somnolence detection and analysis based on labview

8/19/2019 somnolence detection and analysis based on labview

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CHAPTER 1

INTRODUCTION

1.1INTRODUCTION

Somnolence is the state where a person is almost asleep or very lightly asleep. It refers to an

inability to keep awake [41]. In this thesis somnolence and sleepiness are considered

synonymous, but the term somnolence will be used. nother concept commonly used is fatigue,

which is an e!treme tiredness that comes from physical or mental activity. Somnolence can also

be described by the grade of wakefulness or vigilance. "akefulness is the same as alertness or a

state of sleep inability, whereas vigilance can be defined as watchfulness or a state where one is

prepared for something to happen. #here are several factors which affect the grade of

wakefulness [41]. #he time spent to carry out a task $time on task% and the amount of sleep

during night are the most important factors. &ther factors which are responsible are the amount

of light, sound, temperature and o!ygen content. 'otivation and monotony of the task will also

have an effect on the grade of wakefulness.

'any traffic accidents are caused by drivers falling asleep at the wheel [41]. It would

thus be important to find a way to detect somnolence before it occurs and to be able to warn the

driver in time to avoid traffic accidents. Some systems have already been developed, based on

recording of head movements, steering wheel movements, heart rate variability or grip strength.

Systems that use a video camera for the tracking of eye movements have also been developed.

(owever, so far no system has proved to be sufficient reliable [))] which can detect somnolence

and alert the drivers in time to avoid fatal accidents that take life of so many passengers aboard.

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1.2ACCIDENTS CAUSED BY SOMNOLENT DRIVERS

ccording to statistic analyses made by the merican *ational (ighway #raffic Safety

dministration $*(#S% [41], the official number of traffic incidents on highways related to

somnolence is 1+). (owever, scientific studies the last years reveal that the actual number

probably is much higher. #he number should be as much as 1-+- [41]. &ne reason can be that

people that report traffic accidents lack the practice in /udging the role of somnolence as a

contributing factor. It is difficult to give an e!act measure of somnolence in the way that is

possible with for e!ample alcohol. 0urthermore, because somnolence is a transient state, it also

makes the detection difficult.

1.3METHODS USED FOR SOMNOLENCE DETECTION

Somnolence can be measured by using physiological measures, performance measures, self

report or e!pert ratings [))]. #he different methodologies are described below.

1.3.1 PHYSIOLOGICAL MEASURES

hysiological measures are commonly used for somnolence detection as these can provide a

direct and ob/ective measure. ossible measures are 223, eyelid

closure, eye movements, heart rate, pupil sie, skin conductance and production of the hormones

adrenaline, nor+adrenaline and cortisol [))]. 223 can be counted as the reliable indicator of

somnolence. #he amount of activity in different fre5uency bands can be measured to detect the

stage of somnolence or sleep. Several studies also reveal that the good indicator of somnolence

are eye parameters such as blink duration, blink fre5uency, delay in lid reopening and the

occurrence of slow eye movements (S2'%. #hese parameters can be measured by 2&3. In a

paper [46] it has been suggested that somnolence should be defined based on a combination of

brain and eye. 223 could be used to detect deficiencies in information processing, which can

occur even though the eyes are wide open, and the slow eye closures would detect insufficient

perceptual capabilities. #he problems with both 2&3 and 223 are the re5uirement of obtrusive

electrodes which make them unsuitable to use in cars, as cabling of the drivers would not achieve

any acceptance. (ence, they are not compatible to be used in a real+time somnolence detection


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system. decrease in heart rate and an increase in heart rate variability are taken as to be

indicators of somnolence, as well as decrease in pupil sie, spontaneous pupillary movements

and decrease in skin conductance. decreased production of adrenaline, nor+adrenaline and

cortisol are other possible indicators of somnolence [))].

1.3.2 DRIVING PERFORMANCE MEASURES

7riving performance measures include steering wheel movements, lateral position, speed

variability and reaction time. Studies indicate that the steering wheel variability increases with

the amount of somnolence. #he steering movements also become larger and occur less often, and

the lateral position variability increases as the driver gets drowsier. lso, with the increase in

somnolence, the speed variability increases and the minimum distance to any lead vehicle

decreases. #he reaction time to any une!pected events also gets longer with increased

somnolence. #he problem concerning using driving performance measures as indicators of

somnolence is inter+ and intra individual differences in driving performance, which could be

solved by a combination of different measures. It has been suggested that the sufficient reliable

detection method is the combination of performance measures with physiological measures [))].

1.3.3 SELF REPORT

Self+report refers to the sub/ective rating made by the driver and there are many rating scales are

obtaining this. It is important that the scales are displayed in such a way that they are unobtrusive

and don8t alert the driver, since that would affect the drivers state. #here are various rating scales

have been constructed, for e!ample the Stanford Sleepiness Scale $SSS% and the 9arolinska

Sleepiness Scale $9SS% [4:]. 9SS is a nine graded absolute rating scale that has been validated

against 223 and 2&3 indicators of sleepiness [4:]. Step 1, ), 6, ; and < contain a verbal

description of somnolence. #he original 9SS has been modified by adding descriptions to the

intermediate steps as well. #he reason for this is that people seemed to report the steps with

verbal descriptions more often than the intermediate steps.

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MODIFIED VERSION OF KSS

Some descriptors about how alert or sleepy you might be feeling right now are shown in #able

1.1 below.

Table 1.1 'odified version of 9SS.

"hen used in driving e!periments the scale is memoried by the driver before the e!periment

and a verbal rating shall be made, to avoid disturbing the driver.

1.3.! E"PERT RATINGS

2!pert ratings are made on a similar scale as self report by an observer. =esults from earlier

studies indicate that these ratings are reliable and consistent. #he observer looks for behavioural

indicators of somnolence, for e!ample eyelid closures, a vacant stare, body movements or the

head falling backward or forward.

