MY-HEART PROJECT: ANALYSIS OF SLEEP AND STRESS

PROFILES FROM BIOMEDICAL SIGNAL

Sergio Cerutti, Anna M. Bianchi and Martin O. Mendez

Depertment of Bioingeneering, Polytechnic University of Milan, Piazza Leonardo da Vinci 32, Milan, Italy

Keywords: Wearable Device, Heart Rate Variability, Sleep, Autonomic Nervous System, Stress, Arousal,

Autoregressive Model, Hidden Markov Model.

Abstract: Advances in micro and nanotechnology, wireless technology, word-wide web networking, biomedical

digital signal processing, textile tissue and implantable devices, etc. have permitted the development of

alternative solutions for a better diagnosis of various pathologies, health care and prevention. These

advances allow the remote continuous monitoring of persons, whenever and wherever they are. My-Heart

FP6 EU Project integrates these components to develop a new concept of prevention and diagnosis for

cardiovascular diseases. The present paper will mainly focus on a Concept of the Project which is called

Take-Care and deals with the management, and integration of information for monitoring and personal

motivation in the health care environment & applications. In its main goal, Take-Care Concept allows the

learning in easy way about own self-body responses to different situations in the normal lifestyle: in

particular, it offers an evaluation of life quality from sleep performance and stress management based on an

integration of information between heart rate variability and respiration signals.

1 INTRODUCTION

My-Heart is a 6th Framework Programme EU

Integrated Project for fighting cardiovascular

diseases (CVD) by prevention and more objective

diagnosis based upon vital signs processing in

wearable devices. Cardiovascular diseases cause

50% of all deaths in the whole EU. It is well known

that a healthy and active life style can significantly

reduce the risk of cardiovascular diseases (primary

prevention) and limits the recurrence rate of acute

events (secondary prevention). Therefore, My-Heart

aims at creating smart electronic solutions and

appropriate services that empower users to take

more control of their own health. The project

addresses a spectrum of care, from prevention and

the adoption of a healthier lifestyle to chronic

disease management. The technological needs for

My-Heart applications range from vital sign

monitoring in daily life (ECG, respiration, skin

impedance, etc), to body-worn low-power devices

which run powerful and original detection

algorithms for health status and acute cardiac event

prediction, to low-power wireless links and server

architectures for data handling at professional sites.

Inside My-Heart project a Take-Care concept is

carried out that addresses people trying to find

support to develop a healthier lifestyle. Today more

and more people are facing problems: overweight,

inactivity, bad sleep and stress contribute to develop

cardiovascular diseases and are affecting millions of

European people.

Take-Care system provides people with a better

understanding of their overall health, motivating

them to become active in staying healthy and feeling

well. It is the ambition of Take Care to provide easy-

to-use technology and to develop solutions to

manage bad sleep, stress, inactivity and overweight.

For the sleep management solution the focus is

on analysing sleep stages and sleep fragmentation.

For stress solution the focus is on providing the user

a biofeedback tool to be used during relaxation

exercises to more objectively measure the effect of

the relaxation on the single subject.

In this context, the present paper describes the

procedures for the evaluation of sleep quality and for

stress management based on the analysis of the HRV

and respiration signals. The analysis is thought for a

device aimed to provide support to people who want

to develop a healthier lifestyle, with major focus on

cardiovascular disease prevention.

2 METHODOLOGY

Take-Care system (TCS) is integrated by different

modules. Each module is developed by taking into

account the analysis of a specific function. Sleep

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Cerutti S., M. Bianchi A. and O. Mendez M. (2008).

MY-HEART PROJECT: ANALYSIS OF SLEEP AND STRESS PROFILES FROM BIOMEDICAL SIGNAL.

