FACULDADE DE ENGENHARIA DA UNIVERSIDADE DO PORTO

Sensor based sleep patterns andnocturnal activity analysis

Pedro Ramiro Guimarães Ribeiro

PREPARATION FOR THE MSC DISSERTATION

Master in Electrical and Computers Engineering

Supervisor: Miguel Velhote Correia, PhD

Co-Supervisor: Vânia Guimarães, MSc

July 17, 2014

Contents

1 Introduction 11.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Document Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Background and State of the art 32.1 The phenomenology of sleep . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.1.1 Movement, heart rate and respiration during sleep . . . . . . . . . . . . . 42.1.2 Body Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2 Sleep disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.3 Monitoring modalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.3.1 Polysomnography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.3.2 EEG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.3.3 Actigraphy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.3.4 The photoplethysmogram and oxygen saturation . . . . . . . . . . . . . 102.3.5 Cardiovascular measures . . . . . . . . . . . . . . . . . . . . . . . . . . 112.3.6 Respiration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.3.7 Audio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.3.8 Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.4 Smartphone based systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3 Project Specification and Work Plan 153.1 Proposed Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.2 System overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.2.1 Sensor Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.2.2 Bluetooth Communication . . . . . . . . . . . . . . . . . . . . . . . . . 163.2.3 Smartphone Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.3 Development Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.3.1 Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.3.2 Android Operation System Overview . . . . . . . . . . . . . . . . . . . 173.3.3 WEKA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.4 Work Plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183.4.1 Task Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4 Conclusion 21

i

ii CONTENTS

List of Figures

2.1 An exemplary plot showing the sleep stages for a single night in a normal youngadult.[1] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2 Young adults plot of core body temperature, plasma melatonin, wake propensity,and the responsiveness to light. [2] . . . . . . . . . . . . . . . . . . . . . . . . . 5

3.1 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

iii

iv LIST OF FIGURES

List of Tables

2.1 The different effect of each sleep stage . . . . . . . . . . . . . . . . . . . . . . . 6

v

vi LIST OF TABLES

Abbreviations and Symbols

BCG BallistocardiogramBMI Body mass indexBP Blood PressureECG ElectrocardiogramEEG ElectroencephalogramEMG ElectromyographicEOG ElectroculogramHRV Heart rate varietyNREM Non Rapid eye movementOS Operating SystemOSA Obstructive sleep apneaPSG PolysomnographyREM Rapid eye movementUI User Interface

vii

Chapter 1

Introduction

This document was developed with the purpose of acquire and review literature relevant for the

development of the Master’s Dissertation and analyse the state of the art of the topic.

This chapter presents the motivation, context and the objectives to be achieved in the disserta-

tion.

1.1 Motivation

Everyone has already experienced trouble sleeping at one time or another. This is normal and

usually temporary, frequently due to stress or other outside factors. When this becomes a regular

occurrence, then probably the person is facing any kind of sleep disorder.

Complaints of sleep difficulty are more common among older persons. A reduced sleep quality

due to sleep deprivation or fragmentation may cause reduced vigilance, attention and information

processing ability, which ultimately may result in trips and falls. In general, the lack of sleep

quality has negative impacts on energy, balance and health.

Measurement and tracking of sleep length and sleep quality, as well as measuring different

events (apneas, periodic limb movements, movement arousals, ...) during sleep over long time

periods can help improve one’s sleep and well-being.

Currently, the gold standard in terms of sleep disorder diagnosis is overnight polysomnogra-

phy (PSG), but the cost of monitoring a person overnight, the scarcity of beds available and the

uncertainty of whether the results are representative of a normal nights’ sleep means that a move

to home diagnostics is likely to be advantageous.

Other means of diagnosis involves monitorization of the patterns of the patient during his

circadian cycle, this monitorization can be done using sleep and dream diaries, portable biosignal

recorders and, frequently, actigraphy.

In an increasingly mobile society, a mobile sleep diary using wireless and mobile networks

can also make the collection of subjective sleep information for use in sleep disorders detection,

efficient, accessible and affordable. This information can be collected from patients and then

transferred directly to a distant clinician.

1

2 Introduction

1.2 Objectives

The aim of this work is to propose a portable and easy-to-use system that permits the realization

of the sleep diary directly at patient’s home.

The system must be able to analyse sleep patterns and nocturnal activity autonomously based

on information acquired from sensors attached to a dedicated position of the body. Sleep efficiency

must be considered towards an analysis of risk, in order to predict the occurrence of falls.The

system must also be able to detect every time a person gets up at night.

External sensors will be connected to a smartphone running an Android application and will

continually record the person’s vital signs. The history of each night will be recorded in order to

provide a longitudinal analysis of data and detect fluctuations in normal sleep-wake patterns.