S#. N$. S%a%e

1 2!tremely alert

>ery alert

) lert

4 =ather alert

6 *either alert nor sleepy

? Some signs of sleepiness

; Sleepy @ but no difficulty remaining awake

: Sleepy, some effort to keep alert

< 2!tremely sleepy, fighting sleep

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1.! ELECTROOCULOGRAM (EOG&

1.!.1 ORIGIN OF THE EOG SIGNAL

2lectrooculography is a method used for the measurement of potential difference between the

front and back of the eye ball. #he 2&3 can thus be used for detection of blinks and eye

movements. #he eye is a dipole with the positive cornea in the front and the negative retina in the

back and the potential between cornea and retina lies in the range -.4 @ 1.- m>. steady baseline

potential is measured by electrodes placed around the eyes when the eyes are fi!ated straight

ahead. change in potential is detected when the eyes are moved as the poles come closer or

farther away from the electrodes, see 0igure 1.1. #he sign of the change depends on the direction

of the movement [)


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movements from vertical, and eye movements from eye blinks. Dy using different kinds of

electrode placements the obtained recordings can be either vertical or horiontal. In horiontal

recording they are placed at the outer edges of the eyes and in vertical recording electrodes are

placed under and above the eye. >ertical recording is usually monocular, which means that the

recording is made across one eye, whereas horiontal recording usually is binocular. 0igure 1.

shows how the electrodes are placed. 2ye blinks are detected by using vertical recording [);]

[)


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Since a change in the form of the blink artifact can be used for hypo vigilance detection, so it is

important to be able to distinguish eye blinks from vertical eye movements [)-].

arameters that are used to describe the blink behaviour, e!tractable from the 2&3

signal, are for e!ample blink fre5uency [blinksFminute], amplitude or eyelid opening level [m>]

and duration [ms]. "hen a person is rela!ed heFshe blinks about 16+- times per minute,

although only +4 are needed from a physiological viewpoint [)


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F')#e 1.3 De/''%'$ $/ bl'0 +)#a%'$ T ' EOG.

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1.ELECTROENCEPHALOGRAM (EEG&

1..1 ORIGIN OF THE EEG SIGNAL

2lectroencephalography is a method commonly used for measuring the electrical activity

generated by the nerve cells of the brain, mainly the cortical activity. #he 223+activity is present

all the time in the brain and recording show both random and periodic behaviour. #he main

origin of the 223 is the neuronal activity takes place in the cerebral corte!, but some activity

also originates from the thalamus and from sub cortical parts of the brain. #he 223 represents

the summation of e!citatory and inhibitory postsynaptic potentials in the nerve cells. #he

rhythmic activity is because of the synchronous activation of the nerve cells [)


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Theta waves $6+; (% have an amplitude of -+1-- E> and will occur in the early stages of sleep,

by hypnagogic imagery, focusing of attention or by problem solving. #here e!ist two types of

theta activity, one that is associated with performance of cognitive tasks and other associated

with the early stages of sleep [). 2!istence of fre5uencies in the delta range in the awake

condition is not normal and probably due to artefacts, but can also be an indicator of a brain

tumour [)


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reference site is normally one ear or the nose. #he sampling fre5uency should be at least 1: (.

#he measured signal is small, only a few microvolt $compared to 2&3 H1-- E>%, which re5uires

a large amplification factor. #o minimie the load on the body, amplification is necessary, which

reduces the current density between the skin and the electrodes. high current density otherwise

implies polariation of the electrodes. #he amplification can make it difficult to separate the real

signal from artefacts [);].

n international system which is used for positioning of the electrodes has been

constructed which is called the International 1-F- system. #he name indicates that the

electrodes are placed at positions 1- and - of the distance between four anatomical

landmarks. #he landmarks are the nasion $bridge of nose%, the inion $pro/ection of bone at the

back of the head% and the left and right preauricular points $depressions in front of the ears%.

#hese points are labeled with a letter and a subscript inde!. #hese letters refer to the regions of

the brain 0 J frontal, & J occipital, A J central, J parietal and # J temporal. #he midline and

numbers indicating the lateral placement and degree of displacement from the midline are

indicated by the subscript indices are . n odd number refers to the left hemisphere, an even to

the right hemisphere. #he number gets 1) higher the farther away it is from the midline [);] [). nother problem which

occur is the small electromagnetic disturbances induced in the cables. #he person should made

no movements and a proper electrode preparation is necessary to minimie the impedance

between skin and electrode [)


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F')#e 1. 2lectrode placement.

1.. CHANGES IN EEG DURING SOMNOLENCE

223 is the most commonly used indicator of somnolence measurement. 223 is widely accepted

as a good indicator of the transition between wakefulness and sleep as well as between the

different sleep stages. It is often referred to as the golden standard. In the person is in alert

condition, or when performing cognitive tasks, the appearance of beta activity is common in the

223. lpha activity is also commonly found in the occipital regions $&1 and &% in the awake

and rela!ed condition [)1] [);] [)


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F')#e 1. EEG ,a%%e# ' a4a0e *$+'%'$

F')#e 1.5 EEG ,a%%e# ' 6$-$le% *$+'%'$

1)


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1. EMPIRICAL MODE DECOMPOSITION (EMD&

1..1 INTRODUCTION

In the last few years, 2lectroencephalogram $223% have received much attention recently due to

the growing interest and popularity of research related to brain computerFmachine interfacing

$DAIFD'I% techni5ues, owing to the very e!citing possibility of computer+ aided communication

with the outside world. new and growing interest in neuroscience, also known as steady+state

potentials stimuli techni5ue, which produces longer in+time and more easy to detect within

monitored 223 steady responses contributes also to 223 signal processingKs recent popularity.

#he noninvasive recording setup is used to monitor the 223 based brain stages. In terms of

signal processing these monitoring stages include the detection, estimation, interpretation and

modeling of brain activities, and also cross+user transparency. #his technology is envisaged to be

at the core of future Lintelligent computingM. &ther industries which would benefit greatly from

the development of online analysis and visualiation of brain states include the entertainment,

prosthetics, virtual reality, and computer games industries, where the control and navigation in a

computer+aided application is achieved without resorting to using muscles, hands, or any

gestures $peripheral nervous system in general%. Instead, the onset of planning an action

Lrecorded from the scalp, and the relevant information is decodedM from this information carrier.

part from purely signal conditioning problems, in most DAIFD'I e!periments other issues suchas user training and adaptation, inevitably cause difficulties and limit a wide spread of this

technology because of the lack of generality caused by cross user differences. "e propose to

make use of a new and growing interest in signal processing community techni5ue of empirical

mode decomposition $2'7%, to help mitigate some of the above+mentioned issues which we

e!tend to multichannel approach of parallel decomposition of single channel signals and further

clustering of so+obtained components among channels to track coherent $synchronied or

correlated in spectral domain% activities in comple! signals as 223.

223 is usually characteried as a summation of e!tracellular currents caused by post+

synaptic potentials $intracellular% from a large sum of neurons which create oscillatory patterns

distributed and possible to record around the scalp. #hose patterns which are in the known

fre5uency ranges can be monitored and classified in synchrony with stimuli given to the sub/ects.

