INSIGHT TO MUTUAL INFORMATION AND MATRIX FACTORIZATION WITH LINEAR NEURAL NETWORKS FOR EPILEPSY CLASSIFICATION FROM EEG SIGNALS

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1 Interntionl Journl of Mechnicl Engineering nd Technology (IJMET) Volume 10, Issue 01, Jnury 2019, pp , Article ID: IJMET_10_01_070 Avilble online t ISSN Print: nd ISSN Online: IAEME Publiction Scopus Indexed INSIGHT TO MUTUAL INFORMATION AND MATRIX FACTORIZATION WITH LINEAR NEURAL NETWORKS FOR EPILEPSY CLASSIFICATION FROM EEG SIGNALS Deprtment of ECE, Bnnri Ammn Institute of Technology Sthymnglm, Indi ABSTRACT As rich sptiotemporl dynmics re exhibited in the humn brin, it is quite complicted in nture. Sudden electricl disturbnce of the brin occurs in temporry mnner nd it cuses epileptic seizures. Seizures my be sometimes confused ith other events nd sometimes it my even go unnoticed. Prediction of occurrence of n epileptic seizure is quite difficult nd it is very difficult to understnd the course of ction. To nlyze this idespred disorder of the brin, Electroencephlogrphy (EEG) is used. It is indeed one of the best techniques to probe the ctivity of the brin nd it is highly useful to dignose the neurologicl disese. Tons of informtion is obtined by the EEG monitoring system nd nlyzing it visully is quite difficult. Therefore, the dimensionlity of the EEG dt is reduced ith the help of dimensionlity reduction techniques like Mutul Informtion (MI) nd Mtrix Fctoriztion (MF). The vlues reduced through dimensionlity reduction re then clssified ith the help of Liner Lyer Netorks for the clssifiction of epilepsy from EEG Signls. Results sho tht hen MI is used to reduce the dimensionlity nd clssified ith Liner Lyer Netorks n verge clssifiction ccurcy of 96.60% is obtined. When MF is employed ith Liner Lyer Netorks n verge clssifiction ccurcy of 97.47% is obtined. Keyords: EEG, Seizure, MI, MF, Epilepsy O. Cite this Article:, Insight to Mutul Informtion nd Mtrix Fctoriztion ith Liner Neurl Netorks for Epilepsy Clssifiction from EEG Signls Interntionl Journl of Mechnicl Engineering nd Technology, 10(01), 2019, pp editor@ieme.com

2 1. INTRODUCTION A set of brin disorders hich re chronic in nture nd chrcterized by recurrent seizures is clled s epilepsy [1]. The bsic dignostic test for epilepsy is done ith the help of EEG [2]. EEG provides n incessnt mesure of the function of the cortex nd hs n mzing sptio temporl resolution. For lot of clinicl purposes nd resons, vrious efforts re spent on interpreting the EEG signls. For interpreting EEG signls, the gold stndrd in present clinicl prctice is through visul scnning nd inspection hich is quite tedious [3]. Shortge in the supply of experienced electroencephlogrphers is big problem nd so there is need for the utomted systems hich helps in the detiled interprettion of the EEG signls [4]. Some of the significnt nd most relevnt orks discussed in literture regrding EEG signl processing nd epilepsy clssifiction from EEG signls re discussed s follos. The permuttion entropy of sclp EEG in order to investigte epilepsy s given by Ferlzzo et l [5]. The ggregtion opertors long ith the fuzzy techniques for the epilepsy clssifiction from EEG signls using cerebrl blood flo s done by Hrikumr nd Kumr [6]. An utomted detection system to detect the epileptic seizures s designed by Smi et l ith the help of Support Vector Mchine (SVM) Clssifier [7]. A Modified Sprse Representtion Clssifier nd Nïve Byesin Clssifier ere developed by Rjguru nd Prbhkr for the clssifiction of epilepsy from EEG signls [8]. A chos bsed nonliner nlysis of epileptic seizures s developed by Shu et l [9]. From velet thresholding point of vie, the different frequency behviors of EEG signls in epileptic ptients ere nlyzed by Hrikumr nd Kumr [10]. The dignosis of epilepsy ith the help of combined doffing oscilltor s done by Gndhi et l [11]. The dimensionlity reduction techniques ere clssified by utilizing the Genetic Algorithms for clssifying epilepsy from EEG signls by Prbhkr nd Rjguru [12]. The time-frequency nlysis s done in detiled mnner for detection of epileptic seizures by Tzlls et l [13]. The clssifiction of epilepsy risk levels using vrious distnce mesures s done by Prbhkr nd Rjguru [14]. The methodologies like Byesin Liner Discriminnt Anlysis (BLDA), Hybrid Artificil Bee Colony ith Prticle Srm Optimiztion (ABC-PSO), Sprse Principl Component Anlysis (S-PCA), nd Soft Decision Trees Clssifiers s utilized by Rjguru nd Prbhkr for clssifying the epilepsy risk levels from EEG signls [15],[16]. In this MF nd MI re used s dimensionlity reduction techniques nd it is lter clssified ith the help of Liner Lyer Netorks. The orgniztion of the pper is described s follos. In section 2, the mterils nd methods re discussed folloed by the ppliction of liner lyer netorks s post clssifiers for clssifiction of epilepsy from EEG signls in section 3. Section 4 provides the results nd discussion. Section 5 gives the conclusion. The pictoril description of the ork is shon in Figure editor@ieme.com