In Proceedings of the First International Conference on Health Informatics, pages 273-278

 SciTePress

fragmentation, sleep staging, obstructive sleep

apnoea and stress are the functions singled out inside

TCS. Firstly, a general scheme of TCS will be

presented. Then, in subsection 2.1, the basic

description of sleep fragmentation module is

presented. Sleep staging module will be the

argument of subsection 2.2. Sleep apnoea module is

presented in subsection 2.3 and finally subsection

2.4 concerns with relaxation and stress module.

The main goal of TCS project is to improve life

quality based on sleep performance. Sleep analysis

is carried out by assessing different

electrophysiological signals, which in general

require well trained and specialized personnel for the

signal interpretation and dedicated equipment. TCS

takes advantage from peripheral signals of easy

acquisition and sufficient signal to noise ratio. TCS

is concentrated in the analysis of surface electrical

activity of the heart (electrocardiogram, ECG), since

it exhibits high sensitivity to different

physiologic and pathologic behaviours, and

respiration. Figure 1 shows a TCS general scheme.

2.1 Sleep Fragmentation Module

Arousal from sleep (AS) has been one of the most

studied sleep phenomena related to sleep

fragmentation (SF). SF is associated with several

symptoms, ranging from somnolence, excessive

daytime sleepiness, impaired learning and memory

capabilities, up to much more severe consequences

such as cardiovascular diseases. When SF is

associated with sleep-disordered breathings, such as

obstructive sleep apnoea (OSA), there is high

likelihood to develop arterial hypertension and other

cardiovascular diseases.

AS is normally scored from either the central or

occipital leads of the electroencephalogram (EEG)

during standard polysomnographic studies. An AS

consists in “an abrupt shift in EEG frequency, which

may include theta, alpha and/or frequencies

Textile Technology

Pre-Processing

ECG

Respiration

Processing Processing

Respirogram

Classification

Processing

Classification

Processing

Sleep Profile Apnea Profile

Index Sleep

Fragmentation

Stress Index

Relax level

Module

Sleep Staging

Module

Apnoea Detection

Module

Sleep

Fragmentation

Module

Stress

Figure 1: Take-Care System (TCS) scheme.

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greater than 16 Hz but not spindles” (Atlas Task

Force, 1992). AS generates a typical waveform in

the heart rate (HR), which consists in an abrupt

increment of the HR, followed few seconds later by

a decrement in the HR (Sforza et al., 2000). Based

on this knowledge, a search algorithm was

implemented to identify the cortical arousal

projection into the heart rhythm. This algorithm

searches in the heart rate the pattern illustrated in

Figure 2.

Figure 2: Arousal detection from RR intervals.

At each beat, in this case called control value,

the algorithm observes if two and four beats later

there is a decrement in the HR, this decrement must

obey a predefined percentage. If this condition

exists, the algorithm moves ahead and tries to find a

HR value higher than the control one between the

beats 7 and 14. If this condition is present, then an

autonomic arousal is found and the process restart

20 beats later.

Sleep fragmentation is based on the number of

arousal episodes during the whole night. However,

the sleep process is affected in a different way if an

arousal episode occurs during the first or last

NREM-REM sleep cycle. A simple solution, that

respects the physiologic impact of an arousal during

the sleep process, is weighting differently arousal

events depending of the time in which these occur.

A proposed index (Sleep Fragmentation Index, SFI)

is considered by splitting the total sleep time into

three parts and is computed as follows:

SFI =3 * (#A’s in the first segment) + (#A’s in

the second segment) + 0.33 * (#A’s in the thir

segment)

(1)

Where #A is the number of arousals in a specific

time of the night. This equations gives high weight if

an arousal episode occurs during the first sleep hours

and less weight if it occurs during the last sleep

hours.