2'7 utilies empirical knowledge of oscillations intrinsic to a time series in order to represent

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them as a superposition of components with well defined instantaneous fre5uencies. #hese

components are called intrinsic mode functions $I'0%. new concept of multiple spatially

localied amplitude and fre5uency oscillations related to presented stimuli in time fre5uency

domain is described which let us obtain final traces of fre5uency and amplitude ridges coherent

among the 223 channels..

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CHAPTER 2

LITERATURE REVIE7

2.1 LITERATURE REVIE7

1.&P'*$% A%$'e e% al. 'ay -1 in the paper entitled N&n+Gine 7etection of 7rowsiness Csing

Drain and >isual Information8 [] introduces a somnolence detection system using both brain and

visual activity is presented in this paper. #he brain activity is monitored using a single

electroencephalographic $223% channel. n 223+based somnolence detector using diagnostic

techni5ues and fuy logic is proposed. >isual activity is monitored through blinking detection

and characteriation. Dlinking features are e!tracted from an 2lectrooculographic $2&3%

channel. 0eatures are merged using fuy logic to create an 2&3+based somnolence detector.

#he features used by the 2&3+based detector are voluntary restricted to the features that can be

automatically e!tracted from a video analysis of the same accuracy. Doth detection systems are

then merged using cascading decision rules according to a medical scale of somnolence

evaluation. 'erging brain and visual information makes it possible to detect three levels of

somnolenceO Lawake,P Lsomnolent,P and Lvery somnolent.P &ne ma/or advantage of the system

is that it does not have to be tuned for each driver. #he system was tested on driving data from -

different drivers and reached :-.? correct classifications on three somnolence levels. #he

results show that 223 and 2&3 detectors are redundantO 223+based ntoine icot, Sylvie Ahar

bonnier, and lice Aaplier detections are used to confirm 2&3+based detection and thus enable the

false alarm rate to be reduced to 6 while the true positive rate is not decreased, compared with

a single 2&3+based detector.

METHODOLOGY

#he overview of the detection method is shown in figure .1. 0irst the 223 power spectrum is

computed using a Short #ime 0ourier #ransform $S#0#% to calculate the relative power into the

different 223 bands every second. #hen, the relative power of the alpha band is median

filtered using a sliding window to re/ect abnormal values. 'eans Aomparison #est

$'A#% is computed at last to compare the energy to a reference level, learnt at the

1?


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beginning of the recording while the patient is not supposed to be somnolent. 'A# is

normalied. common threshold of detection can be proposed taking into account the

acceptable level of false alarms and validated using e!periments which has been presented

in $icot et al., --:%. Aoncomitantly, a >ariances Aomparison #est $>A#% is computed

on the raw 223 data to detect high amplitude artifacts. Information on the occurrence of

artifacts can be used as an inde! of reliability on the Lsomnolent decisionP.

0igure .1 Somnolence detection method

2.2LITERATURE SURVEY

S'8 I%e+e#,al e% al. -1) in the paper entitled L7evelopment of a drowsiness warning

system using neural networkP [1] states that, a vehicle driver somnolence warning system using

image processing techni5ue with neural network is proposed. #he proposed system is based on

facial images analysis for warning the driver of somnolence or inattention to prevent traffic

accidents. #he facial images of driver are taken by a video camera which is installed on the

dashboard in front of the driver. *eural network based algorithm is proposed to determine the

level of fatigue by measuring the eye opening and closing, and warns the driver accordingly. #he

results indicated that the proposed e!pert system is effective for increasing safety in driving.

1.&L' F)9C8a e% al. Sept. -1 in the paper entitled N3eneralied 223+Dased 7rowsiness

rediction System by Csing a Self+&rganiing *eural 0uy System8 [4] states that 7riverKs

somnolent state monitoring system has been implicated as a causal factor for the safety driving

1;


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issue, especially when the driver fell asleep or distracted in driving. (owever, the difficulties in

developing such a system are lack of significant inde! for detecting the driverKs somnolent state

in real+time and the interference of the complicated noise in a realistic and dynamic driving

environment. In our past studies, we found that the electroencephalogram $223% power spectrum

changes were highly correlated with the driverKs behavior performance especially the occipital

component. 7ifferent from presented sub/ect+dependent somnolent state monitor systems, whose

system performance may decrease rapidly when different sub/ect applies with the somnolence

detection model constructed by others, in this study, we proposed a generalied 223+based Self+

organiing *eural 0uy system to monitor and predict the driverKs somnolent state with the

occipital area.

2.&De68-)08 P#a:al' e% al. ugust -1 in the paper entitled N223 based 7rowsiness

estimation using 'ahalanobis distance8 [)] in this paper, a new lgorithm for automatic driver8s

somnolence detection based on 223 using 'ahalanobis 7istance is proposed. #his uses

physiological data of drivers to measure or detect somnolence. #hese include the measurement of

brain wave or 223 and approaches based on 223 signals have the advantages in making

accurate and 5uantitative assessment of alertness levels. (ence under the assumption that the

223 power spectrum in an alert state can be reasonably modeled using a multivariate normal

distribution, 7etection of the somnolence present in the signal with known awake signal is the

sub/ect of this paper.

3.&V':a;ala and *eural

*etwork approach. #he algorithm is tested on nearly 1-- images of different persons under

different conditions and the results are satisfactory with success rate of


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'ulti+Gayer erceptron $'G% as a classifier. Specifically, the proposed method estimates =

coefficients using 2I> $2rrors+In >ariables% providing an accurate estimation in a noisy process

and linear predictive coding $GA% analysis not considering noise. Samples of 223 data from

each predefined state were used to train the 'G program by using the proposed feature

e!traction algorithms. #he trained 'G program was tested on unclassified 223 data and

subse5uently reviewed according to manual classification.

