**2.4 Sleep monitoring as a tool for health management**

Sleep loss, whether acute or chronic, poses significant risks in the performance of many ordinary tasks (e.g. driving, performing mental tasks, etc.) and has a substantial impact on social welfare. Studies have shown that people with lack of sleep constitute a major health risk for themselves and their surroundings. In light of this, the EASI (Enhancing Activity Through Sleep Improvement) project that consists of a multidisciplinary consortium is focusing on the monitoring and management of the sleep quality.

Algorithms have been developed that can automatically estimate parameters related to sleep quality of individuals such as sleep fragmentation and sleep stages. This information can be used in order to identify impaired sleep and with the use of environmental and bed variables sleep quality can be improved. Improved sleep quality will not only have positive effect on the individual's performance but also on the number of health problems related to sleep. In the commercial stage, the algorithms can be integrated in wearable devices that can provide visual feedback in relation to sleep quality and advice on actions that can improve sleep.

#### **2.4.1 Automatic detection of awakenings**

It has been presented in the literature that there exists a negative link between sleep fragmentation on daytime performance. Not only sleep duration, but also sleep continuity is an important factor in the recuperative sleep process. Sleep disturbances of only a few seconds contribute to the development of daytime sleepiness (Bonnet, 1985; Carrington & Trinder, 2008).

A popular method to monitor the number of awakenings during sleep is by using an actigraph. Actigraphs are used to detect body movements using a build-in accelerometer and give indices of awakenings. A number of studies have been presented that focus on the detection of awakenings based on activity (Lotjonen et al., 2003; Paquet et al., 2007; Sitnick et al., 2008). The use of actigraphy as a sleep-wake indicator is subject to discussion (Pollak et al., 2001; Tryon, 2004). Some studies using accelerometers have lead to wake detection between 35% and 50% (Paquet et al., 2007). An important shortcoming of these methods is their failure to detect an awakening when a person lies immobile in bed. In some extreme cases even a transition from supine to sitting position can sometimes be undetected (Sitnick et al., 2008).

During the course of the EASI project, an algorithm has been developed that is able to automatically detect every time the user is awake during the sleeping period (Bulckaert et al., 2010). Additionally, the algorithm is able to detect awakenings that are not scored as such according to the Rechschaffen & Kales (1968) criteria (i.e. awakenings that are shorter than 15s) and are referred to as 'short awakenings'. A visualisation of the algorithm output is shown in Fig. 6.

#### **2.4.2 Detection of REM sleep**

A normal sleep night consists of 5 distinct sleep stages, that occur in a structured sequence starting with light sleep with stages 1 and 2, followed by deep sleep, also called slow wave sleep with stages 3 and 4, and then followed by REM sleep. On average, light sleep occurs during 50-60% of sleep time, deep sleep during 15-20% of sleep time, REM sleep during 20-25% of sleep time and 5% or less is spent in wakefulness (Carskadon & Dement, 2000). Although REM is not the dominant part of the sleep time, most of sleep research

Sleep loss, whether acute or chronic, poses significant risks in the performance of many ordinary tasks (e.g. driving, performing mental tasks, etc.) and has a substantial impact on social welfare. Studies have shown that people with lack of sleep constitute a major health risk for themselves and their surroundings. In light of this, the EASI (Enhancing Activity Through Sleep Improvement) project that consists of a multidisciplinary consortium is

Algorithms have been developed that can automatically estimate parameters related to sleep quality of individuals such as sleep fragmentation and sleep stages. This information can be used in order to identify impaired sleep and with the use of environmental and bed variables sleep quality can be improved. Improved sleep quality will not only have positive effect on the individual's performance but also on the number of health problems related to sleep. In the commercial stage, the algorithms can be integrated in wearable devices that can provide visual feedback in relation to sleep quality and advice on actions that can improve

It has been presented in the literature that there exists a negative link between sleep fragmentation on daytime performance. Not only sleep duration, but also sleep continuity is an important factor in the recuperative sleep process. Sleep disturbances of only a few seconds contribute to the development of daytime sleepiness (Bonnet, 1985; Carrington &