1.3 Document Structure

Apart from this chapter, this document has the following chapters:

• Chapter 2: Background and the State of the Art— Presents the background overview

and some related works.A contextualization is given and afterwards the theory behind the

theme is explored.

• Chapter 3: Work Plan— This chapter provides an approach to the project development

since its beginning until its conclusion including all the methods developed and procedures

involved.

• Chapter 4: Conclusion— Reveals some of the insights acquired in this document.

Chapter 2

Background and State of the art

In this Chapter there is going to be a study of the phenomenology of sleep and explaining the

different sleep stages effects(2.1), as well as listed and briefly characterized the main sleep disor-

ders(2.2).The aim is to help the reader understand the background involved in the development of

this dissertation’s project.

It is also presented the existing solutions to detect the different sleep stages and/or some of the

sleep disorders discriminated

2.1 The phenomenology of sleep

"Sleep, resting state in which an individual becomes relatively quiescent and unaware

of the environment. During sleep, which is in part a period of rest and relaxation,

most physiological functions such as body temperature, blood pressure, and rate of

breathing and heartbeat decrease. However, sleep is also a time of repair and growth,

and some tissues, e.g., epithelium, proliferate more rapidly during sleep." [3]

There are many definition,like this on, presented by different authors and despite sleep isn’t

completely well known by scientists, it is already clear that is divided into two broad types, based

on EEG measurements: NREM and REM[4, 5].

In REM sleep, the main feature is muscle paralysis which blocks the neuronal connection

between the brain and most muscles. Muscle paralysis prevents the awake-like brain activity from

causing movement of the body during sleep.It is also during this stage that the brain is more

active and dreaming is more frequent and vivid, although dreaming does occur also during NREM

sleep.[6]

In 1968, Rechtschaffen and Kales based on EEG changes, divided NREM sleep into four fur-

ther stages: N1,N2,N3,N4.[5] In 2007 was published, the AASM Manual for the Scoring of Sleep

and Associated Events resulting in some changes, with the most significant being the combin-

ing of stages N3 and N4 into one stage N3.The stage N1 represents the drowsy state between

wake-fullness and sleep, and the depth of sleep is progressively increased in stages N2 and N3.[4]

3

4 Background and State of the art

Figure 2.1: An exemplary plot showing the sleep stages for a single night in a normal youngadult.[1]

As shown in Figure 2.1,in briefest summary, the normal human adult enters sleep through

NREM sleep, REM sleep does not occur until 80 minutes or longer thereafter, and NREM sleep

and REM sleep alternate through the night, with an approximately 90-minute cycle with approx-

imately 4–6 cycles during the course of a normal 6–8 hour sleep period.However, these timings

change depending on the length of time asleep, age, medication, physical health and mental health.

In the first third of the night, the cycles contain relatively more of the deepest N3 sleep, whereas

REM sleep dominates in the last third. [4, 7, 1]

2.1.1 Movement, heart rate and respiration during sleep

The Table 2.1 shows a summary of how the effects of the different stages can be seen in heart

rate, respiration and movement activity. The middle column shows how sleep stages affect move-

ment, heart rate, respiration and the electrophysiological features of sleep stages, according to the

American Association of Sleep Medicine (AASM) standard [4], are given in the right column.

In summary, the level of movement activity is much smaller in sleep than in wakefulness, al-

though there is somewhat more movement in phasic REM periods than in the rest of sleep.Marked

changes can be seen in heart rate and respiration across different sleep stages. These changes

are caused by various physiological mechanisms. For example, the autonomic coordination be-

tween heart rate and respiration is strong in NREM sleep (heart rate varies steadily in the phase of

respiration) and weak in REM sleep (heart rate is more erratic).

2.1.2 Body Temperature

In humans, the circadian rhythm for the release of melatonin from the pineal gland is closely

synchronized with the habitual hours of sleep.At night, the body responds to the loss of daylight by

producing melatonin, a hormone that makes us sleepy. During the day, sunlight triggers the brain

2.2 Sleep disorders 5

to inhibit melatonin production so we feel awake and alert. The body temperature is synchronized

with the melatonin secretion decreasing during sleep stages as shown in figure 2.2.

Figure 2.2: Young adults plot of core body temperature, plasma melatonin, wake propensity, andthe responsiveness to light. [2]

2.2 Sleep disorders

Sleep disorder can cause the sleep to be disturbed that could lead to the inability of fall asleep, go

back to sleep or frequent waking up during the night. Consequently can make a person feel tired,

6 Background and State of the art

Table 2.1: The different effect of each sleep stage

Sleep Stages Effects Features

WakeMuch MovementStable respiration

EEG: Alpha activity (8-13 Hz) for >= 50% of the epoch.

N1Little movement.Decreased HRV.Instability in respiration amplitude.