.&Ma#+' =a8#a e% al. -11 in the paper entitled N223+Dased 7rowsiness 7etection for Safe

7riving Csing Ahaotic 0eatures and Statistical #ests8 [;] states that they have tried to

demonstrate that sleepiness and alertness signals are separable with an appropriate margin by

e!tracting suitable features. #hey have recorded the signals while sub/ects did a virtual driving

game. #hey tried to pass some barriers that were shown on monitor. #hen, after preprocessing of

recorded signals, we labeled them by somnolence and alertness by using times associated with

pass times of the barriers or crash times to them. #hen, we e!tracted some chaotic features

$include (iguchi8s fractal dimension and etrosian8s fractal dimension% and logarithm of energy

of signal. Dy applying the two+tailed t +test, we have shown that these features can create


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5.&D8),a%' L.S e% al. ug. -1- in the paper entitled N novel drowsiness detection scheme

based on speech analysis with validation using simultaneous 223 recordings8 [11] states that #he

results are simultaneously validated through 2lectroencephalography $223% based

measurements. "e have designed a )?+hour long e!periment where the sub/ects are asked to

repeat a particular sentence at different stages. #he response is analyed for computing various

parameters such as voiced duration, unvoiced duration, and the response time. "e have used

'el+0re5uency+Aepstral+Aoefficients $'0AA% as the features for the silence, voiced and

unvoiced parts of speech. "e have segregated these parts using a 3aussian 'i!ture 'odel

$3''% classifier. #he results have been validated with an 223 based parameter i.e. relative

energy of R band which increases with fatigue. correlation between Speech and 223 based

measurements is observed at various stages of the e!periment.

>.&=8a C8e e% al. ug. -1- in the paper entitled Nn 223+based method for detecting

drowsy driving state8 [)] states a method based on power spectrum analysis and 0ast IA

algorithm was proposed to determining the fatigue degree. In a driving simulation system, the

223 signals of sub/ects were captured by instrument *#+


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data sampled from - professional truck drivers and )6 non professional drivers, the time

domain data are processed into alpha, beta, delta and theta bands and then presented to the neural

network to detect the onset of driver fatigue. #he neural network uses a training optimiation

techni5ue called the 'agnified 3radient 0unction $'30%. #his techni5ue reduces the time

re5uired for training by modifying the Standard Dack ropagation $SD% algorithm. #he '30 is

shown to classify professional driver fatigue with :1.4


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1).%Pa#'08 P e% al. pril --4 in the paper entitled N7etecting drowsiness while driving using

wavelet transform8 [


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present no obstruction to the driver. n ** was trained and tested. #he training and testing

data was obtained from a previous e!periment in a driving simulator driven by twelve drivers,

each under different levels of sleep deprivation. #he network classifies driving intervals into

somnolent and non+somnolent intervals with high accuracy.

s from the above discussion, it is clear that there are many techni5ues by which somnolence

can be detected. Some focus on the behaviour of vehicle. s the paper written by Sayed, =.,

2skandarian, ., and &skard, '. the ** observes the steering angle patterns and classifies

them into somnolent and non+somnolent driving intervals. #hese techni5ues are not much

successful because the behaviou of the vehicle can change with the time, temperature etc.Some

techni5ues based on the physical behaviour of the driver. *eural network based algorithm is

proposed by Itenderpal singh and rof. >.9.Danga to determine the level of fatigue by measuring

the eye opening and closing, and warns the driver accordingly. lso in the paper written by

>i/ayala!mi, .Sudhakara =ao and S Sreehari the somnolence is detected by *eural *etwork

which is trained with 6- non+eye images and 6- eye images with different angles using 3abor

filter. #his techni5ue is also not successful because the physical behaviour of drivers changes

from driver to driver. #he other techni5ues based on the physiological behaviour of the driver.

ll the rest papers above mentioned used this method. #hey used the 223 and 2&3 for

somnolence detection. #his is the most accurate techni5ue to detect the somnolence. In this thesis

the 223 is used to detect the somnolence. #he 2'7 is used to e!tract the I'08s and then the

neural network is trained using these I'08s, to detect the somnolence.

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CHAPTER 3

FORMULATION OF PROBLEM

3.1 OBECTIVES

Somnolence is the main problem while driving. 'ostly the accidents occur due to somnolence.

#his thesis work focuses in the detection of somnolence using the techni5ue 2'7. #he 2'7 is

used to e!tract the I'0s from the 223 data and then these I'0s are converted into fre5uency

domain using (ilbert8s transform. #hen these fre5uencies are used to train the neural network.

#he 'ain ob/ectives of this thesis work can be summaried as follows

• #he 223 data of the sub/ect is taken using the Drain #ech software by using 1? channels.

#he video of sub/ect is also taken by the camera attached on the 223 machine.

• #he 223 data and video are converted into mat files.

• #he somnolent samples are noted manually by using video.

• #he I'08s are e!tracted from the marked somnolent samples using 2'7 to prepare the input

feature vector table.

• =esultant feature vector table is given as input to pre+trained *2C=G *2#"&=9 system

for somnolence detection.

• &utputs of Somnolent and wake are analyed using Gabview Diomedical "orkbench.

3.2 FORMULATION OF PROBLEM

Somnolence is the transition state between awakening and sleep during which a decrease of

vigilance is generally observed. #his can be a serious problem for tasks that need a sustained

attention, such as driving. ccording to a report of the merican *ational (ighway Safety#raffic dministration driver somnolence is annually responsible for about 6?,--- crashes which

is the reason why more and more researches have been developed to build automatic detectors of

this dangerous state. #he driver state monitoring systems can be classified into three kinds of

systemO

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1.%0ocusing on the vehicle behaviour

.%0ocusing on the driver physical behaviour

).%0ocusing on the driver physiological behaviour

FOCUSING ON THE VEHICLE BEHAVIOUR

#he first systems developed were the ones using sensors monitoring the vehicle behavior. #he

main features studied in this areO

• Steering wheel movements

• Gateral position of the car on the road

• Standard deviation of lateral position $S7G%

• #he time to line crossing $#GA%.

#he purpose is to detect an abnormal behaviour of the car, due to the driver somnolence. #he

problems encountered by this kind of methods are that the features used depend on the shape of the road and how one drives, which may change a lot from one driver to another [46].

FOCUSING ON THE DRIVER PHYSICAL BEHAVIOUR

#hese kinds of systems focus on the driversK visual attention. 0ace, mouth and eye tracking

algorithms are used to detect the face. &nce the face, the eyes and the mouth are located, it is

easy to detect eye blinking or yawning and calculate their fre5uency and duration. 0re5uency and

duration of yawning or eye blinking too high indicate a decrease of attention. #he gae can be

calculated with the eyes and the face position or using a stereoscopic camera. #hen, it allows the

driver to be warned when he is not looking at the road. (owever, many differences can be

observed between drivers, which make it hard to monitor fatigue with only one feature. #he

probabilistic networks allow all features to contribute to the decision of the level of attention.

'oreover, e!ternal factors $weather, hour of the day, etc...% can contribute in these networks to

determine the level of attention. (owever, video features are not the best indicators of

somnolence. #he best indicators of fatigue are the physiological indicators.