A popular method to monitor the number of awakenings during sleep is by using an actigraph. Actigraphs are used to detect body movements using a build-in accelerometer and give indices of awakenings. A number of studies have been presented that focus on the detection of awakenings based on activity (Lotjonen et al., 2003; Paquet et al., 2007; Sitnick et al., 2008). The use of actigraphy as a sleep-wake indicator is subject to discussion (Pollak et al., 2001; Tryon, 2004). Some studies using accelerometers have lead to wake detection between 35% and 50% (Paquet et al., 2007). An important shortcoming of these methods is their failure to detect an awakening when a person lies immobile in bed. In some extreme cases even a transition from supine to sitting position can

During the course of the EASI project, an algorithm has been developed that is able to automatically detect every time the user is awake during the sleeping period (Bulckaert et al., 2010). Additionally, the algorithm is able to detect awakenings that are not scored as such according to the Rechschaffen & Kales (1968) criteria (i.e. awakenings that are shorter than 15s) and are referred to as 'short awakenings'. A visualisation of the algorithm output

A normal sleep night consists of 5 distinct sleep stages, that occur in a structured sequence starting with light sleep with stages 1 and 2, followed by deep sleep, also called slow wave sleep with stages 3 and 4, and then followed by REM sleep. On average, light sleep occurs during 50-60% of sleep time, deep sleep during 15-20% of sleep time, REM sleep during 20-25% of sleep time and 5% or less is spent in wakefulness (Carskadon & Dement, 2000). Although REM is not the dominant part of the sleep time, most of sleep research

**2.4 Sleep monitoring as a tool for health management** 

**2.4.1 Automatic detection of awakenings** 

sometimes be undetected (Sitnick et al., 2008).

sleep.

Trinder, 2008).

is shown in Fig. 6.

**2.4.2 Detection of REM sleep** 

focusing on the monitoring and management of the sleep quality.

focuses on REM sleep because this state resembles most to wakefulness and is being linked to dreaming and memory consolidation (Karni et al., 1994; Tilley & Empson, 1978; Takahara et al., 2008). In the same direction, during the course of the EASI project, an algorithm was developed that is automatically detecting periods of REM sleep. Additionally, the algorithm is contributing to the discussion of whether dreams occur only during REM sleep or not, by exploiting the concept of Additional Heart Rate and its link to emotions (Myrtek, 2004) during sleep. An example of the algorithm output is presented in Fig. 7.

Fig. 6. Manual scoring of the sleep stages and the output of the developed algorithm for awakening detection

Fig. 7. Example of the algorithm output for the REM detection algorithm

Non-Invasive Methods for Monitoring Individual Bioresponses in Relation to Health Management 155

The Royal Meteorological Institute of Belgium (RMI, Ukkel) which is located at the centre of Belgium, provided daily data on air temperature (°C) and precipitation (mm) from 1996 to 2008. To be capable of catching the dynamics of the NE cases, we calculated monthly averages precipitation (mm) and average temperatures (°C) based on the daily reported

The Tree Seed Centre of the Ministry of the Walloon Region supplied categories of seed production of beech and native oak species (*Quercus robur*, *Quercus petraea*). Tree seed production for each tree species is divided into four categories: "very good years" (the species is fruiting throughout the Walloon territory and practically all trees are bearing seed in high quantities), "good years" (the species is fruiting throughout the territory, but the trees are bearing much less seed and some trees do not fruit), "moderate years" (there is a reduced number of trees bearing seeds and sometimes only located in a portion of the

The mehanistic population model used in this study was based on the equations proposed by Sauvage et al. (2007). Their model consists of two sub models. The first sub model (Bank vole's population model) describes the bank vole's demography and infection and the second sub model (Human population sub model) describes the access of human to the forest and the dynamics of the subsequent human infections. In the model the bank voles contaminated the environment that spread the virus into the human population. For a more

By combining the mechanistic model of Sauvage et al. (2007) and the transfer function model, the incidence of NE cases per year could be modelled accurately. The modelling

<sup>1996</sup> <sup>1997</sup> <sup>1998</sup> <sup>1999</sup> <sup>2000</sup> <sup>2001</sup> <sup>2002</sup> <sup>2003</sup> <sup>0</sup>

temperature, precipitation and estimated carrying capacity) versus measured (-•--) incidence

Fig. 8. The result (-------) of the data-based MISO model with 3 inputs (average monthly

of NE in Belgium from January 1996 till January 2003 ( <sup>2</sup> RT of 0.68)

Year

territory) and "low years" (years without fructification in significant quantities).

detailed description of the model we refer to the work of Sauvage et al. (2007).

results for the period 1996 – 2003 are shown in Fig. 8.

