous system**

**2.2 Innervation**

beneath [55].

**4.1 Sweating abnormalities**

Sjögren's disorder) in nature [60, 61].

body (**Figure 3**) [47, 48].

**Figure 3.**

*The central and peripheral nervous system [1, 3, 8].*

*The Role of Magnetic Resonance Imaging (MRI) in Autonomic Nervous System Monitoring DOI: http://dx.doi.org/10.5772/intechopen.89593*

#### **2.2 Innervation**

*Autonomic Nervous System Monitoring - Heart Rate Variability*

**116**

**Figure 3.**

**Figure 2.**

*Sensory neurons [1, 3, 8].*

*The central and peripheral nervous system [1, 3, 8].*

Autonomic nerves travel in accordance with organs via the entire body. The true portion on the of the autonomic nerves remaining achieves the spinal piece at definitive spinal fragments. The neural signal travel from the autonomic system to the other body part through number pf the nerves that distributed throughout the body (**Figure 3**) [47, 48].

#### **3. Physiology of autonomic nervous system**

Thoughtful and parasympathetic divisions regularly work contrary to one another. Yet, this resistance is better named reciprocal in nature as opposed to hostile. The thoughtful partition regularly works in activities needing fast reactions. The thoughtful framework is regularly the "battle or flight" framework, while the other framework is frequently the "rest and summary" or "feed and breed" framework [49–51]. In any case, numerous cases of thoughtful and parasympathetic movement cannot be credited to "battle" or "rest" circumstances. For example, adjustable over out of a leaning again and placing role would contain an unsustainable decline between circulatory pressure notwithstanding a compensatory rise within the blood vessel's thoughtful tonus. Another mannequin is the steady, second-to-second tweak of the bough with the aid of thoughtful then parasympathetic impacts, so an aspect on the respiratory cycle. When all is said and done, these two frameworks ought to be viewed as for all time tweaking imperative capacities, in normally hostile design, to accomplish homeostasis. Higher living beings keep up their honesty by means of homeostasis, which depends on negative criticism guideline, which, thusly, ordinarily relies upon the autonomic anxious system [52–55]. Some run-of-the-mill activities of the thoughtful and parasympathetic sensory systems are recorded beneath [55].

#### **4. Pathology of autonomic nervous system**

#### **4.1 Sweating abnormalities**

Sudomotor or perspiring changes can likewise be highlights of autonomic brokenness, inferring changes in perspiring not related legitimately to side effects of orthostatic narrow mindedness or on the other hand presyncope [56–58]. Patients may report either expanded or on the other hand over the top perspiring or diminished perspiration yield and warmth narrow mindedness, either internationally, segmentally, or on the other hand sketchy in appropriation. Numerous patients with distal perspiration misfortune report expanded perspiration yield, which may happen as a compensatory reaction is unaffected territories, for example, the head and upper-middle, yet which is seen by the patient as unnecessary perspiring [59].

Sudomotor brokenness might be because of anomalies in focal control instruments (as in the different framework decay), or all the more generally in patients with autonomic fringe neuropathy, either as a disconnected variation from the norm of postganglionic thoughtful nerve strands just in hypohidrosis or worldwide anhidrosis, or as a component of an increasingly summed up autonomic neuropathy, either essential (immune system autonomic neuropathy) or auxiliary (amyloidosis, diabetic fringe neuropathy, or little fiber tangible neuropathy because of Sjögren's disorder) in nature [60, 61].

#### **4.2 Secretomotor symptoms**

Secretomotor indications incorporate sicca manifestations of dry eyes (xerophthalmia) and dry mouth (xerostomia). Patients do not visit the physicians for more investigations unless they becomes serious, however, with cautious addressing, they might be evoked. The brokenness of autonomic innervation might be seen in autonomic neuropathies or part of summed up autonomic disappointment, albeit even more ordinarily found previously [62–65].