FOCUSING ON THE DRIVER PHYSIOLOGICAL BEHAVIOUR

#he 2lectroencephalogram $223% and the 2lectro+oculogram $2&3% are mainly used to study

somnolence. Uet, several researches have focused on other physiological indicators such as the

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electrocardiogram $2A3% to monitor driversK heart rate or the driversK temperature. #he 2&3 is

the measurement of the resting potential of the retina. 223 is so efficient in detecting

somnolence that it is often used as a reference indicator.

STRESS FREE EEG SIGNAL ACUISITION IN VIRTUAL DRIVING

ENVIRONMENT

In this study, we have defined a new protocol for data ac5uisition based on driving condition.

Introduced protocol is a safe and simple one for somnolent driving data ac5uisition, because in

some previous protocols, researchers have not given attention to driving situation. It means that

they have recorded 223 signal from somnolent sub/ects in usual condition, but not while

driving. Some data ac5uisitions have been done when sub/ects drive a real car. #his protocol is

the best way for data recording from somnolent drivers, but has some disadvantagesO

1. It is really e!pensive as if driver can8t be able to control the car then it can lead to fatal

accidents which will be a great loss in term of life and finance

. It is a time consuming process as driver knows that he has to get somnolent and will be

difficult to get into that state.

). It makes sub/ects stressful because sub/ects know that it is possible to get somnolent and

have driving events, so 223 data is a mi!ture of stress and somnolence.

Gaboratory researches have shown that in reality, drivers that get somnolent are not aware

of their somnolence, so before driving events they have no stress or an!iety about incidence of

accident. Decause of this, we have simulated driving condition by a simple method. &ur protocol

was a safe and simple one in our virtual driving condition, sub/ects were rela!ed and they were

not under stress so 223 signals arise from somnolence, but not out of stress.

?


27/53

CHAPTER !

METHODOLOGY ADOPTED

!.1 DESIGN METHODOLOGY

#he aim with this thesis is to develop the method for somnolence detection. 223 data, recorded

from 1? electrodes from the : different sub/ects. 7etection of somnolence based on e!traction of

I'08s from 223 signal using 2'7 process and characteriing the features using trained

rtificial *eural *etwork $**% is introduced in this paper. &ur sub/ects are : volunteers who

have not slept for last 4 hour due to travelling. 223 signal was recorded when the sub/ect is

sitting on a chair facing video camera and are obliged to see camera only. ** is trained using autility made in 'atlab to mark the 223 data for somnolent state and awaked state and then

e!tract I'08s of marked data using 2'7 to prepare feature inputs for *eural *etwork. &nce the

neural network is trained, I'0s of *ew sub/ects 223 Signals is given as input and ** will

give output in two different states i.e. Nsomnolent8 or Nawake8. #he system is tested on : different

sub/ects.

In this process, we have selected sub/ects based on their daily working routines. ll the

sub/ects are working in a company as Service engineers who provide services to the clients on

site and travel from one state to another. "e selected the sub/ect who travelled whole night and

are now sleep deprived. In the morning when they reach office their state is similar to the

operators who are forced to do monotonous, but attention demanding /obs like driving for long

routes. So in this way our sub/ect is ready for the recording. ll the steps followed after these are

shown in flowchart on ne!t page.

;


28/53

CHAPTER

RESULTS AND DISCUSSION

.1 EEG RECORDING

SUBECTO #he sub/ect for taking 223 recording for 7rowsiness detection was arranged with

the person who was not properly slept in the previous night due to long /ourney. "e tried to

simulate the condition of the drivers during driving for long hours and for this sub/ect had to sit

on chair and have to see the camera continuously as the drivers see the road. s the sub/ect not

got proper sleep in the previous night due to travelling, he becomes somnolent after some time

watching the camera. 223 recording was taken from the start till the sub/ect starts sleeping to

see the change in 223 signals in this whole process. Csing the >ideo camera it becomes easy to

see the condition of sub/ect for awake and somnolent stages and this will help us in marking the

positions for somnolent signals using L#ake SamplesP.

HARD7AREO #o take 223 recording following Items are re5uiredO

• 223 'achine

• 223 Software

• 223 2lectrodes

• 223 aste

• A

• >ideo Aamera $#o capture sub/ect8s condition%

ll the items along with 223 'achine L BrainTechP were arranged from Alarity 'edical vt.

Gtd. a company that manufactures neurological e5uipments based in 'ohali. Drain#ech is a 4

channels 223 recording machine that captures signal from brain at the rate of 6? ( andsimultaneously records >ideo using >ideo Aamera attached to the A. Drain#ech uses software

provided by company to capture data and present it on screen based on the selected 'ontage,

0re5uency and Sweep Speed.

:


29/53

RECORDING

223 recording is obtained by placing electrodes on the scalp with a conductive paste, usually

after preparing the scalp area by cleaning it with hair cleaning solution to reduce oil from the

scalp to reduce impedance due to dead skin cells. 2lectrodes are placed on the locations specified

in the International 1-+- system which is used for most clinical and research applications as

shown below in figure 4.1, captured from the 223 software.

F')#e !.1 Pla*e-e% $/ ele*%#$+e6 $ 6*al,

In this system, 1 Standard electrodes, 1 3round 2lectrode, 1 =eference 2lectrode and 1 293

electrode is used as shown in figure 1 above. 2ach electrode is connected to one input of

a differential amplifier $one amplifier per pair of electrodes% a common system reference

electrode is connected to the other input of each differential amplifier. #hese amplifiers amplify

the voltage between the active electrode and the reference.

Standard settings for the (0 $(igh ass 0ilter% and a G0 $Gow ass 0ilter% i.e. 1 ( and ;- (

are set respectively. n additional notch filter is typically used to remove artifact caused by

electrical power lines $6- (%. Since an 223 voltage signal represents a difference between the

voltages at two electrodes, the representation of the 223 channels is referred to as a montage. In

our recordings we used Dipolar #ransverse 'ontage which shows better results for Somnolent

and Sleep Stage in terms of 0re5uency changes producing lpha and #heta Dands.


30/53

fter taking 223 record of the sub/ect, the 223 data is analyed using Drain#ech nalysis

Software provided by Alarity 'edical. #he data is saved in .eeg format defined by Alarity

'edical and the >ideo is saved in .avi format. #o use 223 data in our 'atlab application we

need this data in 2!cel 0ormat so that it can be easily read in 'atlab Software. #o convert the

223 data into 2!cel 0ormat, 223 data converter tool available in Drain#ech nalysis software

is used.

