A Comparison of Genetic Algorithm & Neural Network (MLP) In Patient Specific Classification of Epilepsy Risk Levels from EEG Signals

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1 Egieerig Letters, 14:1, EL_14_1_18 (Advace olie publicatio: 1 February 007) A Compariso of Geetic Algorithm & Neural Network (MLP) I Patiet Specific Classificatio of Epilepsy Risk Levels from EEG Sigals Dr. (Mrs.) R.Sukaesh, R. Harikumar Member, IAENG Abstract This paper is aimed to compare the performace of a Geetic Algorithm (GA) ad Multi- Layer Perceptro (MLP) Neural etwork i the classificatio of epilepsy risk level from Electroecephalogram (EEG) sigal parameters. The epilepsy risk level is classified based o the extracted parameters like eergy, variace, peaks, sharp ad spike waves, duratio, evets ad covariace from the EEG of the patiet. A Biary Coded GA (BCGA) ad MLP Neural etwork are applied o the code coverter s classified risk levels to optimize risk levels that characterize the patiet. The Performace Idex (PI) ad Quality Value (QV) are calculated for these methods. A group of te patiets with kow epilepsy fidigs are used i this study. High PI such as 93.33% ad 95.83% for BGA ad MLP are obtaied at QV of 0.14 ad Idex Terms EEG Sigals, Epilepsy, Geetic Algorithm, Multi Layer Perceptro, Risk Levels I. INTRODUCTION The recogitio of specific waveforms ad features i the Electroecephalogram (EEG) for classificatio of epilepsy risk levels has bee the subject of much research. Techiques used are raged from statistical methods to sytactic ad kowledge based approaches. All of these methods require the defiitio of a set of features (or symbols ad tokes) to be detected, ad a patter matcher to compare the observed values with the ideal, prototypical oes. A alterative approach, ispired by the Mauscript received o 18 TH July 006 Dr. (Mrs.) R.Sukaesh, Professor & Divisio Head, Medical Electroics Divisio, Departmet of ECE, is with the Thiagarajar College of Egieerig, Madurai, Tamil Nadu, 65015, Idia. (Phoe: ; fax: ; drsukaesh003@yahoo.com). R.Harikumar, Professor, Departmet of ECE, is with the Baari Amma Istitute of Techology-Sathyamagalam, & Research Scholar, Medical Electroics Divisio at Thiagarajar College of Egieerig, Madurai, Idia. (Correspodig author Phoe: ; fax: ; harikumar_rajaguru@yahoo.com). cofiguratio of the huma brai, ivolves the use of artificial eural etworks (ANN). Oe specific ANN architecture is the multilayer perceptro (MLP) feed-forward etwork with oe or more layers betwee the iput ad output odes (hidde layers). Traiig is achieved of MLP is achieved by the back propagatio algorithm, which is a geeralized least mea square algorithm. Most studies of ANNs i epilepsy are focused o spike ad sharp wave form detectio from EEG Sigals. This research focused o classificatio of epilepsy risk levels from EEG sigals through ANNs ad Geetic Algorithm (G.A). The GA is a type of atural evolutioary algorithm that models biological process to optimize highly complex cost fuctios by allowig a populatio composed of may idividuals to evolve uder specific rules to a state that maximizes the fitess. Joh Hollad developed this method i 1975 [1]. May researchers share the ituitios that if the space to be searched is large, is kow ot to be perfectly smooth ad uimodal (i.e., cosists of a sigle smooth hill ), or is ot well uderstood, or if the fitess fuctio is oisy, ad if the task does ot require a global optimum to be foud, i.e., if quickly fidig a sufficietly good solutio is eough a GA will have a good chace off beig competitive with or surpassig other optimizatio methods []. A compariso of GA ad MLP Network as a classificatio ad optimizatio tools for bio medical egieers with a useful applicatio of Epilepsy risk level classificatio is aalyzed. A. Backgroud The Electroecephalogram (EEG) is a measure of the cumulative firig of euros i various parts of the brai. It cotais iformatio regardig chages i the electrical potetial of the brai obtaied from a give set of recordig electrodes. These data iclude the characteristic waveforms with accompayig variatios i amplitude, frequecy, phase etc, as well as brief occurrece of electrical patters such as spidles, sharps ad spike waveforms. EEG patters have show to be modified by a wide rage of variables icludig biochemical, metabolic, circulatory, hormoal, euroelectric ad behavioral factors. I the past, the ecephalographer, by

