Table Of ContentEEG BRAIN SIGNAL
CLASSIFICATION
FOR EPILEPTIC
SEIZURE
DISORDER
DETECTION
EEG BRAIN SIGNAL
CLASSIFICATION
FOR EPILEPTIC
SEIZURE
DISORDER
DETECTION
SANDEEP KUMAR SATAPATHY
SATCHIDANANDA DEHURI
ALOK KUMAR JAGADEV
SHRUTI MISHRA
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PREFACE
EEGsignalsaretheelectricwavesgeneratedinthehumanbrainwhichisthe
main cause of different tasks performed by a person. These signals are very
small in magnitude but collectively it defines the behavior of a person.
Hence,carefulandcompleteanalysisofthesesignalscansolvedifferentdis-
eases occurring in our brain. High volume and uncertainty of these signals
make it very difficult for any medical person to diagnose a brain disorder
diseasejustbylookingatthegraphicalrepresentationofthesesignals.Hence,
there are plentiful opportunities for computer scientists to produce a
computer-based model that can detect efficiently any specific neurological
disease after proper analysis of these signals. The signals recorded from
human brain contain several features, those needs to be extracted by some
featureextractiontechnique.Afterthisanydiseasedetectionproblemcanbe
solved by the application of different data mining techniques. The classifi-
cation technique is basically used for solving these kinds of problems that
takes the help of different supervised or unsupervised machine learning
techniques.
TheprimaryobjectiveofthisresearchistoclassifytheEEGbrainsignal
for detection of epileptic seizures using machine learning techniques.
Machine learning techniques have recently developed most efficient tech-
niquesforsolvingdifferentkindsofproblems.Wearemainlyconcentrating
on supervised machine learning techniques. These techniques require a
prior knowledge about data that make the machine learn for recognizing
newdataandperformclassificationoperation.Butitisnecessarytoprepro-
cesstherawEEGdatacollectedfrompatientstobringthemintoaformfea-
turesample-baseddataset.Thesignalsareanalyzedbydecompositionusing
DiscreteWaveletTransform(DWT)withDaubechieswaveletoforder2up
tolevel4.Afterthisdecompositiondifferentstatisticalfeaturesfordifferent
coefficient are collected together to build the dataset. We performed an
empirical survey of different machine learning techniques applied for
designing classifier model for epilepsy identification. The different tech-
niquesusedareMultilayerPerceptronNeuralNetwork(MLPNN)withdif-
ferentlearningalgorithms(suchasback-propagation,resilient-propagation,
and Manhattan update rule), different variations of neural network such as
ProbabilisticNeuralNetwork(PNN),RecurrentNeuralNetwork(RNN),
RadialBasisFunctionNeuralNetwork(RBFNN),andsoon,andSupport
vii
viii Preface
Vector Machines (SVM) with different kernel functions (such as linear,
polynomial, and RBF kernel).
Further, we have enhanced the performance of RBFNN network by
implementing different swarm intelligence-based optimization techniques
as training algorithm. Swarm intelligence is one of the most efficient fields
fortheoptimizationproblems.Theswarmintelligencemethodsusedinthis
work are Particle Swarm Optimization (PSO) and Artificial Bee Colony
(ABC) algorithms. This work proposes a novel hybrid technique to train
RBFNNbyoptimizingtheparametersofthisnetworkusingtheimproved
PSOalgorithmandABCalgorithm.PSOisoneofthemosteffectiveopti-
mization technique used so far. But again we have modified the existing
PSO algorithm to enhance the performance by adding a new technique
forfindingthevalueoftheinertiaweightusedinthecalculationofvelocity
update.Theexperimentalevaluationshaveprovedthattheperformanceof
theproposedtechniqueishigherthanRBFNNtrainedwithgradientdecent
approachaswellasRBFNNtrainedwiththeconventionalPSOalgorithm.
WehavealsosuggestedachangetoexistingABCalgorithm,wherewehave
usedtournamentselectioninplaceofroulettewheelselectioninonlookers
beesphase.Besides,theoperationoftheproposedtechniquewasshownto
bemoreefficientbydifferentexperimentalevaluations.Forthiswork,ABC
algorithmwaschosenasmoreefficientalgorithmbecauseofthelessnumber
ofdependableparametersandimprovedaccuracyofRBFNNascompared
to PSO algorithm.
Along with classification accuracy, specificity, sensitivity, and many
other different parameters were considered for measuring the performance
ofclassificationalgorithms.Also,theexperimentalevaluationsaresupported
by a k-fold cross validation technique to validate the results obtained by
different experiments.