*ow the final output is 223 raw data in 2!cel file having data of 1- Seconds i.e. 6?- samples $

V rate of 6? h% of 1? channels on every sheets and a video file in .avi format.

.2 DATA CONVERSION

program L Excel to MatlabP is written in 'atlab to transfer the 223 data from 2!cel to 'atlab

$.mat% format. #his program re5uires following information to convert the dataO

• Source 2!cel 0ile name

• 7estination Gabview biomedical #oollkit

• *umber of data sheets in 2!cel 0ile

#he 223 raw data from each e!cel sheet having 1- seconds data for selected 1? channels is read

and merged to make a complete data array of 1? channels. *ow the second program .avi is

written in 'atlab that converts >ideo 0ile $.avi format% directly labview toolkit . #his rogram

re5uires following information to runO

• Source 2!cel 0ile name

• Gabview Diomedical toolkit

• s >ideo is not a primary re5uirement of our pro/ect. It is used to note down the

locations where the person feels somnolent so that this information can be passed to

Gabview Diomedical toolkit.

s >ideo is re5uired /ust to mark the somnolent locations so >ideo file with large sie is not

re5uired, and so the sie of the captured frames is reduced to 1F4 th of its original sie. s >ideo is

recorded at rate of 6 frames per second, we are reducing the frames per second also to reduce

sie of output file and we are using every )rd frame and saving this frame into .mat file for further

use using 'atlab.

)-


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.3 MARKING OF SOMNOLENT SAMPLES

&nce the 223 data and relative video data is available in 'atlab format, it is used to note

down the sample position of the somnolent state of sub/ect using L#ake SamplesP utility. In this

utility, each video frame and its respective 223 data is displayed as shown in figure 4..

F')#e !.2 Ta0e Sa-,le6 U%'l'%;

)1


32/53

.! MEAN INTRINSIC MODE FUNCTIONS (IMF6& FROM EEG DATA

#he 'ean Intrinsic mode functions $I'08s% are generated from the 223 data using 2mpirical

'ode 7ecomposition $2'7% process. ll the available fre5uencies are separated using this

process as show in figure 4.)

F')#e !.3 IMF6 e


33/53

. EEG SIGNAL ANALYSIS 7ITH LABVIE7

#he 223 signal that was ac5uired and recorded with 1? channel on different sub/ects is shown in

figure 4.4 and table 4.1.

Table !.1 68$4' %8e +a%a /$# 1 C8ael EEG $l; 2 6a-,le6

))


34/53

F>

9

F!

F!

9

F=

F=

9

F3

F3

9

F5

T!

9

C!

C!

9

C=

C=

9

C3

C3

9

T3

T

9

P!

P!

9

P=

P=

9

P3

P3

9

T

T

9

O2

O2

9

P=

P=

9

O1

O1

T

+4 +) +; 4 4 +4 + - +1 ; +) 6 +; ) 1

+4 + +< 4 ) +) + - + : +4 ? +: ) 1

+ + +11 6 +) +1 +1 + < +4 ; +< 4 1

1 - +14 6 +) +1 +1 + 1- +4 : +1- ) )

1 - +16 ; + +1 +1 + 11 +) 1- +1 ) ?

- +1 +16 1- + 1 +1 +1 + 14 + 14 +1? 1-

1 +) +14 1) + 1 - +1 +1 1? +1 1; +1< 1

) +4 +14 14 + - - +1 +1 1; +1 1; +- 1)

4 +4 +1) 1) 1 +1 - 1 +1 +1 1? + 1? +1: 1

+) +1) 11 1 +1 - - +1 14 - 1? +1; 1 1

+) + +1 1 1 - - 4 - +1 1) 1; +1: - 14

+1- + +1 14 - 1 ? - +1 1) ) 1< +1< - 1;

+14 + +11 1; - - ; - +1 14 4 - +1 +1 1:

+1) +) +1- 1; - +1 ; 1 +1 14 ) 1 +1 +1 1:

+< +4 +11 1? 1 - + ? 1 +1 1? - +- - 1;+; +6 +11 14 1 - + 6 1 +1 1< - + 1?

+: +6 +11 1) 1 - + 4 1 +1 + 4 +4 ) 1;

+11 +4 +< 1) - - +) ) +1 4 +) ? +6 ) 1:

+1 +4 +< 1 + +4 ) + ) +) 6 +6 1:

+? +6 +< 1- +6 4 +6 4 + + ) +) 1;

4 +; +11 ; +: 6 +? 4 +) - + - + 1?

1- +: +11 6 +< 6 +? 4 +4 1< + 1< +- 16

? +: +1- ) +< ) +4 ) +4 1< + 1< +- 1 16

+) +; +: +< 1 + +) 1< +) 1< +- - 16

+1- +? +; - +: +1 - ) + 1; +) 1: +1: - 14

)4


35/53

F')#e !.! S8$4' %8e 6'al /$# 1 *8ael /$# $l; 2 ',)%6 (S)b:e*% 1&

#he analysis of the signal made by using 1--- samples for different electrode combinations as in

figure 4.4 combinations are 0:+04,04+0W +++++++ &1+#6. 0igure 4.6 X #able 4. shows the 1?

channel 223 signal 0or Sub/ect .