EEG: Alpha activity for <50 % of the epoch.Low-voltage mixed-frequency activity. Vertex sharp waves.EOG: Slow eye movements.

N2

Little movement.Decreased HRV.Stable respiration.Body temperature decreases

EEG: Slow-wave activity (0.5-2 Hz) for <20% of the epoch.Sleep spindles or K-complexes.

N3Little movement.Decreased HRV.Very stable respiration.

EEG: Slow-wave activity for >= 20% of the epoch.

REM

Movements during phase REM.Increased HRV and blood pressureUnstable respiration.Fluctuations in body temperature

EEG: Low-voltage mixed-frequency activity.Saw-tooth waves (2-6 Hz).EMG: Low activity.EOG: Rapid eye movements.

fatigued, and irritable, making it difficult to concentrate during the day and may lead to motor

vehicle accidents, cardiovascular and endocrine disorders, or heightened pain perception.[8]

It is possible to divide and classify sleep disorders in seven categories according to the inter-

national classification of sleep disorders [9]:

• Insomnias: Difficulty initiating or maintaining sleep, early awakening or poor sleep quality

characterized this category.

It frequently coexists with medical, psychiatric, sleep, or neurological disorders and is char-

acterised by a reduction of total sleep time and latency for REM sleep, an increase in

spontaneous micro-arousals, a reduction of slow-wave sleep and an increase in rapid eye

movements .[10] The prevalence of insomnia increases with age. In one study of elderly in-

dividuals, 57 percent had complaints consistent with insomnia and only 12 percent reported

normal sleep. [11]

• Sleep-related breathing disorders: This is a group of conditions that may be associated

with abnormal respiration during sleep, and includes chronic snoring, upper airway resis-

tance syndrome, obstructive sleep apnoea and obesity hypoventilation syndrome. The most

common is the sleep obstructive sleep apnoea which is characterized by obstructive apneas

and hypopneas caused by repetitive collapse of the upper airway during sleep.The symptoms

are excessive daytime sleepiness, snoring, and choking or gasping during sleep.[12]

• Hypersomnias of central origin not due to a circadian rhythm sleep disorder, sleeprelated breathing disorder or other cause of disturbed nocturnal sleep: This category

includes those disorders in which the primary complaint is daytime sleepiness that is not

due to disturbed sleep or misaligned circadian rhythms.

2.3 Monitoring modalities 7

The major diagnosis within this category are narcolepsy, which is characterized by excessive

daytime sleepiness and abnormal REM sleep and idiopathic hypersomnia which is a sleep

disorder that is characterized by chronic excessive daytime sleepiness and often difficulty

waking up from nocturnal sleep or daytime naps.

• Circadian rhythm sleep disorders: These are disruptions of the circadian time-keeping

system that regulates the (approximately) 24h cycle of biological processes. (The circadian

pacemaker in humans is located mainly in the suprachiasmatic nucleus, which is a group of

cells located in the hypothalamus.) Circadian rhythms affect sleep and wake cycles, cortisol

release, body temperature, melatonin levels, and other physiologic variables and can be

(non-pathologically) disturbed by shift work, time zone changes (jet-lag), medications and

changes in routine.

• Parasomnias: Disorders that intrude into the sleep process and are manifestations of central

nervous system activation producing nonvolitional motor, emotional, or autonomic activity.

Most parasomnias are associated with a specific type of sleep (rapid eye movement [REM]

or non-rapid eye movement [NREM] sleep).[9]

Parasomnias usually associated with REM sleep include nightmares and sleep paralysis

and the ones associated with NREM sleep are night terrors, enuresis nocturnal, bruxism,

sleepwalking and confusional arousals.[9]

• Sleep-related movement disorders: Sleep related movement disorders are characterized

by simple, stereotypic movements that disturb sleep. Movements that occur during sleep

but do not adversely affect sleep or daytime function are not considered a sleep related

movement disorder. Classic sleep related movement disorders include restless legs syn-

drome, periodic limb movement disorder, sleep related leg cramps, sleep related bruxism

(teeth grinding), and sleep related rhythmic movement disorder. The most common is the

Restless Leg Syndrome which is a neurological disorder and causes an irresistible urge to

move the legs to relieve an uncomfortable sensation deep within the legs during non-REM

sleep.

[12]

• Other sleep disorders: Like alcohol abuse-related or psychiatric disorders.

2.3 Monitoring modalities

Traditional modality and the gold standard method are PSG systems, an overnight physiological

sign monitoring in sleep clinics. Non-traditional modalities, such as audio, actigraphy, video or

temperature, are receiving increasing interest due to their potential utility for reduced PSG systems

and home sleep monitors.