5

10

15

Number of NE cases

20

25

30

35

climate data of Ukkel.

**2.5.2 Modelling of NE outbreaks** 

The algorithm was tested on 11 subjects (mean age 23+3 years) and resulted in an average true positive classification of 75.8% and an average false positive classification rate of 21.1%.

#### **2.5 Monitoring and predicting hanta viruses and Lyme infectious disease outbreaks by integrating remote sensing and climatic data with biophysical models**

In the industrialized world with an intensive service sector, professional activities in agriculture, forestry and natural resources industry has been declining for decades. As such fewer professionals directly come into contact with the land. On the other hand, since people now spend more time for leisure, more outdoor recreational activities have been observed. Hiking, outdoor sports, picnicking, hunting etc has now enlarged the human exposure to the land. This increase has led to more contacts of humans with environmental related diseases such as Lyme Borreliosis (LB) and Nephropathia Epidemica (NE).

LB is a tick borne disease caused by the species of bacteria belonging to the genus Borrelia, whereas in Western Europe NE is caused by Pumuula viruses. Although different of nature, they share a common host, the bank vole. This small rodent is reservoir for both the bacteria as the viruses. For NE, the bank vole is also the vector species, whereas for LB ticks are the vector.

Since the abundance of ticks and bank voles depends on habitat characteristics for food supply and shelter among others, remote sensing techniques can be used to monitor vegetative systems that create habitats for these species. By integrating earth observation data from MODIS, LANDSAT, NOAA/AVHRR sensors with meteorological data of precipitation, temperature, relative humidity and estimates of bank vole and tick populations in data driven biophysical models, an expert based system is being developed to monitor and predict infection disease outbreaks of LB and NE for Belgium.

Hantaviruses are rodent or insectivore borne viruses and some of them are recognized as causes of human hemorrhagic fever with renal syndrome (HFRS). In western and central Europe and in western Russia one of the most important Hantavirus is *Puumala virus* (PUUV), which is transmitted to humans by infected red bank voles (*Myodes glareolus*). PUUV causes a general mild form of hemorrhagic fever with renal syndrome called nephropathia epidemica (NE) (Clement et al., 2006).

In general, only 13% of all PUUV infections are serodiagnosed, the other being interpreted as 'a bad flu' (Brummer-Korvenkontio et al., 1999; Clement et al., 2007) or remaining unnoticed. HFRS, including NE, is now the most underestimated cause of infectious acute renal failure worldwide, so the officially registered NE is only the top of the iceberg.

Because of the dynamic nature of the bank vole's population, a dynamic systems approach might also be the basis for the development of monitor applications. In this research we combine a data-based modelling approach with a mechanistic model (Sauvage et al., 2007) that allows modelling the dynamics of the NE cases with a compact model structure that takes into account climatological data. More specifically, we aimed at building a multiple– input, single-output (MISO) transfer function to model the incidence of NE cases in Belgium from 1996 till 2003 as a function of: measured average monthly air temperature (°C), monthly precipitation (mm) and carrying capacity (vole ha-1) estimated from the mechanistic model described by Sauvage et al. (2007).

#### **2.5.1 Available data**

The Scientific Institute of Public Health (IPH, Brussels) in Belgium provided Nephropathia epidemica (NE) data. In Belgium, the weekly numbers of NE case per postal code (a spatial entity smaller than the municipality) were available from 1994 until 2008.

The Royal Meteorological Institute of Belgium (RMI, Ukkel) which is located at the centre of Belgium, provided daily data on air temperature (°C) and precipitation (mm) from 1996 to 2008. To be capable of catching the dynamics of the NE cases, we calculated monthly averages precipitation (mm) and average temperatures (°C) based on the daily reported climate data of Ukkel.

The Tree Seed Centre of the Ministry of the Walloon Region supplied categories of seed production of beech and native oak species (*Quercus robur*, *Quercus petraea*). Tree seed production for each tree species is divided into four categories: "very good years" (the species is fruiting throughout the Walloon territory and practically all trees are bearing seed in high quantities), "good years" (the species is fruiting throughout the territory, but the trees are bearing much less seed and some trees do not fruit), "moderate years" (there is a reduced number of trees bearing seeds and sometimes only located in a portion of the territory) and "low years" (years without fructification in significant quantities).

