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Student Conference on Research and Development (SCOR eD) 2003 Proceedings, P utrajaya, Malaysia

Expert System for Characterization of Electroencephalography (EEG)

Signals to Detect Sleep Onset

F. A. Hamid', A. Hussain', 'A . Mohamed', M. A. Mohd Alii,B . Yeop Majli;', R. A. AI?, and A. S . Mohamed4

'EESE Dept., Faculty ofEngineEririf;g Institute of Microengineering and N anoelectronics

Universiti Kebangsaan Malaysia, 43600 UKM B angiSelango r Dam1 Ehsan, MalaysiaDept. o f Othorhinolaryngology& 'Dept. of Medicine

Faculty o f Medicine, Universiti Kebangsaan Malaysia56000 Cheras, Kuala Lumpu r, Malaysia

e-maiI:i m a h ~ v 1 s i . c n e . u k n i . n ~ ~ ~ini(ii2eiie.~IkIn,iny, z a h ~ ~ c u g . ~ ~ k n i . m p ,

mam@enq.ukm.my & burliau(iilenq. ikmmy

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Abstract- This paper describes the development of an

approach that uses expert system to differentiate various

stages of sleep: i.e. from awake state to stage I1 sleep. An

expert system that performs sleep staging using the' characteristics analysis of the human sleeps.

electroencephalography (EEG) signals is presented. The

' Kappa PC expert system shell, the frequencies andwaveforms characteristics of the EEG signals were usedin he implementation ofth e sleep on set detection.

Keywords

Driver drowsiness, sleep onset, expert system,

electroencephalography.

I. INTRODUCTION

Driver drowsiness is a n important risk on the roads inwhich it is one of he main factors leading to accidentsor near-missed accidents, This fact has been proven by

many studies that have established links between driverdrowsiness and road accidents. Therefore, it is crucial to

develop methods so as to reduce risks of accidents dueto drowsiness.

Presently, several studies have tried to model thebehavio r of drowsy driver by e stablishing links between

drowsiness and certain parameters related to vehicle as

well as the driver [ I ] &[2]. Sleep onset detection can be

used to prevent driver drowsiness that gives majorimplication in transportation system safety. EEG signal

has been found to be the mast predictive and reliable

indicator of detecting the onset of sleep [11.

Therefore, it is important to understand thecharacteristics of EEG signal in order to determinewhen the drowsiness begins.

LL. EXPERIMENTALSETUP

In this work, the data from three normal persons agedbetween 23 and 31 years were recorded using samplingrate of 25 6 Hz. Subjects were requested to performbrain-related activities such as reading and perfodng

simple calculation. No filtering was apphed to the data

during recording. The subjects were seated in a sound-

controlled room with dim lighting and air-conditioner

unit switched on for providing extra comfort to the

subjects.

Subjects were also asked to abstain from taking

excessive caffeine and naps on the day prior to testing.Additionally, they were asked to reduce their sleep

during the night prior to testing so that subjec ts will bedeprived from their sleep. Eighteen electrodes are

placed over the scalp and connected by wires to an EEGmachine. Surface electrodes are placed at specificlocations on the subjects scalp as recommended by

lnternationnl Electrocnc~pphalograpli~ rid ClinicalNeurophysiology (SEEGCN).

The system is known as the '10-20 System' since it is

derived by 10% and 20 % measurements relative to four

scalp landmarks [3]. The signals from he brain are verysmall (measured in pV) and therefore, amplification isrequired before the electrical signals are displayed and

stored. To classify the EEG signals, the power spectraldensity (PSD) s extracted for values within the range of

the delta, theta, alpha and beta waves i.e. from 0.5 to 30

Hz.

No pre-filtering or artifact removal was applied to the

data. Fig. l(a) shows the EEG signal as recorded by theMEDELECmachine while Fig. 1 (b) depicts the I second

duration of the single channel plot for T5-01 lectrode.

In Fig. l(c) and (d), the Fast Fourier Transform (FFT)plot and PSD plot are shown, respectively.