8 Background and State of the art

2.3.1 Polysomnography

In this technique, multiple physiologic parameters are measured while the patient sleeps in a labo-

ratory. Considering the diversity of the PSG systems available, the American Association of Sleep

Medicine (AASM) published a revision for those systems, classifying in two difference levels:

level one for the standard PSG and level 2 for portable PSG [4].For a sleep study both levels must

accommodate the minimum of seven channels[4]:

• EEG: To determine arousals from sleep;

• EOG: To detect REM sleep;

• EMG submentonian: To look for limb movements that cause arousals;

• ECG

• Chest Wall monitors: To document respiratory movements;

• Thermistor and/or nasal cannula: To nasal and oral airflow measurements;

• Oximetry: To measure oxyhemoglobin saturation.

In terms of PSG home sleep testing, there are some solution in the market. Somté PSG [13] im-

plements a fully PSG home sleep testing. It has twenty five available channels and a software for

data analysis.

Although offer a complete sleep study, such modality is not comfortable and is invasive for

person in study as well as expensive, time-consuming and complex process.

2.3.2 EEG

During sleep, the human brain goes through several psychological and physiological states that

are relatively stable. Those changes in sleep patterns and brain signals can be captured in EEG

recordings combined with EOG(to identify eye movements) and EMG(to identify the drop in

muscle tone seen during REM sleep)

Several groups have tried to validate commercial devices in terms of matching sleep stages.

Berthomier et al. assessed a single-channel EEG device(ASEEGA, Physip) by scoring sleep

in 15 healthy volunteers and reported an accuracy for sleep stage classification of 96.0% [14].

Schweitzer et al. evaluated BioSomnia in 36 subjects, and concluded that is possible to detect

sleep disruption in the case of light-to-moderate sleep fragmentation.However it seems that the

cyclicity of sleep cannot be determined.[15]. BioSomnia is a single EEG channel sleep system

using neural network algorithms to automatic sleep analysis.

The Intelclinic proposed NeuroOn an headband that uses EEG, EOG, and EMG readings to

monitor sleep quality. Using low-energy Bluetooth it connects to your phone and will vibrate and

2.3 Monitoring modalities 9

light when it’s time to wake up. It fits snugly on the head like a sleep mask and keeps light out

completely. [16]

In the patent [17], is proposed combination of a sensor matrix comprising a plurality of EEG

electrodes and a pressure sensors configured to receive pressure signals indicative of the placement

of the head of the person on the sensor matrix. This set of sensors are placed in a pillow and the

objective is measure the sleep stages and quality.

2.3.3 Actigraphy

Actigraphy also called accelerometry is the measurement of activity with the use of a small,

wristwatch-sized device. Those devices are constantly evolving and may include a tri-axial ac-

celerometer, sensors for light sensing in different spectral bands, body temperature and humidity

sensors and the capability to collect user-provided information, such as subjective mood scores.[18]

It is an inexpensive, non-invasive and easy-to-use modality, often used to assess sleep-wake

cycles, or circadian rhythms, over an extended period of time based on the principle that there is

reduced movement during sleep and increased movement during wake.[19]

From the recent revision in [18] and the recommendation in American Academy Sleep Medicine

[4, 20] is possible to conclude:

• When compared with PSG, the actigraphy is a reliable measure to detecting sleep in normal

healthy adult but less reliable when sleep become more perturbed.

• When compared with sleep diary, studying the circadian rhythms, it offers equal results

in terms of sleep time and total sleep time, but is much reliable in terms of sleep latency,

number of naps and duration of arousals. However the use of both actigraphy and sleep

diary combine provides additional information to remove artefacts.

• The measure of the human activity on the wrist presents a robust circadian pattern. The

circadian period of sleep - wake defined by actigraph predicts correctly the one defined

from PSG when both are measure simultaneously.

• Although not be indicated for the routine diagnosis, assessment of severity, or management

of any of the sleep disorders, actigraphy is useful in the assessment of specific aspects of the

following disorders:

Insomnia - Assessment of sleep variability, measurement of treatment effects, and detection

of sleep phase alterations in insomnia secondary to circadian rhythm disturbance.

Restless legs syndrome/periodic limb movement disorder Assessment of treatment ef-

fects.

Circadian rhythm perturbation Since the circadian phase of the pulse activity measure-

ment appears to be synchronise with the phase of melatonin secretion, the actigraph can

then detect changes in sleep when it is tried in a unfavourable phase of the circadian cycle.

• Actigraphy is useful to clarify the effects of pharmacological treatments, phototherapy, as

well as prediction evaluation in the treatments adopted.

10 Background and State of the art

• Because of the limitations of actigraphy like sleep overestimation and the masking effect, it

is recommended to use complementary assessment methods (objective and subjective)like,

for instance, in [21] is proposed actigraphy, oximetry to detect OSA.