#### **5. Magnetic resonance imaging (MRI)**

For MRI examination, the patient is situated inside an MRI scanner up to expectation constructions a consolidated alluring discipline around the sector in imitation of keep imaged. In utmost therapeutic applications, protons (hydrogen particles) that containing cloud particles was passed into tissues in order to create a sign that later use to make a photograph of internal structure of the body. Initially, energy of swaying magnetic field is temporarily related after the patient at the becoming reverberation recurrence. The energized hydrogen iotas beam a radio recurrence signal, which is estimated with the aid of an accepting curl. The radio sign may stay instituted to encode role data with the aid of altering the foremost pleasing subject utilizing bias loops. As those curls are rapidly became concerning or far away that redact the trademark stupid concussion on an MRI check. The difference in a number of tissues is managed by using the dimensions at which energized particles appear returned to a coherent state. Exogenous division specialists would possibly lie fond in accordance with the unaccompanied in conformity to perform the photograph clearer. [65] The actual parts of an MRI machine are precept magnet and the RF framework, which admits the NMR signal. The complete framework is restrained by using at least certain PCs. The area virtue on the magnet is estimated in teslas then preserving in thinking so just concerning the frameworks labor at 1.5 T, business frameworks are on hand someplace in the extent concerning 0.2 yet 7 T. For claustrophobic patient usually the open superconducting magnet machine is used. Recently, MRI has been shown either at ultra-low fields. The place ample sign quality is done conceivable via prepolarization (on the pray of 10 up to −100 mT) then by estimating the Larmor antecedence fields at around one hundred microteslas including very delicate superconducting quantum arrest gadgets (SQUIDs) [66]. Each art comes lower back according to its harmony administration and then exasperation by using the unrestricted unwinding approaches regarding T1 or T2. The T1 weighted picture is treasured because surveying the brain tissues, distinguishing greasy structure, describing average lung accidents and now every is pointed out in performed because

**119**

*The Role of Magnetic Resonance Imaging (MRI) in Autonomic Nervous System Monitoring*

of acquiring morphological data, simply namely because of post-differentiate imag-

In nervous system disorders, the non-ionizing radiation is used to scan and produce multi-dimension images with and without contrast media utilization. In the 1970s, Ian Robert Young and Hugh Clow had first brain images using MRI. In 1990, Seiji Ogawa who used the oxygen-depleted blood phenomena introduced functional MRI (fMRI). In the 1990s, the development and introduction of the new MRI protocols helped in the demonstration of gray and white matter areas of the nervous system. Many MRI scans later were done by using high magnetic strength

The authors are thankful to the Deanship of Scientific Research, at Majmaah

enable the neurosurgeons to perform a successful procedure (**Figure 5**).

The T2-weighted picture shows a valuable structure for identifying and recognition of the pathophysiological problems of ANS and gives useful information that

*DOI: http://dx.doi.org/10.5772/intechopen.89593*

ing (**Figure 4**) [30, 67, 68].

*MRI T2-weighted image [30, 64–66].*

**6. Conclusion**

**Figure 5.**

(3.0 up to 9.4 T).

**Acknowledgements**

**Conflict of interest**

University, for funding this research.

There are no conflicts of interest.

**Figure 4.** *MRI T1-weighted image [30, 64–66]*

*The Role of Magnetic Resonance Imaging (MRI) in Autonomic Nervous System Monitoring DOI: http://dx.doi.org/10.5772/intechopen.89593*

**Figure 5.** *MRI T2-weighted image [30, 64–66].*

of acquiring morphological data, simply namely because of post-differentiate imaging (**Figure 4**) [30, 67, 68].

The T2-weighted picture shows a valuable structure for identifying and recognition of the pathophysiological problems of ANS and gives useful information that enable the neurosurgeons to perform a successful procedure (**Figure 5**).

#### **6. Conclusion**

*Autonomic Nervous System Monitoring - Heart Rate Variability*

even more ordinarily found previously [62–65].

