A Patient Specific Neural Networks (MLP) for Optimization of Fuzzy Outputs in Classification of Epilepsy Risk Levels from EEG Signals

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1 Egieerig Letters, 3:, EL_3 (Advace olie publicatio: 4 August 006) A Patiet Specific s (MLP) for Optimizatio of Fuzzy Outputs i Classificatio of Epilepsy Risk Levels from EEG Sigals Dr. (Mrs.) R. Sukaesh, ad R.Harikumar, Member, IAENG Abstract I recet years s have bee widely used as patter ad statistical classifiers i bio medical egieerig. Most research to date usig hybrid systems (Fuzzy-Neuro) focused o the Multi-Layer Perceptro (MLP). Here we focus o MLP etwork as a optimizer for classificatio of epilepsy risk levels obtaied from the fuzzy techiques usig the EEG sigal parameters. The obtaied risk level patters from fuzzy techiques are foud to have low values of Performace Idex (PI) ad Quality Value (QV). The eural etworks are traied ad tested with 480 patters extracted from three epochs of sixtee chael EEG sigals of te kow epilepsy patiets. Differet architectures of MLP etwork was compared based o the miimum Mea Square Error (MSE), the better MLP etwork (-4-) were selected. The MLP etwork out performs the fuzzy techiques with high Quality Value (QV) of 5 whe compared to low QV of 6.5. Idex Terms EEG Sigals, Epilepsy, Fuzzy Logic, Multilayer Perceptro s, Risk Levels I. INTRODUCTION Epilepsy, a disease kow from aciet times, was believed to be give by the Gods ad it is ow cosidered as a widow to the brai s aatomy ad fuctio ad is, therefore, a icreasigly active iterdiscipliary field of research. The highest icidece of epilepsy occurs i ifat ad i the elderly. This is due to geetic abormalities, developmetal aomalies, febrile covulsios, as well as brai craiofacial trauma, cetral ervous system ifectios, hypoxia, ischemia ad tumors []. The hallmark of epilepsy is recurret seizures. The seizures are due to sudde developmet of sychroous euroal firig i the cerebral cortex ad are recorded by electrodes o the scalp. Electroecephalographic (EEG) recordigs from the epileptic brai show that these discharges may begi locally i portios Mauscript received March 9, 006. (Write the date o which you submitted your paper for review.) Dr. (Mrs.) R.Sukaesh, Professor & Divisio Head, Medical Electroics Divisio, Departmet of ECE, is with the Thiagarajar College of Egieerig, Madurai, Tamil Nadu, 6505, Idia. (Phoe: ; fax: ; drsukaesh003@yahoo.com). R.Harikumar, Research Scholar, Medical Electroics Divisio, Departmet of ECE, is with the Thiagarajar College of Egieerig, Madurai, Tamil Nadu, 6505, Idia. (Correspodig author Phoe: ; fax: ; harikumar_rajaguru@yahoo.com). of the cerebral hemispheres. Geeralized seizures cause altered cosciousess at the oset ad are associated with a variety of motor symptoms, ragig from brief localized body jerks to geeralized toic-cloic activity. Seizures come ad go, i a seemigly upredictable way. I some patiets, seizures ca occur hudreds of times per day; i rare istaces, they occur oly oce every few years. The Electroecephalogram (EEG) is a recordig of the electrical potetials geerated by the brai. Typically, sixtee chaels of data are recorded by measurig the potetial differece betwee pairs of electrodes placed o the scalp []. EEG recordig is a routie cliical procedure that provides iformatio pertiet to the diagosis of a umber of brai disorders, particularly epilepsy. Betwee seizures, the EEG of a patiet with epilepsy may be characterized by occasioal epileptic form trasiets-spikes ad sharp waves [3]. EEG patters have show to be modified by a wide rage of variables icludig biochemical, metabolic, circulatory, hormoal, euroelectric ad behavioral factors [4]. I the past, the Ecephalographer, by visual ispectio was able to qualitatively distiguish ormal EEG activity from localized or geeralized abormalities cotaied withi relatively log EEG records. The differet types of epileptic seizures are characterized by differet EEG waveform patters [5]. 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 [6]. I this paper, we compare the performace of three differet Multi Layer Perceptro (MLP) eural etworks i optimizig the epileptic risk level of the patiet classified by the fuzzy system. We also preset a compariso of these methods based o their performace idices ad quality values. 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, Combiatore, Idia. A paper record of 6 chael EEG data is acquired from a cliical EEG moitorig system through 0-0 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