2.3.3.1 Body position

Persons position can be recorded overnight and used as an adjunct to other signals for diagno-

sis.Body position can be derived from the accelerometer sensor together with a magnetometer or

using gyroscope.Summarily, the accelerometer is used to describe the physical motion and the

force of gravityis used to identify the position. The mapping between sensor orientation and the

body position then depends on where the sensor is worn.[18] Many home testing system such as

SOMNOwatch, Motionlogger, ActiGraph, Actiwatch, have a body position sensor incorporated

and others devices have recently emerged, not intended for diagnosis but to be sold directly to

customers for self-help use. Like Fitbit [22], Jawbone UP [23] and SleepTracker [24], and oth-

ers, that are based on wrist actigraph measurement and have a web application for viewing the

measurements and a smartphone interface.

2.3.3.2 Sleep-wake segmentation and circadian rhythm analysis

Actigraphy has been used in the estimation of the shape and characterization of circadian cycles

and in the estimation of the sleep and wakefulness states but there’s still some doubts about the

results obtained.

Domingues et al., in [25] and [26] used the actigraphy on dominant wrist to estimate the

sleep/wakefulness using different scoring algorithms and it was concluded that the actigraphy by

it self is not sufficient to achieve good results needing another physiological data.

2.3.4 The photoplethysmogram and oxygen saturation

Non-invasive approaches have been proposed for BP monitoring in sleep studies, since surrogate

measures of BP can be obtained from ECG and PPG signals.

The main use of PPG in sleep studies is the measurement of SpO2, either for sleep apnoea alarm

systems or for OSA diagnosis.

The Portapres and Finometer systems (Finapres Medical Systems BV, Holland), and the Task

Force Monitor system (CNSystems Medizintechnik, GmbH) are the commercial equipments ex-

isting. [27]

WatchPAT [28]uses SpO2 (oxyhemoglobin saturation by pulse oximetry) signal and PAT (pe-

ripheral arterial tone) signal to score the sleep states. It uses two probes for data sensing collection,

so the comfort of the user’s sleep will be affected.

2.3 Monitoring modalities 11

2.3.5 Cardiovascular measures

HR reflects the autonomic nervous system, it is highly synchronized with the sympathetic acti-

vation during transition from non-REM sleep to wakefulness. When the transition occurred the

heart rate abruptly increased clearly distinguished with other changes.[29] Episodes of OSA are

accompanied by a characteristic HR pattern consisting of bradycardia during apnoea followed by

abrupt tachycardia on its cessation, which can be used to detect OSA.

In [30] is proposed a real-time sleep apnea monitor using single-lead ECG implemented on

Android OS. Pentagay et al. [31],with an accelerometer placed on the chest has collected the heart

sound generated during OSA episodes combined with ECG.

Looking for non-contact techniques some methods were proposed, using principles of BCG wich

is a recording of the oscillatory body motion that occurs with every heartbeat and is therefore

attributed to forces produced by the heart action. [32] Chung et al. [29], installed load cells at the

bottom of a bed’s four legs, to obtain BCG measures. The objective was get a correlation with

HRV and sleep stages and the results showed substantial agreement with visual-scored methods".

However they had some problems because REM and wakefulness have similar patterns.

2.3.6 Respiration

Respiration is one of the key measurements in the monitoring of sleep disorders. The current

standards for respiratory monitoring are the Respiratory Inductive Plethysmogram and the air-

flow measurement methods which are very intrusive as the persons need to wear probes that will

constraint and restrict their movement.

Non-invasive alternatives have been proposed which make use of different techniques.

Hwang et al. [33] developed a method using polyvinylidene film based sensor, that detects dif-

ferent levels of pressure caused by the volume change in the body during the respiration phases.

OSA events were detected showing good correlation with the ones detected from PSG.

Castro et al. [34] developed an under-mattress system based on piezoelectric and piezoresistive

sensor to detect breathing and heart rate and measure sleep quality in patients with depression. The

sensor was put under the bed mattress, in the centre, and it was used a cylinder filled with water

to simulate a medium size adult. The results obtained shows that the system is able to detect

breathing if the person stays quiet in supine or prone.

Rofouei et al. [35] developed a method using a neck-cuff system with various sensors housed

in a soft neck-worn collar. The sensors included were an oximetry, a microphone and accelerom-

eter to detect OSA and the accuracies of the sensors was tested in one subject, with good results.

Masa et al. [36] made a study that evaluates the efficiency of thoracoabdominal bands and it

was possible the identification of respiratory effort-related arousal (obstructive events that are not

detected by thermistor) with efficacy similar to the oesophageal pressure measurement.

12 Background and State of the art

2.3.7 Audio

Audio recording is a useful method for monitoring sleep as it is inexpensive and does not dis-

turb the natural sleep environment as the microphone does not need to touch the subject. Audio

recordings are used to identify snoring, normal breathing or obstructive events.