**5. Magnetic resonance imaging (MRI)**

Secretomotor indications incorporate sicca manifestations of dry eyes (xerophthalmia) and dry mouth (xerostomia). Patients do not visit the physicians for more investigations unless they becomes serious, however, with cautious addressing, they might be evoked. The brokenness of autonomic innervation might be seen in autonomic neuropathies or part of summed up autonomic disappointment, albeit

For MRI examination, the patient is situated inside an MRI scanner up to expectation constructions a consolidated alluring discipline around the sector in imitation of keep imaged. In utmost therapeutic applications, protons (hydrogen particles) that containing cloud particles was passed into tissues in order to create a sign that later use to make a photograph of internal structure of the body. Initially, energy of swaying magnetic field is temporarily related after the patient at the becoming reverberation recurrence. The energized hydrogen iotas beam a radio recurrence signal, which is estimated with the aid of an accepting curl. The radio sign may stay instituted to encode role data with the aid of altering the foremost pleasing subject utilizing bias loops. As those curls are rapidly became concerning or far away that redact the trademark stupid concussion on an MRI check. The difference in a number of tissues is managed by using the dimensions at which energized particles appear returned to a coherent state. Exogenous division specialists would possibly lie fond in accordance with the unaccompanied in conformity to perform the photograph clearer. [65] The actual parts of an MRI machine are precept magnet and the RF framework, which admits the NMR signal. The complete framework is restrained by using at least certain PCs. The area virtue on the magnet is estimated in teslas then preserving in thinking so just concerning the frameworks labor at 1.5 T, business frameworks are on hand someplace in the extent concerning 0.2 yet 7 T. For claustrophobic patient usually the open superconducting magnet machine is used. Recently, MRI has been shown either at ultra-low fields. The place ample sign quality is done conceivable via prepolarization (on the pray of 10 up to −100 mT) then by estimating the Larmor antecedence fields at around one hundred microteslas including very delicate superconducting quantum arrest gadgets (SQUIDs) [66]. Each art comes lower back according to its harmony administration and then exasperation by using the unrestricted unwinding approaches regarding T1 or T2. The T1 weighted picture is treasured because surveying the brain tissues, distinguishing greasy structure, describing average lung accidents and now every is pointed out in performed because

**4.2 Secretomotor symptoms**

**118**

**Figure 4.**

*MRI T1-weighted image [30, 64–66]*

In nervous system disorders, the non-ionizing radiation is used to scan and produce multi-dimension images with and without contrast media utilization. In the 1970s, Ian Robert Young and Hugh Clow had first brain images using MRI. In 1990, Seiji Ogawa who used the oxygen-depleted blood phenomena introduced functional MRI (fMRI). In the 1990s, the development and introduction of the new MRI protocols helped in the demonstration of gray and white matter areas of the nervous system. Many MRI scans later were done by using high magnetic strength (3.0 up to 9.4 T).

#### **Acknowledgements**

The authors are thankful to the Deanship of Scientific Research, at Majmaah University, for funding this research.

#### **Conflict of interest**

There are no conflicts of interest.

#### **Author details**

Yousif Mohamed Y. Abdallah1 \* and Nouf H. Abuhadi2

1 Radiological Science and Medical Imaging Department, College of Applied Medical Science, Majmaah University, Majmaah, Saudi Arabia

2 Diagnostic Radiology Department, College of Applied Medical Science, Jazan University, Jazan, Saudi Arabia

\*Address all correspondence to: y.yousif@mu.edu.sa

© 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.

**121**

*The Role of Magnetic Resonance Imaging (MRI) in Autonomic Nervous System Monitoring*

0014-2999(92)90676-U.

ISBN 978-0-7817-5940-3

[10] Costanzo LS. Physiology. Hagerstwon, MD: Lippincott

Williams & Wilkins; 2007. p. 37. ISBN

[11] Moore KL, Agur AM. Essential Clinical Anatomy. 2nd ed. Lippincott Williams & Wilkins, Inc.; 2002. p. 199.