2 visual ispectio was able to qualitatively distiguish ormal EEG activity from localized or geeralized abormalities cotaied withi relatively log EEG records. The most importat activity possibly detected from the EEG is the epilepsy [6], [7]. Epilepsy is characterized by ucotrolled excessive activity or potetial discharge by either a part or all of the cetral ervous system. The differet types of epileptic seizures are characterized by differet EEG waveform patters [8].With real-time moitorig to detect epileptic seizures gaiig widespread recogitio, the advet of computers has made it possible to effectively apply a host of methods to quatify the chages occurrig based o the EEG sigals. II. MATERIALS AND METHODS The EEG data used i the study were acquired from te epileptic patiets who had bee uder the evaluatio ad treatmet i the Neurology departmet of Sri Ramakrisha Hospital, Coimbatore, Idia. A paper record of 16 chael EEG data is acquired from a cliical EEG moitorig system through 10-0 iteratioal electrode placig method. The EEG sigal was bad pass filtered betwee 0.5 Hz ad 50Hz usig five pole aalog Butter worth filters to remove the artifacts. With a EEG sigal free of artifacts, a reasoably accurate detectio of epilepsy is possible; however, difficulties arise with artifacts. This problem icreases the umber of false detectio that commoly plagues all classificatio systems. With the help of eurologist, we had selected artifact free EEG records with distict features. These records were scaed by Umax 6696 scaer with a resolutio of 600dpi. Sice the EEG records are over a cotiuous duratio of about thirty secods, they are divided ito epochs of two secod duratio each by scaig ito a bitmap image of size 400x100 pixels. A two secod epoch is log eough to detect ay sigificat chages i activity ad presece of artifacts ad also short eough to avoid ay repetitio or redudacy i the sigal [1] [] [3]. The EEG sigal has a maximum frequecy of 50Hz ad so, each epoch is sampled at a frequecy of 00Hz usig graphics programmig i C. Each sample correspods to the istataeous amplitude values of the sigal, totalig 400 values for a epoch. The differet parameters used for quatificatio of the EEG are computed usig these amplitude values by suitable programmig codes. The parameters are obtaied for three differet cotiuous epochs at discrete times i order to locate variatios ad differeces i the epileptic activity. We used te EEG records for both traiig ad testig. These EEG records had a average legth of six secods ad total legth of 60 secods. The patiets had a average age of 31 years. A total of 480 epochs of secods duratio are used. Geeral features of the test records are as follows. Record 1ad 4: High risk level with peaks ad spikes. Record 3 ad6: Patiet uder cliical observatio after two weeks of itesive drug therapy. Record ad 8: Very High risk level with eergy, Peaks ad spikes. Record 5ad 7: Medium risk level with variace, eergy, peaks ad spikes. Record 9ad 10: Low risk level with variace, eergy, peaks ad spikes with occasioal medium risk levels A. Feature Extractio ad Code Coverter System The various parameters obtaied by samplig are give as iputs to the code coverter system as show i fig. 1. These parameters are defied as follows [9], [10], [11]. 1. The eergy i each two-secod epoch is give by E (1) x i i1 Where x i is sigal sample value ad is umber of samples. The ormalized eergy is take by dividig the eergy term by The total umber of positive ad egative peaks exceedig a threshold is foud.3. Spikes are detected whe the zero crossig duratio of predomiatly high amplitude peaks i the EEG waveform lies betwee 0 ms ad 70 ms ad sharp waves are detected whe the duratio lies betwee 70ms ad 00ms. 4. The total umbers of spike ad sharp waves i a epoch are recorded as evets. 5. The variace is computed as give by i 1 ( x ) i xi Where i is the average amplitude of the epoch The average duratio is give by D p ti i 1 p Where t i is oe peak to peak duratio ad p is the umber of such duratios. 7. Covariace of Duratio which is defied as the variatio of the average duratio is CD p i1 D t pd i () (3) (4)