Doukas et al. [37] proposed an integrated mobile platform for remotely and automatically di-

agnosing OSA based on snore analysis. The sleep sounds were collect from microphones placed

over the person’s bed and the data processing is based on the application of short discrete Fourier

transform.The system was tested in different conditions and obtained good detection rate. Alqas-

sim et al. [38] proposed a mobile application that uses voice recording software and accelerometer

combined to record the breathing patterns. The person places the smartphone on the arm, abdomen

or can be used a wireless Bluetooth accelerometer. The objective is diagnosing sleep apnoea and

the results shows three level of classification, severe, mild and no sleep apnoea.

2.3.8 Temperature

The central temperature can be used to measure indirectly the melatonin secretion which is inti-

mately correlated with sleep circadian rhythm.

Due the share of some vessels with hypothalamus, the cerebrum region that control the body

temperature, and the circadian cycles, the tympanum is a optimum place to measure temperature.

In [2], Sanches et al. proposed a method,using a auricular bluethooth with a thermometer that

continuously transmit the temperature to a smartphone. The results reveal the ability of the system

to track small temperature changes along the circadian cycle. Boano et al. [39] implemented a

system that uses small wireless sensores nodes on the body to measure distal skin temperature at

the bake of the hands and feet, at the trunk of the body((e.g., above the liver), and at the ear.The

results obtained shows a good classification on the circadian rhythm.

Other study suggested an integrated variable, based on thermometry, actimetry and body posi-

tion to reduce individual recording artefacts and showed that it is well correlated with rest-activity

logs [40]. In [41] it was evaluated an circadian phase estimation using standard least squares al-

gorithmic regression techniques on skin temperatures, accelerometry and ambient light level in

the blue spectral band. The results showed a statistically significant improvement of variance of

prediction error over traditional single predictor methods.

2.4 Smartphone based systems

Rapid advances in technology have enhanced the capability of smartphones and added powerful

features to them. Now, one can find phones with high computing capabilities, large capacity mem-

ories, built-in sensors,Bluetooth, Wi-Fi interfaces, and high resolution displaying options.Besides

the technology, open standards play a significant role in increasing the importance of smartphones

and encouraging developers to implement applications in different areas such as healthcare.

As reported in recent studies [42], the utilization of hand held devices such as smartphones in

2.4 Smartphone based systems 13

health-related applications will continue to increase.

2.4.0.1 Sensors built-in

Most Android devices have a number of built-in sensors included in three broad categories[43]:

• Motion: Includes accelerometers, gravity sensors, gyroscopes, and rotational.They measure

acceleration forces and rotational forces along three axes

• Environmental: Includes barometers, photometers, and thermometers.They measure envi-

ronmental aspects such as ambient temperature and pressure, illumination, and humidity.

• Position: Includes orientation sensors and magnetometers.They determine the "physical

position" of a device.

Helping you to acquire and make sense of raw sensor data there is something called the An-

droid sensor framework, providing classes and interfaces to work with the sensor manager. The

sensor frame work is useful to determine which sensors are available, the capabilities(such as max-

imum range, power requirements, resolution), acquire data and monitor sensor changes. There is

no direct way to determine the rate at which the sensor framework is sending sensor events to your

application.However, is possible to acquire timestamps associated with each event, enabling you

to calculate the sampling rate over multiple events[43].

Most of the available smartphone applications for sleep detection use some combination of a

screening questionnaire, actigraphy from the in-built accelerometer or a wrist actigraph, and an

analysis of the audio signal recorded from the phone’s in-built microphone.

In the application market, Google Play Store, there are many applications available, which

track sleep. "Sleep As Android" for android is one famous example of these applications available

in the market. It uses motion sensor for sleep tracking [44]. "Sleepbot" is another application

available for android that tracks motion and sounds to paint a visual picture of the sleep [45].

Comparing to the activity tracker devices, the advantage of such mobile phone applications is that

the user except his/her smartphone requires no extra device or sensor. However like the activity

trackers, these mobile phone applications also need interaction of the user and the sleep behavior

change of the user probably.

Chen et al. [46] proposed a combination of features collected from the light sensor, accelerom-

eter, microphone and phone usage to form a sleep model and predictor.

A prototype for an OSA screening app can be found in Behar et al. [47] which uses features de-

rived from audio, accelerometry and pulse oximetry and a support vector machine to generate a

probability that a patient has OSA. They have used questionnaires, audio, on-body actigraphy and

oxygen saturation from a large clinical database (856 patients) to validate the approach and the

results on the clinical data have been promising. However, this system, although helpful, is still

intrusive as it needs the user to attach an headphone microphone and the phone needs to be in a

armband.