#### **2.5.2 Modelling of NE outbreaks**

154 Health Management – Different Approaches and Solutions

The algorithm was tested on 11 subjects (mean age 23+3 years) and resulted in an average true positive classification of 75.8% and an average false positive classification rate of 21.1%.

**2.5 Monitoring and predicting hanta viruses and Lyme infectious disease outbreaks** 

In the industrialized world with an intensive service sector, professional activities in agriculture, forestry and natural resources industry has been declining for decades. As such fewer professionals directly come into contact with the land. On the other hand, since people now spend more time for leisure, more outdoor recreational activities have been observed. Hiking, outdoor sports, picnicking, hunting etc has now enlarged the human exposure to the land. This increase has led to more contacts of humans with environmental

LB is a tick borne disease caused by the species of bacteria belonging to the genus Borrelia, whereas in Western Europe NE is caused by Pumuula viruses. Although different of nature, they share a common host, the bank vole. This small rodent is reservoir for both the bacteria as the viruses. For NE, the bank vole is also the vector species, whereas for LB ticks are the

Since the abundance of ticks and bank voles depends on habitat characteristics for food supply and shelter among others, remote sensing techniques can be used to monitor vegetative systems that create habitats for these species. By integrating earth observation data from MODIS, LANDSAT, NOAA/AVHRR sensors with meteorological data of precipitation, temperature, relative humidity and estimates of bank vole and tick populations in data driven biophysical models, an expert based system is being developed

Hantaviruses are rodent or insectivore borne viruses and some of them are recognized as causes of human hemorrhagic fever with renal syndrome (HFRS). In western and central Europe and in western Russia one of the most important Hantavirus is *Puumala virus* (PUUV), which is transmitted to humans by infected red bank voles (*Myodes glareolus*). PUUV causes a general mild form of hemorrhagic fever with renal syndrome called

In general, only 13% of all PUUV infections are serodiagnosed, the other being interpreted as 'a bad flu' (Brummer-Korvenkontio et al., 1999; Clement et al., 2007) or remaining unnoticed. HFRS, including NE, is now the most underestimated cause of infectious acute

Because of the dynamic nature of the bank vole's population, a dynamic systems approach might also be the basis for the development of monitor applications. In this research we combine a data-based modelling approach with a mechanistic model (Sauvage et al., 2007) that allows modelling the dynamics of the NE cases with a compact model structure that takes into account climatological data. More specifically, we aimed at building a multiple– input, single-output (MISO) transfer function to model the incidence of NE cases in Belgium from 1996 till 2003 as a function of: measured average monthly air temperature (°C), monthly precipitation (mm) and carrying capacity (vole ha-1) estimated from the

The Scientific Institute of Public Health (IPH, Brussels) in Belgium provided Nephropathia epidemica (NE) data. In Belgium, the weekly numbers of NE case per postal code (a spatial

entity smaller than the municipality) were available from 1994 until 2008.

renal failure worldwide, so the officially registered NE is only the top of the iceberg.

**by integrating remote sensing and climatic data with biophysical models** 

related diseases such as Lyme Borreliosis (LB) and Nephropathia Epidemica (NE).

to monitor and predict infection disease outbreaks of LB and NE for Belgium.

nephropathia epidemica (NE) (Clement et al., 2006).

mechanistic model described by Sauvage et al. (2007).

**2.5.1 Available data** 

vector.

The mehanistic population model used in this study was based on the equations proposed by Sauvage et al. (2007). Their model consists of two sub models. The first sub model (Bank vole's population model) describes the bank vole's demography and infection and the second sub model (Human population sub model) describes the access of human to the forest and the dynamics of the subsequent human infections. In the model the bank voles contaminated the environment that spread the virus into the human population. For a more detailed description of the model we refer to the work of Sauvage et al. (2007).

By combining the mechanistic model of Sauvage et al. (2007) and the transfer function model, the incidence of NE cases per year could be modelled accurately. The modelling results for the period 1996 – 2003 are shown in Fig. 8.

Fig. 8. The result (-------) of the data-based MISO model with 3 inputs (average monthly temperature, precipitation and estimated carrying capacity) versus measured (-•--) incidence of NE in Belgium from January 1996 till January 2003 ( <sup>2</sup> RT of 0.68)

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In future work the modelling approach may be improved by integration of estimated bank vole population dynamics measured in the field. This could give us the possibility to quantify the carrying capacity based on the field measurements instead of epidemiological models. More details on this application can be found in the work of Amirpour Haredasht et al. (2011).