3 The output of code coverter is ecoded ito the strigs of seve codes correspodig to each EEG sigal parameter based o the epilepsy risk levels threshold values as set i the table II The expert defied threshold values are cotaiig oise i the form of overlappig rages. Therefore we have ecoded the patiet risk level ito the ext level of risk istead of a lower level. Likewise, if the iput eergy is at 3.4 the the code coverter output is at medium risk level istead of low level [1]. Fig.1.Block diagram of Geetic Algorithm ad Neural etwork based Classificatio system The average values of extracted parameters i each secods epoch over sixtee chaels of the patiet record 4 is listed i the Table I Table I Average values of Extracted Parameters from Patiet Record 4 Parameters Epoch1 Epoch Epoch3 Eergy Variace Peaks 1 Total Sharp &Spike Total Evets Total Average duratio Covariace With the help of expert s kowledge ad our experieces with the refereces [1],[13],[14], we have idetified the followig parametric rages for five liguistic risk levels (very low, low, medium, high ad very high) i the cliical descriptio for the patiets which is show i table II Table II Parameter Rages for Various Risk Levels B. Code Coverter as a Pre Classifier The ecodig method processes the sampled output values as idividual code. Sice workig o defiite alphabets is easier tha processig umbers with large decimal accuracy, we ecode the outputs as a strig of alphabets. The alphabetical represetatio of the five classificatios of the outputs is show i table III. Table III Represetatio of Risk Level Classificatios Risk Level Normal Low Medium High Very High Represetatio The ease of operatio i usig characteristic represetatio is obviously evidet tha i performig cumbersome operatios of umbers. By ecodig each risk level oe of the five states, a strig of seve characters is obtaied for each of the sixtee chaels of each epoch. A sample output with actual patiet readigs is show i fig. for eight chaels over three epochs. It ca be see that the Chael 1 shows low risk levels while chael 7 shows high risk levels. Also, the risk level classificatio varies betwee adjacet epochs. There are sixtee differet chaels for iput to the system at three epochs. U W X Y Z Risk levels Normal Low Medium High Very high Normalized Parameters Eergy Variace Peaks Evets Sharp waves Average Duratio Covariace This gives a total of

4 forty-eight iput output pairs. Sice we deal with kow cases of epileptic patiets, it is ecessary to fid the exact level of epilepsy risk i the patiet. This will also aid towards the developmet of automated systems that ca precisely classify the risk level of the epileptic patiet uder observatio. Hece a optimizatio is ecessary. This will improve the classificatio of the patiet ad ca provide the EEGer with a clear picture [15]. The outputs from each epoch are ot idetical ad are varyig i coditio such as [YYZXXXX] to [WYZYYYY] to [YYZZYYY]. I this case eergy factor is predomiat ad this results i the high risk level for two epochs ad low risk level for middle epoch. Chael five ad six settles at high risk level. Due to this type of mixed state output we caot come to proper coclusio, therefore we group four adjacet chaels ad optimize the risk level. The frequetly repeated patters show the average risk level of the group chaels. Same idividual patters depict the costat risk level associated i a particular epoch. Whether a group of chael is at the high risk level or ot is idetified by the occurreces of at least oe Z patter i a epoch. Epoch 1 WYYWYYY YZZYXXX YYZXYYY YZZYXYY ZZZYYYY YYZXXXX ZZZYYYY YYYYXXX Epoch WYYWYYY YYYYXXX XZZXYYY WYYYXXX WYZYYYY YYYYXXX Epoch 3 Fig.. Code Coverters Output WZYYWWW YYYXYYY YYYXYYY YZZYYYY ZZZYYYY YYYXZYY The Code coverter s classificatio efficiecy is evaluated from the followig parameters. The Performace of the Code coverter is defied as follows [5], PC MC FA PI 100 (5) PC Where PC Perfect Classificatio, MC Missed Classificatio, FA False Alarm The Performace of code coverter is 40%. The perfect classificatio represets whe the physicia agrees with the epilepsy risk level. Missed classificatio represets a High level as Low level. False alarm represets a Low level as High level with respect to physicia s diagosis. The sesitivity S e ad specificity S p are defied as [17], S e = [PC/(PC+FA)]*100 (6) (0.5/0.6)*100=83.33% S p = [PC/(PC+MC)] *100 (7) (0.5/0.7)*100=71.4% Due to the low values of performace idex, sesitivity ad specificity it is essetial to optimize the out put of the code coverter. I the followig sectio we discuss about the GA based optimizatio of epilepsy risk levels. III. BINARY CODED GENETIC