2.2 Sleep Staging Module

In the modern hospital Sleep Centers, the standard

sleep evaluation procedure consists in the

acquisition and analysis of three signals:

electroencephalogram, muscular activity and

electrooculogram. These signals present specific

changes in time that characterize six different stages:

wake, sleep stage 1, sleep stage 2, sleep stage 3,

sleep stage 4 and REM. However, sleep stages 1 to 4

present similar characteristics that allow to regroup

them all together in only one sleep stage, which is

labelled NREM. With standard leads, at peripheral

level, NREM and REM sleep present specific

patterns, that with some grade of confidence, are

significantly recognized. For instance, during

NREM, heart rate is stable, eyes do not present

movements, there is muscular tone and respiration is

regular and deep. In contrast, during REM sleep,

heart rate is instable, eyes present circular

movements, there is no muscular tone and

respiration exhibits instability (Guyton, 2000).

Therefore, appropriate mathematical tools of signal

processing and pattern recognition might possibly

identify NREM and REM sleep periods on the basis

of these characteristics.

2.2.1 Processing

This module is built up by two blocks. The first

block is processing, in which extraction of the

features that characterize REM and NREM sleep is

carried out. During sleep, HR presents different

behaviours as well as a series of non-stationarities.

These characteristics in HR during sleep require

necessarily the application of special mathematical

tools. Wavelets, time-frequency and time-varying

approaches have interesting properties to obtain the

spectral features of the HR in the most diverse

conditions. TCS uses a time-varying autoregressive

model to extract important spectral features of the

HR during sleep. The selection for this approach

resides in its characteristic of real-time processing,

high time and frequency resolution and very low

computational cost.

Figure 3 shows an example where sleep stage 2,

4 and REM are analysed in the frequency domain.

The power spectrum was obtained by a time varying

autoregressive filter, which evaluates beat by beat

the frequency content of a time series.

From a large amount of possible features to

classify REM and NREM sleep, TCS uses only four

features to discriminate between them:

Control

RR value

0.95 * RR

value 2

beats

later

0.9 * RR

value 4

beats

later

1.2 * RR

value

between

7 and 14

beats

later

MY-HEART PROJECT: ANALYSIS OF SLEEP AND STRESS PROFILES FROM BIOMEDICAL SIGNAL

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 RR mean;

 Very low frequency component in RR;

 Modulus of the pole of the autoregressive

model in the high frequency component;

 Phase of the pole of the autoregressive model

in the high frequency component.

Figure 3: Time-varying spectral analysis of RR series in

REM, stage 2 and stage 4 during sleep (A, C and E), with

the respective power spectral densities (B, D and F).

2.2.2 Classification

A Hidden Markov Model classifier (HMM)

integrates the second block, classification. HMM

was selected since this classifier takes into account

the temporal dynamic of a time series. This

characteristic is very important for this study, since

the length of the NREM-REM cycles varies in time.

Figure 4 present an example where the selected

features and the sleep profile are compared with the

clinical hypnogram. The sleep stages are defined as:

0 = wake, 3 = NREM and 5 = REM. The

classification was evaluated epoch by epoch of 30

sec according to the traditional clinical use.

2.3 Sleep Apnoea Module

Sleep apnoea is one the most common sleep

pathologies. Only in the USA, this pathology affects

sleep apnoea does not present evident symptoms,

most of the time, sleep apnoea is undiagnosed. Sleep

apnoea produces consequences that range from daily

somnolence to heart failure. Sleep apnoea is divided

into three different types: obstructive, central and

mixed. Obstructive apnoea is the most common

sleep pathology and with more severe consequences

and direct influences in the cardiovascular function.

Sleep apnoea is generated by an occlusion in the

upper airways, which does not permit the air to enter

to the lungs. Generally, this occlusion is produced

by an inadequate sympathetic activation, decrement

of the upper airways radio by obesity or exposition

to agents as smog. The duration of a single

obstructive apnoea episode is between 10 and 20

seconds. During this time, oxygen saturation

decreases and respiratory efforts begin to increase in

time in order to re-open the upper airways. If oxygen

level in the blood decreases in such a way that the

respiratory efforts are not enough to restore

respiration, the central nervous system produces an

arousal, which reactivates all the systems, and the

respiration is restored. This process could occur

hundreds of times during a single night: in this case

a bad sleep quality and cardiac consequences are

evidenced, giving as results social problems,

accidents and heart attacks (Young et al., 2002).