0-7803-8 173-4/03/$17.00 02003 IEEE.5

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II

II

I

\4\

I

,

Fig. 1. (a) Recorded EEG signal, (b) single channel plotfrom TS-01, (c) FFT plot, and (d) PSD plot

111. DESIGNAND MPLEMENTATION

Expert system is a field of Artificial Intelligence (AI)

that combines information from previous experiences sothat it can replace the expert role in the form of updatedinformation based on the expert knowledge [4].Due toits long-list of advantages, we decided to adopt it forsolving the problem in classifying the EEG signals. The

developed object-oriented expert system characterizesthe EEG signals so that the sleep onset can be detected.Th e KAPPA-PC expert system shelf hasbeen used since

it has in-house reasoning models and mechanisms tohelp speed up the expert system development [SI.Th e

results from the expert system are displayed graphicallyusing a window interface.

Initial work involves the conce ptual design of the expertsystem for characterization of the EEG signaIs to solvethe sleep on set detection that can be related to driver

drowsiness problem. An object-oriented approach isused to develop the d e s since it allows greaterflexibility in the overall design of the system. Rules are

simple means of representing knowledge and are usedfor reasoning with the data in the objects. Ten rules

were constructed, part of the rules are listed below inFig. 2,

RULE 1:

I f Data:Max-Freq * 3 And

Then (Ce1Beta:Betn= beta;

Dnta:Max-Frcq 30;

AppcndToList(

CharacterizatianOfEEG:ElectrodeBeta,

Chnracterization0fEEG:Electrode ):

reset2( );};

RULE 7:

If Data:Max_Freq+ And

Data:Max-Freq < 13;Then (GelA1pha:Alpha =alpha;

AppendToList(

CharacterizatioaOfEEC:ElcctrodcAIpha,Charncterlzntion0fEEG:Electrode :

reset2( );};.

i

RULE 10:

LengthList( Charncterizatian0fEEC:ElectrodeOther ) 5

LengthList( Charscterization0EEG:ElectrodeAlpha )

And LengthList( Characterization0l'EEC:ElectrodeOther

)>LengthList( Characterization0fEEEC:ElectrodeTheta

And LengthList( Chnrncterizntion0lEEC:ElectrodcOthcr

1'LengthList( Charactcrization0CEEG:ElcctrodeBeta AndLengthList( Charaetcrization0lEEG:EfectrodcOther >LengthList( Charaeterization0lEEG:EtectrodeOther ;

Then DisplayTextt Transcriptl, "DATA IS OUT OFFREQUENCY RANGE." ;

i r

Fig. 2. Part of the rules forEEG classification

task

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INTRODUCTIONESINTRODUCTIQN 9

INOIWDUAL NO

I Y E S IC H O O 5

ELECTRODE

INDlV lDUAL

TESTING

(USING RULE 1 -5 )I . I

TESTING

OUTPUT

Fig. 3. Flowchart of the designed expert system

IV.THE DEVELOPED SOFTWARE

The flowchart of the developed software system, whichis self-explanatory, is shown in Fig. 3.  The windowinterface for the main program is shown in Fig. 4(a).

Upon starting this program, user needs to load a data

and later has an option to either run a sequent ofanalysis interactively on the entire data using SingleChannel Analysis or perform it automatically at one go

using Overall AnaIysis. Fig. 4(b) shows a dialog promptfor data loading while Fig. 4(c) shows a dialog prompt

for the analysis options.

Which type of analysisyou want to proceed?

ESINolE C H A N i h i k N A L W I 3 -

p-miJ"EwTmt

( c)Fig. 4.Window interface (a) Main Menu (b) Input Data

and (c) Analysis option

Fig. 5 . Single channel analysis window interface

There are 18 electrode channels to choose from anddetail analysis will be performed upon request. Thelocation of each electrode is given in the montage view

of Fig. 5. Pressing the 'ANALYSE' button will trigger

the analysis and th e ES software will prompt the result

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of the EEG signal anatysis in terms of its brain wave

type. Th e brainwave type, its quantity and duration willthen be tested against the rules to determine the subject

awakehleep condition. Table 1 lists the frequency rangeof various brainwave types, its category and the subject

awakehleep conditionsaccordingly.