ALGORITHM GA has blossomed rapidly due to the easy availability of low cost but fast speed small computers. The complex ad coflictig problems that required simultaeous solutios, which i past were cosidered deadlocked problems, ca ow be obtaied with GA. However, the GA is ot cosidered a mathematically guided algorithm. The optima obtaied are evolved from geeratio to geeratio without striget mathematical formulatio such as the traditioal gradiet type of optimizig procedure. Ifact; GA is much differet i that cotext. It is merely a stochastic, discrete evet ad a o liear process. The obtaied optima are a ed product cotaiig the best elemets of previous geeratios where the attributes of a stroger idividual ted to be carried forward ito the followig geeratio. The rule of the game is survival of the fittest will wi [3]. A simple geetic algorithm ca be summed up i seve steps as follows [16]: 1. Start with a radomly geerated populatio of chromosomes. Calculate fitess of each chromosome 3. Select a pair of paret chromosomes from the iitial populatio 4. With a probability P cross (the crossover probability of the crossover rate ), perform crossover to produce two offsprig 5. Mutate the two offsprig with a probability P mut (the mutatio probability) 6. Replace the offsprig i the populatio 7. Check for termiatio or go to step Each iteratio of the above steps is called a geeratio. The termiatio coditio is usually a fixed umber of geeratios typically aywhere from 50 to 500 or more. Uder certai other circumstaces, a check for global miimum is doe after each geeratio ad the algorithm is termiated as ad whe it is reached [4].The biary coded geetic algorithm (BCGA) is a type of geetic algorithm that works with a fiite parameter space. This characteristic makes it ideal i optimizig a cost due to parameters that assume oly fiite umber of values. I case of optimizig parameters that are cotiuous, quatizatio is applied. The chief aspect of this method is the represetatio of the parameter as strigs of biary digits of 0 ad 1. This compositio allows simple crossover ad mutatio fuctios that ca operate o the chromosomes. A. Biary Represetatio The five risk levels are ecoded as Z>Y>X>W>U i biary strigs of legth five bits usig weighted positioal represetatio as show i table IV. Ecodig each output risk level gives us a strig of seve chromosomes, the fitess of which is calculated as the sum of probabilities of the idividual gees. For example, if the output of a epoch is ecoded as ZZYXWZZ, its fitess would be

5 Table IV. Biary Represetatio of Risk Levels Risk Biary Code Level strig Very High Z High Medium Low Normal Y X W U =3 1.Operatio o Data Weight 16/31 = /31= /31= /31= /31= = 1 Probability Usig the above represetatio, we have developed a geetic algorithm that optimizes the output of the code coverter ad gives four risk level patters i the five categories for each patiet. This is obtaied by the followig procedure [16] Ope three files havig 16 strigs each ad process stage 1 Divide ito sets of 4 strigs ad iterate 1) Maximum of 18 geeratios ) Two strigs selected radomly 3) Sigle poit crossover after 3 rd positio with probability P cross = ) Radom mutatio of ay positio to ay state i the offsprig with lower fitess ad probability P mut = which is the probability of XXXXXXX 5) Best two strigs with higher fitess get selected for ext stage Stage operates o 4 chromosomes with 8 from each epoch Divide ito sets of 4 strigs ad iterate i same way as stage 1 Output of stage is 4 best strigs i each epoch Fial stage is row-wise optimizatio i which each row of the epochs are iterated ad oe best output is take Last iteratio ivolvig strig of each row gives the fial 4 output strigs B level patters, which defie that of the patiet. This process for a sigle patiet is show i table V. From the table V, each EPOCH3 YYYXYZZ YYYXYZZ ZYYYZZY YYYXXZY YYYYYZY YYYXYYY ZZYZZZZ YYYXYYY ZYYYYYY YYYYXXX ZZYZZZZ ZZYZZZY FINAL STAGE YYYYZZY YYYXYZZ ZZYWYYY ZYYZYYY YYYZYYY ZZYZZZZ ZZYZYZY ZZYYYYZ EPOCH1 EPOCH EPOCH3 FINAL 4 ZYYZYZZ YYYZZYY YYYXZZZ YYYYYZZ ZYYZZZZ YYYZZZZ ZZYXZZZ ZZYYXZZ ZZYZYZY ZZYYYYZ ZZZYZZZ YYYYZZZ YZYYWWZ ZZZYYWW epoch is first reduced to 4 strigs, which give the optimized risk levels of the epoch. A operatio o the 1 strigs i the fial stage by a row-wise optimizatio gives the fial 4 strigs, represetig the risk levels of the patiet. The drawback i this optimizatio as