2 EEG sigal free of artifacts, a reasoably accurate detectio of epilepsy is possible; however, difficulties arise with artifacts. 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 400x00 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 [7],[8]. 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 oe miutes. The patiets had a average age of 3 years. A total of 480 epochs of secods duratio are used. A Fuzzy System as a Pre Classifier 4. The total umbers of spike ad sharp waves i a epoch are recorded as evets. 5.The variace is computed as σ give by ( xi µ ) σ = () xi Where µ = is the average amplitude of the epoch. p ti 6.The average duratio is give by D = (3) p Where t i is oe peak to peak duratio ad p is the umber of such duratios. 7. Covariace of Duratio.The variatio of the average ( ) D ti duratio is defied by CD = pd (4) B. Fuzzy Membership fuctios p Neuro-Fuzzy classificatio system is show i figure. The mai objective of this research is to classify the epilepsy risk level of a patiet from EEG sigals. This is accomplished as: ) Fuzzy classificatio for epilepsy risk level at each chael from EEG sigals ad its parameters. ) Each chael results are optimized, sice they are at differet risk levels. 3) Performaces of fuzzy classificatio before ad after the MLP eural etworks (supervised) optimizatio methods are aalyzed. EEG Sigal Parameter A. Fial Stage Fig.. Neuro - Fuzzy Classificatio System The parameters derived from EEG sigals are [4] [5] [6],. The eergy i each two-secod epoch is give by E = x i Fuzzy System Code Patters Risk level output 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 ad 70 ms ad sharp waves are detected whe the duratio lies betwee 70 ad 00ms. () The eergy is compared with the other six iput features to give six outputs. Each iput feature is classified ito five fuzzy liguistic levels viz., very low, low, medium, high ad very high [9],[0]. The triagular membership fuctios are used for the liguistic levels of eergy, peaks, variace evets, spike ad sharp waves, average duratio ad covariace of duratio. The output risk level is classified ito five liguistic levels amely ormal, low, medium, high ad very high. C. Fuzzy Rule Set Rules are framed i the format IF Eergy is low AND Variace is low THEN Output Risk Level is low I this fuzzy system we have five liguistic levels of eergy ad five liguistic levels of other six features such as variace, peaks, evets, spike ad sharp waves, average duratio ad covariace of duratio. Theoretically there may be 5 6 (that is 565) rules are possible but we had cosidered the fuzzy pre -classifier as a combiatio of six two iputs ad oe output ( ) system. With eergy beig a costat oe iput the other iput is selected i sequetial maer. This two iputs oe output ( ) fuzzy system works with 5 rules. We obtai a total rule base of 50 rules based o six sets of 5 rules each. This is a type of fuzzy rule based system []. D. Estimatio of Risk Level i Fuzzy Outputs The output of a fuzzy logic represets a wide space of risk levels. This is because there are sixtee differet chaels for iput to the system at three epochs. This gives a total of forty-eight iput output pairs. Sice we deal with kow cases of epileptic patiets, it is ecessary to fid the exact level of risk the patiet. This will also aid i the developmet of automated systems that ca precisely classify the risk level of the epileptic

3 patiet uder observatio. Hece a optimizatio of the outputs of the fuzzy system is ecessary. This will improve the classificatio of the patiet ad ca provide the EEGer with a clear picture. A specific codig method processes the output fuzzy values as idividual code. The alphabetical represetatio of the five risk level classificatios of the outputs is show i table.i Table.I Represetatio of Risk level Classificatios Risk Level Normal Low Medium High Very High Represetatio U W X Y Z A sample output of the fuzzy system with actual patiet readigs is show i fig. for eight chaels over three epochs. It ca be see that the Chael shows medium ad high risk levels while chael 8 shows very high risk levels. Also, the risk level classificatio varies betwee adjacet epochs. Epoch YYYYXX YYYXXY ZYYYZZ ZZYZYZ Epoch ZYYWYY ZZYZYY YYYXYY YYYXYY Epoch 3 YYYXYZ YYYXYZ ZYYYZZ YYYXXZ YYYYYZ YYYXYY Fig.. Fuzzy Logic Output The Performace of the Fuzzy method is defied as follows [5] PC MC FA PI = 00 (5) PC Where PC Perfect Classificatio; MC Missed Classificatio; FA False Alarm = [( )/0.5] *00 =40% The perfect classificatio represets whe the physicias ad fuzzy classifier agrees with the epilepsy risk level. Missed classificatio represets a true egative of fuzzy classifier i referece to the physicia ad shows High level as Low level. False alarm represets a false positive of fuzzy classifier i referece to the physicia ad shows Low level as High level. The performace for Fuzzy classifier is as low as 40%. It is essetial to optimize the out put of the fuzzy systems. We employed differet architectures of MLP eural etworks (post classifier) [] to optimize the epilepsy risk level. A pertiet explaatio for the eural etwork optimizatio is give below. III. ROLE OF NEURAL NETWORKS IN THE OPTIMIZATION OF FUZZY OUTPUTS etworks have bee touted as havig excellet potetial for improvig classificatio accuracy i patiet specific diagostic data. However, there have bee few studies which have demostrated these potetial usig real data sets [3]. The appeal of eural etworks as