14 Background and State of the art

None of the current applications that use the smartphone’s built-in sensors, and thus do not

require the purchase of additional hardware, are based on any published scientific evidence [47]

and observe some problem issues such as: The placement of the accelerometer (and hence phone)

is crucial, the location of the microphone and its characteristic acoustic recording properties will

cause enormous variations in the quality of the analysis and the varying quality of audio processing

cards on phones can lead to significant distortions in the recordings. [48]

2.5 Conclusions

The field of sleep analysis is complex and multi-faceted as the study made in this chapter reveals,

with monitoring applications almost always involving several different sensor types, depending

on the suspected conditions. Although EEG is considered the best approach for monitoring brain

activity during sleep, it is insufficient on its own for many sleep disorders(e.g. breathing disorders)

and can be invasive for the person in study. However other signs and monitoring modalities (such

as temperature, audio, ECG...) start being considered, due the development of signal processing

tools and cheap and faster processing hardware.

The rapid adoption of smartphones has led, as well, to a proliferation of applications which

allow a general user to have easy access to some form of self-applied monitoring. Some of them

lack of scientific evaluation of their performance, so appropriately calibration and proper decision

support is needed to be a reliable sleep monitoring.

Chapter 3

Project Specification and Work Plan

3.1 Proposed Approach

In order to accomplished the objectives proposed in Chapter 1 and based on the state of the art

study in the Chapter 2 the solution based on a smarthpone application will have the following

functional requirements:

• Autonomous and continuous monitoring: The system must be able to work for a long

period of time;

• Multi sensors accommodations: Able to integrate more than one sensor in the system and

combine the analysis of the different sensors data;

• Comfort: The solution must be sufficiently compact and lightweight to be worn without

inconvenience.

• Event triggers: Alerting the user when any physiological sensing variables exceeds a

threshold value or when an event of concern is derived from sensor combination;

• Evaluation: Recognition of sleep patterns and evaluate the user’s sleep quality.

3.2 System overview

Figure 3.1 presents an overview of the global architecture for the proposed approach. Sensors (A)

are applied to the subject with the purpose of simultaneously monitoring one or more parameters.

A controller-device (B), the acquisition unit, performs the analog-to-digital conversion and data

encoding of the biosignals coming from the different sensors. Furthermore, the end-device (B)

provides Bluetooth wireless connectivity to the smartphone (C).

The data streamed by the end-device (B) is continuously collected and decoded in real-time on

the smartphone (C) which performs storage and instant display. In the smartphone, algorithmic

transformations are also performed over the signals, which include data compression (based on

deflate algorithm), and signal processing operations consisting of transfer functions computation

15

16 Project Specification and Work Plan

and automated biosignal modelling. Local real-time monitoring of incoming data is shown in the

smartphone (C) screen to provide local feedback to the user, based on a UI screen.

Figure 3.1: System Architecture

3.2.1 Sensor Module

Data is collected from sensors at a specified frequency. The sample rate can be empirically chosen

to provide sufficient resolution while compensating for bandwidth constraints of the system, or it

can be determined to satisfy Nyquist criterion. The device performs noise cancelling, filtering(to

remove unnecessary data).This provides a clean signal for further data extraction.

3.2.2 Bluetooth Communication

The selected technology for communication the cleaned up data is going to be the Bluetooth (BT),

which is one of the three wireless standard interfaces integrated in the vast majority of commercial

smartphones. Although it was not conceived of for sensor networks, Bluetooth is clearly a more

energy-efficient technology than 3G and Wi-Fi. Other low consumption radio protocols (ZigBee,

ANT, wirelessHART,etc.) are not widely employed yet and, most importantly, have not been

incorporated as communication interfaces to existing smartphones until now.[49] In this sense,

Bluetooth is a compromise between consumption and available bandwidth for point-to-point short-

range transmissions.

Nowadays, the most straightforward, cost-effective and seamless way to create a medical personal

area network based on a smartphone is by means of Bluetooth connections.[50] The use of other

wireless technologies obliges the developing of specific hardware modules and heavily reducing

the usability of the smartphone by incorporating external wired radio interfaces.

3.2.3 Smartphone Module

On the smartphone, the data is received from the device via Bluetooth. The software architecture

is going to be implemented as three layers. At the top, the user interface and alarm system is the

3.3 Development Tools 17

main interaction with the user. The middle layer is made up of signal processing and classification

algorithms. At the bottom layer(the physical interface), the communication is performed.

The middle layer of the software is the key layer where the signal processing and classification

algorithms, adequate to the signal received via Bluethoot, are implemented. The results obtained

from this implementation will be shown in the user interface or will deploy alarms to the users.

Furthermore, these layer can be easily upgraded as more sensors or different types of sensors are

added.