evidet from the table V is that eve though there are lower risk level states i the itermediate stage, they get omitted while proceedig to the fial stage. This is because the algorithm takes oly the higher fitess strigs, which are the strigs that represet the higher risk levels. Sice we deal with oly kow cases of epilepsy, it ca be stated that this is ot a disadvatage, as those states will result i false alarms, which are defied later. It ca also be iferred from the table IV that the mutatio takig place i the iitial stages affects the fial result i oly a small extet. Also, the fial four strigs which are obtaied as the risk levels of the patiet matches with the iitial strigs to a large extet. These advatages of the algorithm outlie its use for the optimizatio of the risk levels of epilepsy. The optimizatio of epilepsy risk levels usig MLP eural etwork is aalyzed i the followig sectio of the paper. Table V. Optimizatio by Biary Geetic Algorithm By the applicatio of the above procedure, the 48 risk level patters obtaied by the code coverter are reduced to 4 risk IV. MULTI LAYER PERCEPTRONS (MLP) NEURAL NETWORK FOR RISK LEVEL OPTIMIZATION `Guoqiag (000) listed out the advatages of the eural etworks i the followig theoretical aspects [6].First, eural etworks are data drive self-adaptive methods i that they ca adjust themselves to the data without ay explicit specificatio

6 of fuctioal or distributioal form for the uderlyig model. Secod, they are uiversal fuctioal approximators i that eural etworks ca approximate ay fuctio with arbitrary accuracy. Third, eural etworks are a oliear model, which makes them flexible i modelig real world complex relatioships. Fially, eural etworks are able to estimate the posterior probabilities, which provide the basis for establishig classificatio ad performace. The primary aim of developig a ANN is to geeralize the features (epilepsy risk level) of the processed code coverters outputs. We have applied differet architectures of MLP etworks for optimizatio. The simulatios were realized by employig Neural Simulator 4.0 of Matlab v.7.0 [4]. Sice our eural etwork model is patiet specific i ature, we are applyig 48 (3x16) patters for each MLP model. There are te models for te patiets. As the umber of patters i each database for traiig is limited, each model is traied with oe set of patters (16) for zero mea square error coditio ad tested with other two sets of patters (x16). After etwork is traied usig these, the classificatio performace of test set is recorded. The testig process is moitored by the Mea Square Error (MSE) which is defied as [19] 1 MSE N N i1 ( O i T j ) Where Oi is the observed value at time i, T j is the target value at model j; j=1-10, ad N is the total umber of observatios per epoch ad i our case, it is 16. As the umber of hidde uits is gradually icreased from its iitial value, the miimum MSE o the testig set begis to decrease. The optimal umber of hidde uits is that umber for which the lowest MSE is achieved. If the umber of hidde uits is icreased beyod this performace does ot improve ad soo begis to deteriorate as the complexity of the eural etwork model is icreased beyod that which is required for the problem. Multilayer perceptros (MLPs) are feed forward eural etworks traied with the stadard back propagatio algorithm [0]. To reduce the traiig time, a advaced NN traiig algorithm, called Leveberg-Marquardt (LM) is used. This traiig algorithm is based o the Gauss-Newto method, ad it reduces the traiig time dramatically. It provides a fast covergece, it is robust ad simple to implemet, ad it is ot ecessary for the user to iitialize ay strage desig parameters. It out performs simple gradiet descet ad other cojugate gradiet methods i a wide variety of problems [1]. For a stadard back propagatio algorithm, we used a approximate steepest descet rule ad updated the weight accordig to the followig equatio: E( k) W ( k 1) W ( k) W ( k) W ( k) (9) (10) Where W (k) is the weight at the k th iteratio, α is the learig rate, (k) is the differece betwee NN output ad the expected output. W (k) is the weighted differece betwee the k th ad (k-1) th iteratio (this item is optimal), ad is the mometum costat. I some adaptive algorithms, α chage with time, but this requires may iteratios ad leads to a high computatioal burde. Fortuately, the o-liear least squares Gauss- Newto has bee used to solve may