CHAPTER 1
Introduction
Electroencephalogramorelectroencephalography(EEG)isatrialperformed
onmentalcapacitytorecordtheelectricalactivityinbrain.Theneuralstruc-
tureofthebrainconsistsofseveralneuronsintermsoflacs.Theseneurons
communicate by colliding among themselves and passing information to
each other. This collision leads to the generation of a very small amount
of electricity. This is utterly different from the general electricity, which
is very high in magnitude. This electric signal flow decides the behavior
of a person. In a human brain the normal stream of electrical signal leads
to a healthy person. And an abnormal electrical signal flow can pass to an
unhealthyperson.Hence,thesesignscanberecordedandanalyzedtosolve
manyneurologicaldisorderdiseases.Thetranscriptionoftheelectricalactiv-
ity is essentially caused by putting electrodes on the scalp for 20–40min,
which evaluates the potential fluctuations in the brain [1]. The nerve cells
inthepsychearetheoriginofelectriccharge,andthentheyexchangeions
withtheextracellularmilieu.Ionsofthesamechargerepeleachotherandin
this manner they are forced out of the neurons when a number of ions are
drivenoutatthesametimetheypromoteeachotherandformawayknown
asvolumeconduction[2].Whenthiswavereachestheelectrodetheypush
or force the ions along the air foil of the electrode which create potential
differenceandthisvoltagedifferencerecordedovertimegivesEEGsignals.
Thekeymotivationbehindthisresearchworkistherapidgrowthinvolume
ofbiological andclinicaldata orrecords.Toextractknowledgefrom these
datawhichcanbeservedtobeaclinicalapplication,therearedifferentdata
analysisdifficultieswhichneedtobeovercome.Manyanalyticaltoolsbased
on machine learning (ML) approaches have been invented to tackle with
suchchallengingtaskofdataanalysisproblems.Around1%ofthetotalpop-
ulation in the world are affected by a neurological disease called epilepsy.
AcarefulanalysisoftheseEEGsignalscansolvemanyneurologicaldisorder
diseases.
EEGBrainSignalClassificationforEpilepticSeizureDisorderDetection ©2019ElsevierInc.
1
https://doi.org/10.1016/B978-0-12-817426-5.00001-6 Allrightsreserved.
2 EEGBrainSignalClassificationforEpilepticSeizureDisorderDetection
1.1 PROBLEM STATEMENT
Nowadays,therecordingofEEGsignalcanbeeasilymanagedwiththeaid
ofvarioushardwareandsoftwaretechniques.Bysimplyseeingatthesesig-
nals with naked eyes one cannot make out any abnormality in the sign.
Hence, the most important problem is to study these signals properly and
extract thehiddenfeatures presentinside.Aneurological diseasecanoccur
in a human brain due to abnormal EEG flow. This abnormality should be
properlyanalyzedtospecifythepatternofthisdiseasethatcanhelpwithpre-
dictionofanysuchtypeofdiseasesinhumanbrains.EEGrecordinggener-
allyleadstothecollectionofahugeamountofnumericalinformation that
consists of the state of electrical activity at different time. This recording is
generallytakenfor10–15transactions.Thisdurationissufficienttounder-
standthestateofahumanbrainwhichleadstocollectionofhugequantities
of data. By plotting this information graphically, we can conclude some
behaviorofthebrain,thoughnotcompletely.Asaresult,itismoresignif-
icant to collect these data and pull information from this. In this research
study,themainconcentrationisontheneurologicaldisorderdiseasecalled
epilepsy.Thewholeproblemofthisresearchworkhasbeenbroadlyclassi-
fied into two groups. First,is feature extraction and analysis of a very non-
stationarysignallikeEEGsignal.Second,istheclassificationofEEGsignals
to detect epileptic seizures.
First,wehavedevelopedawell-definedandwell-structuredprocessfor
extractingthehiddenfeaturesfromaverytransientandnonstationarysignal
likeEEGsignal.Forthisasignaltransformationtechniquecalledasdiscrete
wavelet transform (DWT) was used. To compare the significance of these
extractedfeatures,otherfeaturesbasedonsomemathematicalcomputations
were also extracted.
Second, we have developed a well-defined and most efficient classifier
modelthatcanidentifyanddistinguishepilepticseizuresfromnonepileptic
ones. For this, we have considered mostly ML-based classification techniques
likeANNbasedclassifiers,supportvectormachines(SVM),andevolution-
ary theory-based classifiers.
1.2 GENERAL AND SPECIFIC GOALS
AverycarefulanalysisofEEGsignalcanprovideresponseformanyneuro-
logical disorder diseases. These signals are mainly responsible for different
abnormalitiesgeneratedinthehumanbrain.Decadesagoitwasnotpossible
Introduction 3
tostudythesesignalsduetohighpriceoftheequipmentandnonavailability
of adequate engineering sciences. But nowadays these problems have been
resolvedandmanyhardwareequipmentareavailablewithareasonableprice
forrecordingoftheEEGsignal.Butstilltechnologicalimprovementinthe
depthpsychologyofthesesignalsisgettingalong.Manyresearchersareput-
ting their enormous efforts in this area.