patter recogitio systems is based upo several cosideratios. First, eural etworks appear to perform as well or better tha other techiques, ad require o assumptios about the explicit parametric ature of distributios of the patter data to be classified. I this regard they are similar to K-earest eighbor algorithms. However, eural etworks, oce traied, are computatioally more efficiet. A. Multi layer Perceptros (MLP) for Risk Level Optimizatio Multilayer perceptros (MLPs) are feed forward eural etworks traied with the stadard back propagatio algorithm. They are supervised etworks so they required a desired respose to be traied [4]. They lear how to trasform iput data ito a desired respose, so they are widely used for patter classificatio. Most NN applicatios ivolve MLPs. They are very powerful patter classifiers. With oe or two hidde layers they ca approximate virtually ay iput-output map. They have bee show to approximate the performace of optimal statistical classifiers i difficult problems. They ca efficietly use the iformatio cotaied i the iput data. The advatage of usig this etwork resides i its simplicity ad the fact that it is well suited for olie implemetatio [5]. The Leveberg-Marquardt (LM) algorithm is the stadard traiig method for miimizatio of MSE (Mea Square Error) criteria, due to its rapid covergece properties ad robustess. 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. The LM algorithm is first show to be a bled of vailla gradiet descet ad Gaussia Newto iteratio. This error back propagatio algorithm is used to calculate the weights updates i each layer of the etwork. The LM update rule is give as[6] T T ( J J + ) J e W = µ (6) Where J is jacobia matrix of derivatives of each error to each weighted µ is a scalar, ad e is error vector. If scalar µ is very large, the above method approximates gradiet descet. While if it is small the above expressio becomes Gauss-Newto method. Because the GN method is faster but teds to less accurate ear a error miima. The scalar µ is adjusted just like adaptive learig rate used by traibpx. As log as the error gets smaller, µ is made smaller. Traiig cotiues util the error goal is met, the miimum error gradiet occurs, the maximum value of µ occurs or the maximum umber of epochs has fiished.

4 B. Traiig ad Testig Procedures for the Selectio of Optimal Architecture The primary aim of developig a ANN is to geeralize the features (epilepsy risk level) of the processed fuzzy outputs. We have applied differet architectures of MLP etworks for optimizatio. The weights betwee iput layer, the hidde layer ad output layer etwork are traied with error back propagatio algorithm to miimize the square output error to zero. The simulatios were realized by Simulator 4.0 of Matlab v.7.0 [7]. Sice our eural etwork model is patiet specific i ature, we are applyig 48 (3x6) 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 (6) for zero mea square error coditio ad tested with other two sets of patters (x6). 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 [7] MSE = N N ( O i T j ) Where Oi is the observed value at time i, T j is the target value at model j; j=-0, ad N is the total umber of observatios per epoch ad i our case, it is 6. 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. The traiig procedure for MLP etwork is show i the figure.3. (7) show i table 3. The gai or learig rate η (0.3), mometum α (0.5), ad traiig epochs are tabulated for each model [8]. Durig the traiig phase, a error measure (7) of the closeess of the weights to a solutio ca be calculated for each patter (6 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. The squared error (e i ) from equatio (7) betwee the iput ad the output of the ANN is coverted ito the cofidece score usig relatio C i =exp (-λe i ) where refers to the eural etwork idex [9]. I this paper we have chose λ=. The average cofidece score for each MLP architecture is tabulated i the table.ii Table II Estimatio of MSE i Various MLP Architectures Architec ture Trai ig Epoc hs Mea Square Error (MSE) Idex Cofidece score Trai ig E E E E E E Testig C i =exp(-λe i ) E- 3.7 E E -08.E I the MLP etworks testig procedures 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). Therefore we had selected (6-6-),(4-4-) ad (-4-) MLP etwork architectures due to their lesser umber of traiig epochs. IV. RESULTS AND DISCUSSIONS Fig. 3.Traiig of MLP Feed forward (-4-) The results of the MLP back propagatio eural models traied with the Leveberg-Marquardt (LM) learig algorithm are The outputs are obtaied for three epochs for every patiet ad the the epileptic risk level is classified by the eural etwork approach. 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. The performace idex (5) obtaied by Fuzzy techiques ad MLP (6-6-),(4-4)&(-4-) eural etwork optimizatio are 40%, 96.9%, 99.34% ad 00%respectively. The followig table III shows the epilepsy risk level estimatio for te patiet s specific MLP Feed forward etworks at three differet architectures.