supervised NN traiig problem. Whe Gauss-Newto update rule is employed to batch traiig, the solutio provides iteratively as [5]: (11) W( k 1) W( k) W ( k), k 0,1,... Where W(k) deotes the NN weight vector at the kth iteratio ad W(k) is the chaged weight. W(k)is computed from: mi J ( k) W ( k) e( k) (1) ie., J(k) T J(k) W(k)=- J(k) T e(k) (13) Where Where. ei( K) J ( k) Wj( k) is the Euclidea orm. e(k)=[e 1 (k),. e m (k)] T with e i (k)=y i (k)- t i (k); (14) ad W(k)=[w 1 (1,1) w (1,) w 1 (S1,R) b 1 (1) b 1 (s1) w (1,1) b M (SM)] T (15) The above derivatio is the essece of the Gauss-Newto algorithm. However, the Gauss-Newto is geerally ot locally coverget o problems that are very oliear or have very large residuals. To improve upo this situatio, the followig formula is usually adopted: (J(k) T J(k)+ S T S)W(k)= - J(k) T e(k) (16) Where S R x is a o sigular matrix ad is a coefficiet. The searchig directio obtaied from this formula varies as chages. The Leveberge-Marquardt algorithm is based o this method ad replaces S with the idetity matrix ad update weights ca be obtaied. The results of the MLP back propagatio eural models traied with the Leveberg-Marquardt (LM) learig algorithm are show i table VI. For all the models, the maximum umber of iputs is (1x16) ad the umber of output is oe, correspodig to the optimized risk level patter. Takig ito accout the problem uder cosideratio, ANN architectures with three layers were used, as these models have bee documeted as able to draw the boudaries of arbitrarily complex decisio regios. The umber of weights, the gai or learig rate η (0.3), mometum α (0.5), ad traiig epochs are tabulated for each model. Durig the traiig phase, a error measure (9) of the closeess of the weights to a solutio ca be calculated for each patter (16 iput feature patters) that represets a subject i the traiig set. This measure is used for determiig whether a certai subject has bee leared by the system. Table VI. Estimatio of MSE i Various MLP Network

7 Architectures Architecture Traiig Mea Square Error (MSE) Idex Epochs Traiig Testig E E E E E E E E E-08 1.E-08 I the MLP etworks testig MSE idex ad umber of epochs used for traiig are iversely proportioal to each other. Therefore a compromise betwee them was achieved by takig ito the cosideratio of larger traiig cost will rui the system eve though cosiderable accuracy is achieved i the targets (epilepsy risk levels) [],[3]. Therefore we had selected MLP etwork architecture which requires lesser umber of traiig epochs ad the same is depicted i the fig. 3. Fig. 3. Traiig of MLP Feed forward Neural Network (4-4-1) V. RESULTS AND DISCUSSIONS The outputs are obtaied for three epochs for every patiet i classifyig the epileptic risk level by the code coverter, Geetic algorithm, ad MLP eural etwork approaches. To study the relative performace of these systems, we measure two parameters, the Performace Idex ad the Quality Value. These parameters are calculated for each set of the patiet ad compared. A. Performace Idex The PI calculated for the aforesaid classificatio methods are illustrated i table VII usig (5) Methods Code coverter BCGA optimizatio MLP Optimizatio It is evidet that the optimizatios give a better performace tha the code coverter techiques due to its lower false alarms ad missed classificatios. For code coverter classifier we have max detectio of 50% with false alarm of 10%.Similarly for BCGA ad MLP Neural etwork optimizatios we obtaied perfect detectios of 93.75%ad 95.83% with false alarms of 6.5% ad 4.16%. This shows that the BCGA ad MLP eural etwork classifiers are performig better tha the sigle code coverter classifier. B. Quality Value Perfect Classifica tio Missed Classificati o False Alarm Performa ce Idex The goal of this paper is to classify the epileptic risk level with as may perfect classificatios ad as few false alarms as possible. I Order to compare differet classifiers we eed a measure that reflects the overall quality of the classifier [15].Their quality is determied by three factors, Classificatio rate, Classificatio delay, ad False Alarm rate. The quality value Q V is defied as [5], Q V R 0.