Figure 4: Example of NREM-REM classification by Take-

Care System. From the top to the bottom: RR intervals,

very low frequency power, module and phase of the pole

in high frequency, sleep profile evaluated by Take-Care

System and clinical hypnogram. The sleep stages are

defined as: 0 = wake, 3 = NREM and 5 = REM.

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This process produces a defined brady-

tachycardia pattern in the heart rate. TCS explores

the feasibility for obtaining an alternative detector of

apnoeas using the same tools presented in the study

for sleep staging. As autoregressive models present

dedicated characteristic for evaluating features in

dynamic time series, it was implemented as feature

extractor. However, for a more correct detection of

apnoeas, some new features were explored: i.e., an

estimation of the respiratory effort was obtained

from the calculation of the area of the QRS

complexes. Correlation between both time series

was used in order to extract more robust features to

classify obstructive apnoea.

2.3.1 Processing

Also sleep apnoea module is built up from two

blocks. The processing bock helps to extract the

features that separate apnoea and non-apnoea

conditions. Again, an autoregressive model was used

to extract the features. However, each problem

requires the selection of its own features to solve it.

In this case, the set of features is formed by:

 RR mean;

 Very low frequency component in RR;

 Very low frequency component in derived

respiratory signal from ECG;

 Coherence between very low frequency

components of the two signals.

2.3.2 Classification

In this block a K-nearest neighbour classifier is used

to separate between apnoea and non-apnoea periods.

The classification is evaluated in a minute-by-

minute basis. From here it is possible to obtain an

estimate of the time that a person spends in apnoea

during the sleep time. Figure 5 shows an example of

sleep apnoea classification for 25 subjects.

Figure 5: Class separation based on minutes per night

calculated by the KNN classifier processing 4 features for

25 recordings of the testing group. Note that applying a

threshold of 50 minutes per night, apnoea and normal

classes are accordingly separated.

2.4 Stress Module

The stress concept employs a sensorised T-shirt

which allows the continuous recordings of ECG and

respiration. Previously, the subject is asked to fill in

a questionnaire which allows to detect his/her level

of stress, according to a clinical classification score

(APA, 1994). A kind of personalised initial level of

stress is hence obtained (IS). Then, according to the

instructions delivered from the computer, the subject

makes a rest-to-stand manoeuvre (from sitting to a

standing position) which indicates the degree of

responsiveness to a predominantly sympathetic

stimulation.

Then, according to the computer indications, the

subject makes some relaxation exercises, which

consist in deep regular respirations cycles, trying to

“synchronise” as much as possible cardiorespiratory

activity. On the basis of Heart Rate Variability

parameters (HRV) and respiration, as well as on

bivariate magnitudes calculated from the signals, it

is possible to measure the personalised physiological

effects of training sessions after days or weeks of

treatment and hence to re-position the subject

possibly in another location of the stress level plane

which started from IS.

It is advised that the subject could do this

exercise on a regular base (i.e. once per day) and

hence there is the possibility to monitor his/her level

of stress from the responses of his/her vital signs.

After a proper coaching it is believed that through

such exercises the subject could monitor his/her

level of stress and these objective measurements

could be important elements for helping physicians

in a better diagnosis and treatment follow-up of

stress related cardiac pathologies.

3 CONCLUSIONS

Take Care Concept has developed original

applicative tools, implemented through advanced

technological implementations (textiles,

microelectronics storing and controlling devices,

modern wireless communication protocols, etc) in

order to provide a precious instrument of prevention

of cardiovascular pathologies. The basic philosophy

is to detect from subject’s vital signs physiological

and clinical parameters even in continuous

recordings, by employing easy-to-use wearable

devices which allow comfortable home or

ambulatory applications.

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