[12] Neil A. Campbell, Jane B. Reece: Biologie. Spektrum-Verlag Heidelberg-Berlin; 2003. ISBN 3-8274-1352-4

[13] Goldstein D. Principles of Autonomic Medicine (PDF) (Free Online Version Ed.). Bethesda, Maryland: National Institute of Neurological Disorders and Stroke, National Institutes of Health, USA;

2016. ISBN 9780824704087

December 31, 2017

[14] Hadhazy A. Think Twice: How the Gut's "Second Brain" Influences Mood and Well-Being. Scientific American; 2010. Archived from the original on

[15] Zimmerman-Viehoff F, Thayer J, Koenig J, Herrmann C, Weber CS, Deter H-C. Short-term effects of espresso coffee on heart rate variability and blood pressure in habitual and non-habitual coffee consumers- a randomized crossover study. Nutritional Neuroscience. 2016;**19**(4):169-175. Retrieved February 20, 2017

[16] Bunsawat K, White DW,

Retrieved February 20, 2017

[17] Monda M, Viggiano An, Vicidomini C, Viggiano Al, Iannaccone T, Tafuri D, De Luca,

Kappus RM, Baynard T. Caffeine delays autonomic recovery following acute exercise. European Journal of Preventive Cardiology. 2015;**22**(11):1473-1479.

PMID 1350993

0-7817-7311-3

*DOI: http://dx.doi.org/10.5772/intechopen.89593*

[1] Schmidt A, Thews G. Autonomic nervous system. In: Janig W, editor. Human Physiology. 2nd ed. New York, NY: Springer-Verlag; 1989. pp. 333-370

Parasympathetic Function Archived 2012- 08-19 at the Wayback Machine - 1999, MacArthur research network, UCSF

[2] Allostatic Load Notebook:

[3] Langley JN. The Autonomic Nervous System Part 1. Cambridge:

[4] Jänig W. Integrative Action of the Autonomic Nervous System: Neurobiology of Homeostasis

[5] Abdallah Y. Improvement of sonographic appearance using HAT-TOP methods. International Journal of Science and Research (IJSR).

[6] John F. Enteric Nervous System. Scholarpedia. Archived from the original on 8 October 2017; 2007. Retrieved 8 October 2017. DOI: 10.4249/

[7] Willis WD. The autonomic nervous system and its central control. In: Berne RM, editor. Physiology. 5th ed. St. Louis, Mo: Mosby; 2004. ISBN

[8] Pocock G. Human Physiology. 3rd ed. London, United Kingdom: Oxford University Press; 2006. pp. 63-64. ISBN

(Digitally Printed Version). Cambridge: Cambridge University Press; 2008. p. 13.

W. Heffer; 1921

ISBN 978052106754-6

2015;**4**(2):2425-2430

scholarpedia.4064

978-0-19-856878-0

[9] Belvisi Maria G, David

Stretton C, Yacoub Magdi, Barnes Peter J. "Nitric oxide is the endogenous neurotransmitter of bronchodilator nerves in humans". European Journal of Pharmacology. 1992; **210**(2):221-222. DOI: 10.1016/

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#### **References**

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**120**

**Author details**

Yousif Mohamed Y. Abdallah1

University, Jazan, Saudi Arabia

\* and Nouf H. Abuhadi2

1 Radiological Science and Medical Imaging Department, College of Applied

2 Diagnostic Radiology Department, College of Applied Medical Science, Jazan

© 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,

Medical Science, Majmaah University, Majmaah, Saudi Arabia

\*Address all correspondence to: y.yousif@mu.edu.sa

provided the original work is properly cited.

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Retrieved 18 July 2011

CO;2-0. PMID: 9802467

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17075852

PMID 8188896

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Tomography. 1994;**18**(3):339-343. DOI: 10.1097/00004728-199405000-00001. PMID 8188896

*Autonomic Nervous System Monitoring - Heart Rate Variability*

impact of the first 50,000 cases with an assessment of efficacy and utility in a prospective 5000-patient study group.