* T * P 6* P fa C dly (17) Where, C is the scalig costat, R fa is the umber of false alarm per set T dly is the average delay of the o set classificatio i secods P dct is the percetage of perfect classificatio ad P msd is the percetage of perfect risk level missed. A costat C is empirically set to 10 because this scale is the value of Q V to a easy readig rage. The higher value of Q V, the better the classifier amog the differet classifier, the classifier with the highest Q V should be the best. The two differet approaches give differet results. Hece a comparative study is eeded whereby the advatages of oe over the other ca be easily validated ad the best method foud out. A study of code coverter method without ad with BCGA optimizatio was performed ad their results were take as the average of all te kow patiets was tabulated i table VIII. Table VIII. Results of Classifiers Take As Average of All Te Patiets dct msd Table VII. Performace Idex

8 Parameters Code Coverter Method Before Optimizati o Biary Coded Geetic Algorithm VI. CONCLUSION This paper aims at classifyig the epilepsy risk level of epileptic patiets from EEG sigals. The parameters derived from the EEG sigal are stored as data sets. The the code coverter techique is used to obtai the risk level from each epoch at every EEG chael. The goal was to classify perfect risk levels with high rate of classificatio, a short delay from oset, ad a low false alarm rate. Though it is impossible to obtai a perfect performace i all these coditios, some compromises have bee made. As a high false alarm rate ruis the effectiveess of the system, a low false-alarm rate is most importat. Geetic algorithm ad Neural etwork (MLP) optimizatio techiques are used to optimize the risk level by icorporatig the above goals. The spatial regio of ormal EEG is easily idetified i this classificatio method. The major limitatio of GA method is that if oe chael has a high-risk level, the the etire group will be maximized to that risk level. This will affect the o-epilepsy spike regio i the groups ad for NN its additioal traiig cost ivolves i the learig procedures of the etwork. However, the classificatio rate of epilepsy risk level of above 90% is possible i our method. The missed classificatio is almost 0% for a short delay of secods. The umber of cases from the preset te patiets has to be icreased for better testig of the system. From this method we ca ifer the occurrece of High-risk level frequecy ad the possible medicatio to the patiets. Also optimizig each regio s data separately ca solve the focal epilepsy problem. This risk level classificatio of diabetic epileptic patiets may also be take as further extesio of this paper. VII. ACKNOWLEDGEMENT MLP Neural Network Risk level classificatio rate (%) Weighted delay (s) False-alarm rate/set Performace Idex % Quality value The authors thak the Maagemet ad Pricipal of Baari Amma Istitute of Techology, Sathyamagalam, ad the Maagemet of Thiagarajar College of Egieerig, Madurai for providig excellet computig facilities ad ecouragemet. The authors are grateful to Dr.Asoka, Neurologist, Ramakrisha Hospital Coimbatore ad Dr. B.Rajalakshmi, Diabetologist, Govt. Hospital Didigul for providig the EEG sigals. REFERENCES [1] K.F. Ma, Geetic Algorithms: Cocepts ad Applicatios, IEEE Trasactios o Idustrial Electroics, vol,43o,5 October 1996, pp [] Rady L. Haupt ad Sue Elle Haupt, Practical Geetic Algorithms, Joh Wisely ad Sos, [3] D.E.Goldberg, Geetic Algorithms i Search, Optimizatio ad Machie Learig, Readig, MA: Addiso-Wesley, [4] Melaie Mitchell, A Itroductio to Geetic Algorithms, A Bradford Book MIT Press, [5] Aliso A Digle, A Multistage system to detect epileptic form activity i the EEG, IEEE Trasactios o Biomedical Egieerig, vol, 40, o, 1, December 1993, pp [6] Mark va Gils, Sigal processig i prologed EEG recordigs durig itesive care, IEEE EMB Magazie, vol, 16,o,6, November/December 1997, pp [7] Haoqu ad Jea Gotma, A patiet specific algorithm for detectio oset i log-term EEG moitorig-possible use as warig device, IEEE Trasactios o Biomedical Egieerig vol,, 44,o, February 1997,pp [8] Arthur C Gayto, Text Book of Medical Physiology, Prism Books Pvt. Ltd., Bagalore, 9 th Editio, [9] Seugha Park, TDAT Domai Aalysis Tool for EEG Aalysis, IEEE Trasactios o Biomedical Egieerig, vol,37,o,8, August 1990,pp [10] Yuhui Shi, Implemetatio of Evolutioary Fuzzy systems, IEEE Trasactios o Fuzzy Systems, vol,7,o,, April 1999, pp [11] R.Neelavei ad G.Gurusamy, EEG Sigal Aalysis Methods A Review, Proceedigs of Natioal System Coferece NSC-98,, 1998, pp [1] R.Harikumar ad B.Sabarish Narayaa, Fuzzy Techiques for