#able 4. showing the signal for Sub/ect

F')#e !. 68$4' EEG S'al /$# S)b:e*% 2

)6


36/53

Table !.2 68$4' %8e 6'al /$# S)b:e*% 2

F8

-

F4

F4

- -

FZ

FZ

-

F3

F3

-

F7

T4

-

C4

C4

-

CZ

CZ

-

C3

C3

-

T3

T6

-

P4

P4

-

PZ

PZ

-

P3

P3

-

T5

T6

-

O

2

O

2 -

PZ

PZ

-

O

1

O

1 -

T5

27 17

-

45 22 20

-

20 21

-

21

-

13 4 38

-

13 35

-

44 30 -6

24 7

-

48 27 15

-

16 19

-

18

-

13 10 36

-

14 40

-

42 24 -3

-

38 2

-

22 24 10

-

10 17

-

25

-

14 21 51

-

35 76

-

69 1 15

-

57 1 -4 21 12 -9 17

-

30

-

14 21 66

-

50

10

3

-

96 -8 25

-6 8

-

19 17 18

-

15 19

-

26

-

14 11 52

-

36 69

-

72 9 7

39 13-47 22 17

-17 17

-19

-13 4 36

-16 33

-42 30 -9

29 5

-

47 20 11

-

13 16

-

21

-

13 10 36

-

15 37

-

40 25 -5

-

38 3

-

20 18 11 -8 18

-

18

-

13 21 48

-

28 76

-

68 0 19

-

89 8 -3 33 16 -8 21

-

10

-

13 23 62

-

36

11

3

-

10

3

-

13 40

-

56 10

-

17 36 20

-

13 18

-

16

-

12 13 56

-

30 87

-

87 3 23

21 10

-

43 25 17

-

15 13

-

20

-

11 4 38

-

16 40

-

47 25 -4

45 5

-

47 20 11

-

13 13

-

17

-

11 8 31

-

14 31

-

34 24 -6

-

18 -3

-

25 22 9

-

10 15

-

15

-

12 19 57

-

30 77

-

70 8 19

-

91 -5 -6 35 14 -9 16

-

16

-

12 22 91

-

47

13

3

-

12

3 0 44

-

72 8

-

11 34 21

-

14 18

-

23

-

11 13 78

-

42

10

7

-

10

5 6 30

11 22

-

36 17 18

-

15 15

-

27

-

10 5 40

-

22 43

-

48 20 -2

47 11

-

49 12 0 -5 5

-

18

-

10 6 30

-

14 28

-

32 24 -7

2

-

15

-

33 18

-

13 2 4 -8 -9 12 58

-

27 73

-

70 13 18

-

42

-

20

-

11 28 0 -3 13

-

11

-

10 16 89

-

45

12

6

-

12

0 -1 44

)?


37/53

-

27 3

-

13 23 18

-

12 18

-

25

-

11 13 76

-

43

10

5

-

10

3 1 31

-

13 25

-

34 8 15

-

19 19

-

33

-

10 6 41

-

25 45

-

49 17 -1

-

11 16

-

44 2 -5

-

16 18

-

25 -9 7 28

-

17 26

-

27 21

-

10

-6

-

14

-

26 2

-

10 -8 16 -4 -9 15 49

-

27 63

-

57 10 12

-

47

-

23 -8 16 5 -7 15 1

-

10 18 88

-

44

12

3

-

11

5 -1 45

-

55 -8

-

25 34 16

-

11 15

-

15

-

11 13 87

-

45

11

5

-

11

3 3 39

18 9

-

55 28 12 -8 7

-

24

-

10 7 47

-

26 53

-

56 17 4

42 20

-

56 8 0 1 -4

-

19

-

10 7 26

-

15 25

-

27 22

-

10

#he overall recording of the signal is 46 minutes for one video and the signal is selected from

different time levels. #he best selected 223 signal for 6-- samples from the 1? channels were

presented in the figure 4.?.

F')#e !.(a& EEG 6'al a% %'-e 22-')%e6 11 6e*$+6

);


38/53

F')#e !.(b& EEG 6'al a% %'-e 2@-')%e6 1 6e*$+6

F')#e !.(*& EEG 6'al a% %'-e 1>-')%e6 31 6e*$+6

):


39/53

F')#e !.(+& EEG 6'al a% %'-e 1-')%e6 22 6e*$+6

F')#e !.(e& EEG 6'al a% %'-e 12-')%e6 31 6e*$+6

In figure 4.? the dominance was shown by the signal &1+#6 and #4+A4 as compared to the rest

of the signals. #he 223 signal was obtained by placing the electrodes in the 1-+- electrode placement system.

)


40/53

. PARAMETERS CALCULATED

#he arameters were calculated for all the 1? channels during the ac5uisitions of the 223 signal.

#he parameters are root man s5uare value. Standard deviation, variance, min and ma!. #he

stored data was reoriented in the e!cel. #he values of calculated parameters for different

channels are described in the table 4.),4.4,4.6,4.? and 4.;.

Table !.3 Pa#a-e%e#6 *al*)la%e+ /$# EEG S'al a% 22-')%e6 116e*$+6

EEG

Lea+6 R-6 SD Va#'a*e M' -a< Ae#ae

F8 - F4

4.9008

2

4.90572

8

24.1145

9 -13 11

0.0100

4

F4 - FZ

2.6404

44

2.61633

8

6.85871

5 -9 4

-

0.3755

FZ - F33.419852

3.413028

11.67206 -9 6

0.26506

F3 - F7

4.7435

22

4.74826

7 22.5914 -13 11

-

0.0120

5

T4 - C4

2.9419

91

2.94488

8

8.68981

4 -7 7

0.0180

72

C4 - CZ

2.1889

69

2.17722

9

4.74973

9 -6 5

-

0.2469

9

CZ - C3

2.4057

38

2.40381

7 5.78992 -5 5

0.1445

78

C3 - T3

3.0163

22

2.99570

3

8.99200

8 -11 7

-

0.3775

1

T6 - P4

1.7635

82

1.76007

3

3.10405

4 -6 4

0.1365

46

P4 - PZ

2.6911

87

2.68979

4

7.24950

5 -7 7

-

0.1485

9

PZ - P3

1.6153

08

1.59890

9

2.56153

8 -5 3

0.2409

64

P3 - T5

3.0322

25

2.94513

1

8.69016

5 -7 6

-

0.7349

4

T6 - O2

3.0437

69

2.96904

1 8.832 -7 8

-

0.6847

4

O2 - PZ

4.6061

3

4.56442

6

20.8750

5 -12 11

0.6526

1

PZ - O1

4.5096

2 4.61641

20.4039

7 -10 14

0.1204

82

4-


41/53

O1 - T5

3.7467

42

3.69959

4

13.7137

7 -10 8

-

0.6164

7

Table !.! Pa#a-e%e#6 *al*)la%e+ /$# EEG S'al a% 2@-')%e6 16e*$+6

R-6 SD Va#'a*e M' -a< Ae#ae

F> 9 F! 4.14347 4.14-


42/53

P= 9 P3 1.5705 1.6411; .);


43/53

1

C3 9 T3

6.68456

2

T 9 P!

5.83799

3

P! 9 P=6.78380

7

P= 9 P3

10.2299

2

P3 9 T

6.03380

6

T 9 O2

15.9341

1

O2 9 P=

21.3280

5

P= 9 O1

13.0083

2

O1 9 T

3.56277

8

.5 I%e#,#e%a%'$ $/ %8e EEG S'al A*)'#e+ )6' Va#'a*e9C$a#'a*e Ma%#'<

#he analysis for the interpretation of 223 signal for the drowsiness detection was done with the

help of >ariance+ Aovariance 'atri!. #(2 >=I2*A2 @Aovarience was generated for the raw

data that was calculated with the help of Gabview. #he step by step procedure of calculating

>ariance+Aovariance matri! was represented asO

• #he 6-- samples was calculated for the 22g signal using Gabview.