(4 Suppl):A29-A43. DOI: 10.1227/01. neu.0000351279.78110.00. PMC

NIH. Diffusion MRI. Oxford University Press; 2010. pp. 730-740. DOI: 10.1093/ med/9780195369779.003.0047. ISBN

[28] Hajnal JV, De Coene B, Lewis PD, Baudouin CJ, Cowan FM, Pennock JM, et al. High signal regions in normal white matter shown by heavily

T2-weighted CSF nulled IR sequences.

Tomography. 1992;**16**(4):506-513. DOI: 10.1097/00004728-199207000-00002.

[29] Koretsky AP. Early development of arterial spin labeling to measure regional brain blood flow by

MRI. NeuroImage. 2012;**62**(2):602-607. DOI: 10.1016/j.neuroimage.2012.01.005.

PMC 4199083. PMID 22245338

LAP LAMBERT Academic

Venkatesan R, Schillinger DJ, Kido DK, Haacke EM. Small vessels in the human brain: MR venography with deoxyhemoglobin as an intrinsic contrast agent. Radiology. July 1997;**204**(1):272-277. DOI: 10.1148/ radiology.204.1.9205259. PMID: 9205259

[32] Mansfield P, Coxon R,

Glover P. Echo-planar imaging of the brain at 3.0 T: First normal volunteer results. Journal of Computer Assisted

978-3846588987

[31] Reichenbach JR,

[30] Abdallah Y. An Introduction to PACS in Radiology Service: Theory and Practice. Vol. 2. Berlin, Germany:

Publishing; 2012. pp. 140-153. ISBN

Journal of Computer Assisted

Neurosurgery. USA. 2009;**65**

2924821. PMID 19927075

9780195369779

PMID: 1629405

[27] Basser PJ. Invention and Development of Diffusion Tensor MRI (DT-MRI or DTI) at the

B. "Espresso coffee increases parasympathetic activity in young, healthy people". Nutritional Neuroscience. 2009;**12**(1):43-48. Retrieved February 20, 2017

Scientist: 588;1978

Science: 12;1987

Patent # 4809701;1987

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Medicine. 1988;**6**(2):164-174

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1461131

8095572

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[26] Filler A. Magnetic resonance neurography and diffusion tensor imaging: Origins, history, and clinical

[18] Information Reed Business. Britain's Brains Produce First NMR Scans. New

[19] Blood-flow checker. Popular

[20] Le Bihan D, Breton E. Method to Measure the Molecular Diffusion and/or Perfusion Parameters of Live Tissue. US

[21] Abdallah YM. History of medical imaging. Archives of Medicine and Health Sciences. 2017;**5**:275-278

Belliveau JW, Ackerman JL, Lauffer RB, Buxton RB, et al. Dynamic imaging with lanthanide chelates in normal brain: Contrast due to magnetic susceptibility effects. USA: Magnetic Resonance in

**122**

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[34] Abdallah Y. Increasing of edges recognition in cardiac Scintigraphy for ischemic patients. Journal of Biomedical Engineering and Medical Imaging. 2016;**2**(6):39-40

[35] Vaughan T, DelaBarre L, Snyder C, Tian J, Akgun C, Shrivastava D, et al. 9.4T human MRI: Preliminary results. USA: Magnetic Resonance in Medicine. 2006;**56**(6):1274-1282. DOI: 10.1002/ mrm.21073. PMC 4406343. PMID 17075852

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### *Edited by Theodoros Aslanidis*

Heart rate variability (HRV) is considered a reliable reflection of the many physiological factors modulating the normal rhythm of the heart. It reflects autonomic nervous system (ANS) function, and as such, it is used in numerous fields of medicine. Written by experts in the field, this book provides a comprehensive overview of HRV. The first section is dedicated to technical themes related to monitoring and the variables recorded. The second section highlights use of HRV in hypothermia. Finally, the third section covers general aspects of HRV application.

Published in London, UK © 2020 IntechOpen © senata / iStock

Autonomic Nervous System Monitoring - Heart Rate Variability

Autonomic Nervous

System Monitoring

Heart Rate Variability

*Edited by Theodoros Aslanidis*