5 Table III Estimatio of MSE at Te Patiet Specific MLP (6-6-),(4-4-) &(-4-)Feed forward s Model Target Code MLP 6-6- Test risk Mea level Code square Error(MSE) MLP etwork 4-4- Test risk Mea level Code square Error(MSE) MLP etwork -4- Test risk Mea level Code square Error(M SE). ZZZZZZ 3.83E YYYXYY ZZYXYY 9.34E-03 YYYXYY 0 YYYXYY 0 3 ZYYZYY 7.3E YYXXYY YYYXYY 8.56E-03 YYXXYY 0 YYXXYY 0 5. ZZYYXY ZZZZXY 5.03E-03 ZZYYXY 0 ZZYYXY 0 6. XXZYWY YXZYXY 8.45E-03 XXZYWY 0 XXZYWY 0 7. ZYYYZZ ZZYYZZ 4.87E-03 ZZYYZZ 3.8E-08 ZYYYZZ 0 8. YYYXXX ZZYXXX 7.3E-03 ZYYXXX.95E-08 YYYXXX 0 9. ZYZYXW ZYZZXX 6.54E-03 ZYZYXX.75E-08 ZYZYXW 0 0. XYYZWZ YYYZXZ 7.43E-03 XYYZWZ 0 XYYZWZ 0 From table III the (6-6-) MLP eural etwork models,4 ad 7 are settled with sigle error i the risk level codes ad other models are with i two level errors i the higher side of the risk level patters. This is iheretly due to the lower threshold of the classifier ad these results i heavy false alarms of 4.58%. I the( 4-4-) MLP models 7,8 ad 9 are settled at sigle error due to false alarm of 0.64 %. The (-4-) MLP etwork is perfectly idetified all the patters without ay error or ay additioal traiig cost whe compare to (6-6-) & (4-4- MLP etwork models. A costat C is empirically set to 0 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. Table IV shows the Compariso of the fuzzy ad eural etwork optimizatio techiques. It is observed from table IV, that (-4-) MLP eural etwork is performig well with the highest performace idex ad quality values. Table IV Results of Classifiers take as Average of all te Patiets A. Quality Value The goal of this research is to classify the epileptic risk level with as may perfect classificatios ad as few false alarms as possible. I Order to compare differet classifier we eed a measure that reflects the overall quality of the classifier []. Their quality is determied by three factors. Classificatio rate, Classificatio delay ad False Alarm rate, The quality value Q V is defied as C Q = V ( R fa + 0.)*( Tdly * Pdct + 6* Pmsd ) (8) 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 Para Meters Risk level classificatio rate (%) Weighted delay (s) False-alarm rate/set Performace Idex % Quality value Fuzzy Techiques before optimizatio MLP (6-6-) MLP (4-4-) MLP (-4-) %, 99.34%

6 The eural etwork is a slow respose method with iheritig weighted delay of secods. The other two MLP eural etworks are exhibitig quick resposes with weighted delay of.9 secods ad.98 secods respectively. These MLPs are placed with low performace idices ad quality values. V. 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 fuzzy 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. MLP eural etwork optimizatio techique is used to optimize the risk level by icorporatig the above goals. 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. A compariso of Elma ad Radial Basis will be take for further studies. VI. ACKNOWLEDGEMENTS The authors thak 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 Combiatore ad Dr. B.Rajalakshmi, Diabetologist, Govt. Hospital Didigul for providig the EEG sigals. REFERENCES [] L.D.Iasemidis, Epileptic Seizure Predictio ad Cotrol, IEEE Trasactios o Biomedical Egieerig, vol, 50, o.5, May 003 pp [] Celemet.C.etal., A Compariso of Algorithms for Detectio of Spikes i the Electroecephalogram,IEEE Trasactio o Bio Medical