Classificatio of Epilepsy risk level from EEG Sigals, Proceedigs of IEEE TENCON 003, Bagalore, Idia, October 003, pp [13] R.Harikumar, Epilepsy Detectio From EEG Sigals A Statistical Approach, Proceedigs of ICECON 03, NIT Trichy, 5 6 December 003. [14] P.Aravida Bharathi ad V.Beea, Aalysis of Statistical Tests ad Fuzzy Techiques i Diagosis of Epilepsy from EEG sigals, Proceedigs of ICSCI 004, Hyderabad, 1 15 February 004, pp [15] Chig-Hug Wag, Itegratig Fuzzy Kowledge by Geetic Algorithms, IEEE Trasactios o Evolutioary Computatios, vol,,o,4, November 1998, pp [16] Marco Russo, Fu Ge Ne Sys A Fuzzy Geetic Neural System for Fuzzy Modelig, IEEE Trasactios o Fuzzy Systems, vol,6,o,3,august 1998, pp [17] Ragaraj M. Ragayya, Bio- Medical Sigal Aalysis A Case Study Approach, IEEE Press-Joh Wiley &sos Ic New York 00. [18] Hor, J.Goldberg, D.E ad Deb,K. Log path problems. I Y.Davidor, H.P.Schwefel ad R.Mäer (Eds.), Lecture Notes i Computer Sciece 866-Parallel Problem Solvig from Nature-PPSNIII, Iteratioal Coferece o Evolutioary Computatio, The Third Coferece o Parallel Problem Solvig from Nature, 1994,pp [19] Nuretti Acir etal., Automatic Detectio Of Epileptiform Evets I EEG By A Three- Stage Procedure Based o Artificial Neural Networks,IEEE trasactio o Bio Medical Egieerig,vol.5,o.1,Jauary 005, pp

9 [0] Draze.S.etal., Estimatio of difficult to- Measure process variables usig eural etworks, Proceedigs of IEEE MELECON-004, 004, pp [1]. Moreo.L.etal., Brai Maturatio Estimatio Usig Neural Classifier, IEEE Trasactio of Bio Medical Egieerig, vol.4, o.,1995, pp []. Tarasseko.L, Y.U.Kha, M.R.G.Holt, Idetificatio Of Iter-Ictal Spikes i the EEG Usig Neural Network Aalysis, IEE Proceedigs Sciece Measuremet Techology,vol.145,o.6,1998, pp [3]. Yua-chu Cheg, Wei-Mi Qi,WeiYou Cai, Dyamic Properties of Elma Ad Modified Elma Neural Network, Proceedigs of IEEE, First Iteratioal Coferece o Machie Learig Ad Cyberetics, Beijig,00,pp [4] H.Demuth ad M.Beale, Neural Network Tool Box: User s Guide, Versio 3.0, The Math Works, Ic., Natick, MA, [5] Li Gag etal, A Artificial Itelligece Approach to ECG Aalysis, IEEE EMB Magazie,vol 0, 000, pp [6] Guoqiag Peter Zhag, Neural Networks for Classificatio: A Survey, IEEE Trasactio o Systems, Ma Ad Cyberetics-Part C, vol.30 No.4, November 000, pp i Iteratioal ad Natioal Jourals ad also published aroud thirty papers i Iteratioal ad Natioal Cofereces coducted both i Idia ad abroad. His area of iterest is Bio sigal Processig, Soft computig, VLSI Desig ad Commuicatio Egieerig. He is life member of IETE ad ISTE. Dr. (Mrs.) R.Sukaesh Dr.(Mrs.).R.Sukaesh received her bachelor s degree i ECE from Govermet College of Techology Coimbatore i198. She obtaied her M.E (Commuicatio Systems) degree from PSG College of Techology, Coimbatore i 1985 ad PhD i Bio Medical Egieerig from Madurai Kamaraj Uiversity, Madurai i 1999.Sice 1985 she is workig as a faculty i the Departmet of ECE at Thiagarajar College of Egieerig, Madurai ad presetly, she is Professor of ECE ad Heads the Medical Electroics Divisio i the same College. Her mai research area icludes Biomedical Istrumetatio, Neural Networks, Bio Sigal Processig ad Mobile Commuicatio. She is guidig twelve PhD theses i these areas. She has published several papers i Iteratioal ad Natioal Jourals ad also published aroud sixty papers i Iteratioal ad Natioal Cofereces coducted both i Idia ad abroad. She has delivered a umber of ivited lectures i various Uiversities. She has a Diploma i Higher Learig ad has co authored a book o Gadhia Thoughts. She is life member of Bio medical Society of Idia, Idia Associatio of Bio medical Scietist ad ISTE. R.Harikumar R.Harikumar received his B.E (ECE) degree from REC Trichy i 1988.He obtaied his M.E (Applied Electroics) degree from College of Egieerig, Guidy, Aa uiversity Cheai i He has 15 years of teachig experiece at college level. He worked as faculty i the Departmet of ECE at PSNA College of Egieerig & Techology, Didigul. He was Assistat Professor i IT at PSG College of Techology, Coimbatore. He also worked as Assistat Professor i ECE at Amrita Istitute of Techology, Coimbatore. Curretly, he is Professor of ECE, Baari Amma Istitute of Techology, Sathyamagalam ad he is pursuig doctoral programme i Bio-Medical Egieerig uder guidace of Dr. (Mrs.) R.Sukaesh at Departmet of ECE, Thiagarajar College of Egieerig, Madurai. He has published twelve papers