Changes of the subject brain state can be observed byperforming analysis on every consecutive input data.The level of drowsiness can be determined by looking at

various types of brainwaves activities and the way they

change from one state to another. For instance, as the

brainwave descends from beta to alpha, alpha to theta

and finally from theta to delta indicate that the subject

brain state has changed from the, alert to drowsy state

and finally fall asleep. In this work, drowsiness is said

to occur when the brainwave descends from beta toalpha, which is defined as the onset of sleep Table 2 

lists.

Table 1. Fre uencFrequency

NameFrequency correspond

tothe following

ran e Hz

ide for brainwave classification

0.5 - 4 Delta -Sleep/Dreaming-Picsent during transition to drowsiness and

TIable 2

t-rowsy

Stage 2

during slccp.-Drowsy/ Idling-Low level of alertness during drowsin ess.

-Present during an ale n and relaxed state.-Occur during wakerulness, panicularly overthe occipital cortex.

-Appears markedly ai eye closure nnd

decreases at eye opening.

-Highly stteouated during attention- Occur while performing. reaction-time

motor rask.-Increased of alertness, arousal andexcitement.

kleep stage and its characteristics

Characterization

-Alert.-Low-amplitude EEG.

-Fait eye movements.-Movement andlor technical artifacts.

-Present f slow eye movements

with occipital alpha rhythm.

-A decreas e in the amplitude and/or frequency ofthe alpha

rhythm.

-Low-amplitudeactivity at the central andaosteriorEEG

channels.

-Theta activity i n the central EEG (Non-REM).

-Sleep spindles and K-complexes detecled, thetaand delta activity prominent in the central EEG.

IV. CONCLUSION

An expert system for characterization of EEG signal toperform sleep onset detection has been developed usingobject-oriented programming principles. The Kappa-PCexpert system shell has been chosen to achieve the goal.

The developed expert system is able to diagnose and

.. I ..

. . . .

classify the EEG signal correctly according to its

frequency range. Typically, EEG signals can be

categorized according to the frequency bands, which are

made of beta, alpha, theta and delta waves. As thebrainwave changes from beta (highest frequency band)

to delta (lowest frequency band), the sleep onset can be

determined.

V. ACKNOWLEDGEMENT

Th e authors gratefdly acknowledge the financial

support of the Malaysian Ministry of Science,

Technology and Environment (MOSTE) an d UKM for

financing this project under IRPA code 03-02-02-

00 7/SR0003/07-03. A special acknowledgement goes

to Encik Anuar Ithnin of HUKM for his technical

assistance during and after data recording.

VI. REFERENCES

[l] La1 S.K.L. and Craig A. “A critical review of the

psychophysiology of driver fatigue.”BioIogicalPsychology. 2001.55: 173-194.

[2] Robert D. Peters, Esther Kloeppel, Elizabeth

Alicandri, Jean E. Fox, Maria L. Thomas,David R.’ Thome, Helen C. Sing, and Sharon M.

Balwinski. ”Effects of Partial and Total SleepDeprivation On Driving Perfomnce.”(onIine)

l~M://www.fhrc.~ov/htmianfac/sleep/slee~web.htm

(1 1Nov. 2002).133 Frances M. Dyro. “The EEG Handbook”. United

States of America. 1SBN:OJ16-20019-0. 1989.

[4] John Durkin, ”Expert Systems Design And

Development”, Englewood Cliffs, NJ: Prentice-Hall, 1994.

[SI Intellicorp Inc. “Kappa-PC 2.4 Online Manual”.United States of America: Microsoft Corp, 1998.[63 Sydney Louis, MD. 2001.’Woml EEG

Waveforms. E-Medicine,”(odine)S I (7

October 2002).

[7 ] K. irvonen, J. Basan, V. Hakkinen, A. Varri, P.

Loula.”The detection of drowsiness and sleep onset

periods from ambulatory recorded polygraphic

data.” Electroencephalography and cIinical

Neurophysiology, 1997. 102: 132-137.

.

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