Egieerig,vol,50,o.4,003, pp 5-6. [3] Mauca, R.,Casdagil, M.C &Savit, No statioarity i Epileptic EEG ad Implicatios for Dyamics, Mathematical Bio Scieces,47, 998, pp -. [4] K. P.Adlassig, Fuzzy Set Theory i Medical diagosis, IEEE Trasactios o Systems Ma Cyberetics, vol,6, March 986, pp [5] Aliso A Digle et al, A Multistage system to detect epileptic form activity i the EEG, IEEE Trasactios o Biomedical Egieerig, vol, 40, o., December 993, pp [6] 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 997, pp 5-. [7] Arthur C Gayto, Text Book of Medical Physiology, Prism Books Pvt. Ltd., Bagalore, 9 th Editio, 996. [8] J. Seugha Park et al, TDAT Domai Aalysis Tool for EEG Aalysis, IEEE Trasactios o Biomedical Egieerig,vol, 37,o.8, August 990, pp [9] Doa L Hudso, Fuzzy logic i Medical Expert Systems, IEEE EMB Magazie, vol, 3,o.6, November/December 994, pp [0] Pamela McCauley-Bell ad Adedeji B.Badiru, Fuzzy Modelig ad Aalytic Hierarchy Processig to Quatify Risk levels Associated with Occupatioal Ijuries- Part I: The Developmet of Fuzzy- Liguistic Risk Levels, IEEE Trasactio o Fuzzy Systems, vol,4,o., 996, pp 4-3. [] R.Harikumar ad B.Sabarish Narayaa, Fuzzy Techiques for Classificatio of Epilepsy risk level from EEG Sigals, Proceedigs of IEEE TENCON 003, Bagalore, Idia, 4-7 October 003, pp [] Nuretti Acir etal., Automatic Detectio of Epileptiform Evets I EEG by A Three-Stage Procedure Based o Artificial s, IEEE trasactio o Bio Medical Egieerig, vol,5,o.,jauary 005 pp [3] Li Gag etal, A Artificial Itelligece Approach to ECG Aalysis, IEEE EMB Magazie, vol 0, April 000, pp [4] Draze.S.etal., Estimatio of Difficult to- Measure Process Variables Usig s, Proceedigs of IEEE MELECON 004,May -5,004 Dubrovik,Croatia,pp [5] Moreo.L. etal., Brai Maturatio Estimatio Usig Classifier, IEEE Trasactio of Bio Medical Egieerig, vol,4,o., April 995, pp [6] Tarasseko.L, Y.U.Kha, M.R.G.Holt, Idetificatio of Iter-Ictal Spikes i The EEG Usig Aalysis, IEE Proceedigs Sciece Measuremet Techology, vol, 45, o.6, November 998, pp [7] H.Demuth ad M..Beale, etwork tool box: User s guide, Versio 3.0, the Math works, Ic., Natick, MA, 998. [8] Guoqiag Peter Zhag, s for Classificatio: A Survey, IEEE Trasactio o Systems, Ma, Ad Cyberetics-Part C, vol,30,o.4, November 000, pp [9] K.Srirama murty ad B.Yegaarayaa, Combiig Evidece from Residual Phase ad MFCC Features for Speaker Recogitio, IEEE Sigal Processig Letters, vol,3,o., 006, pp Dr. (Mrs.) R.Sukaesh Dr.(Mrs).R.Sukaesh received her bachelor s degree i ECE from Govermet College of Techology Coimbatore i98. She obtaied her M.E (Commuicatio Systems) degree from PSG College of Techology, Coimbatore i 985 ad PhD i Bio Medical Egieerig from Madurai Kamaraj Uiversity, Madurai i 999.Sice 985 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, s, 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 988.He obtaied his M.E (Applied Electroics) degree from College of Eigeerig, Guidy, Aa uiversity Cheai i 990. He has

7 5 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 pursuig doctoral programme as a research scholar i Bio-Medical Egieerig uder guidace of Dr. (Mrs.) R.Sukaesh at Departmet of ECE, Thiagarajar College of Egieerig, Madurai. He has published Eight papers i Iteratioal ad Natioal Jourals ad also published aroud twety seve 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.