3.3 Development Tools

3.3.1 Sensor

The sensor that is going to be used for recording vital signs must be lightweight and small to be

easy and comfortable to wear for a prolonged period. Additional requirement for the sensor are

the low-power consumption. The type of sensor and which vital signs that is going to be collected

it still going to be decided furthermore.

3.3.2 Android Operation System Overview

Android is actually Linux-kernel based operating system for mobile devices, on top of it, there’s

the Dalvik Virtual Machine with its runtime environment, native libraries and services. Applica-

tions can be developed in Java running in Dalvik Virtual Machines or Java Native Invocation to

use native libraries in the system.[51]

For better performance, thus reducing CPU and memory usage in the device is recommended

to use native libraries. Google provides a Software Development Kit (SDK) for developing Java

based applications and a Native Development Kit (NDK) for native code developing in C/C++.[52]

The Eclipse is an Integrated Development Environment(IDE) which integrate a built-in Android

Developer Tools and the Android SDK to help develop the Android application.

3.3.2.1 Applications Components

Within an Android application the following main components are distinguished [53]:

An activity that provides the user a graphic interface and handle the user interaction to the

smartphone screen(e.g. email, calendar and phone dial). It is possible to have multiple activities

connected to each other. If an application has more than one activity, then one of them should be

marked as the activity that is presented when the application is launched and the others activities

are launched by that one.

A services that runs in the background and doesn’t have a graphic interface.It could be used

to start a service to perform a one-time operation (such as download a file).If an application has

more than one activity, then one of them should be marked as the activity that is presented when

the application is launched. Services interact with users by sending notifications through Toast

Notification and Status Bar Notification.

18 Project Specification and Work Plan

A Broadcast Receivers that simply respond to broadcast messages from other applications or

from the system.(e.g. SMS received, incoming call and low battery). It is a component that runs as

an asynchronous process and consumes less computational resources compared to a service. Each

application can have several broadcast receivers and these are able to start new activities when a

notification is received.

3.3.3 WEKA

The classification evaluations is going to be carried out using the classifiers algorithms available

in the Waikato Environment for Knowledge Analysis (WEKA). WEKA is a free machine learning

and data mining tool, which provide a collection of supervised classification algorithms and data

pre-processing methods, along with GUI’s for data analysis. The advantages of WEKA is, that

the algorithms can either be applied directly to a dataset or called from the own Java code. Weka

contains tools for data pre-processing, classification, regression, clustering association rules, and

visualization.[54]

3.4 Work Plan

Figure xx presents the Gantt diagram of the project work plan with the expected duration for each

task.1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

End literature review

Study work tools

Sensor Data collection

Signal processing and data analysis

Android application development

Tests and validation the results

Thesis writing

Website development and updating

Thesis Delivery

3.4.1 Task Description

• End literature review: This task will last just two weeks and serves to complete the litera-

ture review based on the solution adopted

• Study work tools: It is required the study of the proposed work tools. It is necessary getting

familiar with the Android environment and the study of the sensor’s datasheet.

• Sensor data collection: After the study of the sensor, it is necessary start to collect data for

futures tests and evaluations

3.4 Work Plan 19

• Signal processing and data analysis: In this tasks it is going to be processed the sensor’s

data for the integration on the android applications as well as the study of the best algorithms

for sensor’s data analysis.

• Android application development: Definition, design and analysis of the system’s require-

ments including the development framework and then implement the application.

• Tests and validation the results: Realization of tests and validation and then correct exist-

ing issues and optimize the code.

• Thesis writing: This final task is reserved for the writing of the dissertation report as well

as developing the final project presentation;

• Website development and updating: This is a continuous task, starting from the first week,

for the development of the project website as its continuous update.

20 Project Specification and Work Plan

Chapter 4

Conclusion

The present report described the problem of sensor based sleep patterns and disorders analysis.

This problem has been widely discussed since there is an interest in develop home diagnostic

systems in opposition of the conventional PSG based clinical analysis.

To deeper understand the whole problem it is required to know the sleep phenomenology,

which are its different phases and effects and which are the physiological variables involved in the

process. It is also necessary to know the different sleep disorders and how to diagnosis them. Only

with this wide knowledge it is possible to think in a new solution.

The state of the art solutions try to approach the problem with monitoring applications involv-

ing several combinations of different sensor types. It is possible to conclude that a lot of work as

been done principally in the study of sleep breathing disorders (such as sleep apnea) and in the

study of circadian rhythm disorders. The study of sleep breathing disorders is mainly made with

physiological variables of the respiratory and cardiovascular system analysis. The study of the

circadian rhythm disorders is mainly made with actigraphy.

After the characterization of the main objectives of the dissertation and reviewing the state of

the art, we presented in Chapter 3 the strategy that will be used and a temporary work plan and

methodology were defined.

21

22 Conclusion

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