• #he signal calculated using nQn matri!.

• #he matri! a1 was calculated using the formula a1Ja$$oneQa%Q$1Fn%

"here a J6--Q6-- matri!.

• fter the value of the a1 was derived the >ariance @Aovariance matri! was

calculated by using the formula vJ$a1Qa%Q$1Fn%.

#he calculated >ariance @Aovariance matri! are shown in the table 4.?

F> 9

F!

F! 9

F=

F=

9 F3

F3 9

F5

T!

9

C!

C!

9

C=

C=

9

C3

C3

9 T3

T

9 P!

P! 9

P=

P=

9 P3

P3 9

T

T

9

O2

O2

9

P=

P=

O1

O1

9 T

4)


44/53

F> 9 F! 8.1 0.0

-

0.6

-

4.6 4.3 1.9

-

1.5

-

3.4 1.7 1.8

-

0.5

-

2.0

-

1.3 4.8

-

4.0 1.3

F!

F= 0.0 4.5 0.7 0.0 1.3 1.4 1.3 0.0 1.1 0.6 0.9

-

0.8 0.6 1.1 0.0 0.2

F= 9 F3

-

0.6 0.7 2.3 1.6

-

0.1 0.1 1.8 0.6 0.7 0.5 0.6 0.2 1.3

-

0.1 0.4 0.4

F3 9 F5

-

4.6 0.0 1.6 7.8

-

3.5

-

0.9 2.9 4.5

-

0.2

-

1.0 1.3 2.1 2.8

-

3.9 3.5 0.1

T! 9 C! 4.3 1.3

-

0.1

-

3.5 5.1 1.2

-

1.6

-

2.5 2.6 1.6

-

1.1

-

2.6

-

1.3 5.4

-

4.1 0.3

C!

C= 1.9 1.4 0.1

-

0.9 1.2 2.3

-

0.1

-

0.7 0.5 1.8 0.4

-

0.4 0.8 1.6

-

1.2 1.3

C= 9

C3

-

1.5 1.3 1.8 2.9

-

1.6

-

0.1 4.0 1.6 0.1 0.7 1.7 1.0 1.8

-

0.9 1.7 1.0

C3 9 T3

-

3.4 0.0 0.6 4.5

-

2.5

-

0.7 1.6 5.0

-

1.2

-

0.9 1.7 2.5 2.2

-

4.3 4.4 0.0

T 9 P! 1.7 1.1 0.7

-

0.2 2.6 0.5 0.1

-

1.2 3.3 0.8

-

1.0

-

2.0

-

0.4 4.3

-

3.8 0.7

P!

P= 1.8 0.6 0.5

-

1.0 1.6 1.8 0.7

-

0.9 0.8 4.5 0.5

-

0.6 0.7 4.5

-

3.1 2.9

P= 9 P3

-

0.5 0.9 0.6 1.3

-

1.1 0.4 1.7 1.7

-

1.0 0.5 2.8 1.2 1.7

-

2.3 3.4 0.6

P3 9 T

-

2.0

-

0.8 0.2 2.1

-

2.6

-

0.4 1.0 2.5

-

2.0

-

0.6 1.2 3.9 2.1

-

4.8 4.6 0.5

T 9 O2

-

1.3 0.6 1.3 2.8

-

1.3 0.8 1.8 2.2

-

0.4 0.7 1.7 2.1 4.9

-

4.5 4.1

-

0.2

O2 9

P= 4.8 1.1

-

0.1

-

3.9 5.4 1.6

-

0.9

-

4.3 4.3 4.5

-

2.3

-

4.8

-

4.5

13.

3

-

10.

9 3.8

P= 9

O1

-

4.0 0.0 0.4 3.5

-

4.1

-

1.2 1.7 4.4

-

3.8

-

3.1 3.4 4.6 4.1

-10.

9

12.

3

-

4.0

O1 9 T 1.3 0.2 0.4 0.1 0.3 1.3 1.0 0.0 0.7 2.9 0.6 0.5

-

0.2 3.8

-

4.0 5.1

44


45/53

F')#e !.5 Va#'a*e C$a#'a*e EEG 6'al

F'9!.> Va#'a*e C$a#'a*e EEG 6'al

In the table 4.? the variance @covariance matri! for the different electrode signals indicated that

the diagonal elements represented the variance of the 223 signal for the different electrode

configurations. #he covariance of the 223 signal for different electrode placements is displayed

in the off diagonal elements of the matri!.

In the present research the analysis was done with variance and covariance matri! the

interpretation for the 223 signal was concluded that ma!imum variance and covariance was

showed by 04+0,0W+0) and &1+#6.

46


46/53

CHAPTER

CONCLUSION AND FUTURE SCOPE

.1 CONCLUSION

4?


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method to detect somnolence based on 223 signal analysis using 2'7 and pre trained neural

network has been presented here. #he different features used in this system have been selected

using a utility designed in 'atlab manually on a consistent database. #he best part is to capture

records for testing. s in our study, we have simulated driving condition by a simple method

which was safe and simple in our method, those sub/ects were selected which have travelled for

last :+1- hours and have not slept from last 4 hours. #hese sub/ects were to sit on chair in our

study room facing >ideo 223 camera and are directed to see the camera only. In this way

sub/ects were rela!ed and they were not under stress and further they will get somnolent in a

short span so 223 signals will arise easily from somnolence without any stress.

#he covariance of the 223 signal for different electrode placements is displayed in the

off diagonal elements of the matri!.In the present research the analysis was done with varianceand covariance matri! the interpretation for the 223 signal was concluded that ma!imum

variance and covariance was showed by 04+0,0W+0) and &1+#6.

.2 SCOPE OF FUTURE 7ORK

s we are aware that no system designed is 1-- perfect are there is always space for

improvements. Similarly in our work there are some areas where improvement can be done to

make this system more accurate and perfect. 0ollowing are the areas for future study which can be considered for further research work.

1. In this work we have used 1? 223 Ahannels as input to the *I D'#9 $Diomedical

#oolkit% to give results. Ahannels can be reduced without reducing the performance.

. erformance may be improved by using **.

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[] icot ntoine, Sylvie Aharbonnier, and lice Aaplier, L&n+Gine 7etection ofCsing

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SI'=* Y22# SI*3(, 7=. *>22* 7(IGG&*, =&0. 9='Y22# SI*3(, $-14%P

new pproach for Somnolence 7etection X nalysis Dased &n Gabview,P International

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somnolence detection and analysis based on labview

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