Recording of EEG signal is generally made for approximately
20–30min.Inthatrespectseveralelectrodesareplacedonthehumanscalp
torecordtheseEEGsignals.Normallyaplentyofdataarebroughtforthdur-
ingthisrecording,whichisnotpossibleforamedicalpersontoanalyzethem
in naked eyes. Hence, there are several technologies developed to record
thesesignalsanddirectlyplotthegraphsbyusingsoftware,whichareviewed
byaspecialistandsomeobservationscanbemadeonthestateofthebrain.
Routine EEG is a process which is applied for diagnosis in the following
conditions.
1. To distinguish between epileptic seizures from nonepileptic seizures,
syncope (fainting), subcortical movement disorders, and migraine
variants.
2. To serve as an adjunct test of mind destruction.
3. To prognosticate in certain situations like patients with coma.
4. To determine whether to wean antiepileptic medications.
IncertainsituationstheroutineEEGisnotsufficient,eventuallythepatients
haveseizure.Inthiscase,thepatientsaretakentohospitalandEEGiscon-
stantly recorded. This is mostly done to distinguish between epileptic sei-
zures and other seizures. The most important advantage of EEG is its
speed. A very complex neural activity can be read within a few minutes
orintermsofseconds.EEGproducesaverylittlespatialresolutionascom-
paredtomagneticresonanceimaging(MRI)andpositronemissiontomog-
raphy(PET).ThussometimesEEGimagesarecombinedwithMRIscans.
AccordingtoBickfordetal.[3]thedifferentclinicalapplicationofEEGin
human and animals is used in following situations:
1. Monitor alertness, coma, and brain death.
2. Locateregionsofimpairmentfollowingheadtrauma,stroke,ortumor.
3. Monitor cognitive engagement.
4. Produce biofeedback situations.
5. Control anesthesia depth.
6. Investigate epilepsy and locate the seizure origin.
7. Test epilepsy drug effects.
8. Assist in experimental cortical excision of epileptic foci.
4 EEGBrainSignalClassificationforEpilepticSeizureDisorderDetection
9. Investigate sleep disorder and physiology.
10. Monitoring amobarbital effect during Wada test.
Other than clinical uses there are different applications where EEGs are
extensivelyused.Braincomputerinterface(BCI)isatypicalcommunication
systemthatrecognizesuser’scommandonlythroughtheirbrainwavesand
responds accordingly. It is frequently known as mind machine interface or
brainmachineinterface.Itcanbeusedforpeoplewhoareunabletoexpress
through speechor physicalactivities. The basicfunctionof thesedevicesis
to intercept electrical signals that occur between nerve cells in the brain
and render them into a signal that can be read by an external device. The
different cases of BCI are invasive BCI, partially invasive BCI, and
noninvasive BCI.
Inthisresearchwork,theboundaryhasbeenlimitedonlytotheanalysis
andclassificationofEEGsignalsforepilepticseizureidentification.Approx-
imately 1% of the entire population is touched on by the disease known as
epilepsy.Thehumanbrainisthemostcriticalpartofbodythatcontrolsthe
coordination of human muscles. The transient and unexpected electrical
interferencesinthebrainresultinanacutediseasecalledasepilepsyorepi-
lepticseizure.Itisoneofthemostcommondiseasesaroundtheglobe,strik-
ing more than 40 million people worldwide.
1.3 BASIC CONCEPTS OF EEG SIGNAL
The EEG signal is normally employed for the purpose of recording down
theelectricalactionsofthebrainsignalthattypicallyoriginatesinthehuman
brain.AnEEGsignalismeasuredintermsofcurrentsthatflowduringexci-
tationofsynapticactivitiesofmanyneuronsinthecerebralcortex[4].Dur-
ing this excitation of brain cells, the small magnitude of currents are
produced within the dendrites. As a result, these currents generate a mag-
netic field which can be measured by electromyogram machines.
DepthpsychologyandmeasurementofEEGsignalisappliedfortesting
the stateandactivity of human brain.LikePETandMRI, EEGmethodis
alsousedwidelyduetoitspowerofprovidingbesttemporalresolutionand
lowprice.Humanbrainconsistsofvariousneurons.Whentherewillbeany
flowofinformationintothebrain,theseneuronshiteachother.Duringthis
procedureelectricityisgenerated,whichareverysmallinamountandtran-
sient innature.Thisislikewisecalledasanonstationarysignal,becausethe
frequency of this signal is not set up for a certain amount of time. Biolog-
ically,itcanbeassumedthat,duringtheactivationofbraincells,thesynaptic
currents are created within the dendrites [4].