3 An Insight to Mutul Informtion nd Mtrix Fctoriztion ith Liner Neurl Netorks for Epilepsy Clssifiction from EEG Signls Figure 1 Proposed Flo of Work 2. DATA SET AND DIMENSIONALITY REDUCTION TECHNIQUES The EEG dt nlyzed here is for totlly 20 ptients hich re obtined from the Neurology Deprtment of the Sri Rmkrishn Hospitl, Coimbtore. The EEG dt is obtined in Europen Dt Formt (EDF). The 16 chnnel electrodes re kept on the sclp of the epileptic ptients ccording to the stndrd Interntionl system nd the recordings of EEG re done. The recordings of the EEG re done for more thn 55 minutes nd the EEG recordings re split into epochs for esy nlysis nd computtion. Ech epoch of single chnnel hs pproximtely 400 vlues nd so for ll the epochs of the 16 chnnels of the totl 20 ptients, the vlues re too high nd so to reduce the overll dimensionlity of the EEG signls, dimensionlity reduction techniques such s Mutul Informtion nd Mtrix Fctoriztion is utilized Mutul Informtion Given dom() nd b dom(b) p( ) : the probbility of hich tkes the vlue, the probbility prmeters re denoted s follos: p, ) : the joint probbility of hich tkes the vlue nd b tkes the vlue ( b ( ) p, ) p( ) p = b ( b b editor@ieme.com

4 Entropy nd Mutul Informtion re very closely relted [17]. In informtion theory, one of the centrl notices is entropy hich helps to ssess the uncertinty in prticulr rndom vrible. The entropy of rndom vrible denoted s H() is defined s H( ) = p( dom( ) )log p( The informtion hich one rndom vrible tells bout nother rndom vrible is described ith the help of MI. The MI of 2 rndom vribles nd b denoted s I (, b) is defined s I = p(, )log b b dom( ) dom( b) p( ) p( b ) ) p(, b The importnt informtion tht b tells bout is the reduction in uncertinty bout due to the knoledge of b nd vice-vers. If the vlue of I (, b) is greter, then more informtion ill be shred by nd b bout ech other Mtrix Fctoriztion It is fmous computtionl method utilized for reducing the dimensionlity of the dt [18]. This technique hs lso been used idely in the fields of chemicl spectrl nlysis, dt mining, imge processing, formultion of ne drugs nd so on. It is by mens of fctorizing the dt mtrix into sprse, lo rnk nd non-negtive mtrix. It is idely clled s Positive Mtrix Fctoriztion. Assume L to be non-negtive mtrix ith dimension c d. The nonmtrix fctoriztion decomposes the mtrix into sprse, lo rnk nd non-negtive fctors so tht the pproximtion of the originl dt is modeled s L = KQ here K nd Q hve non-negtive elements. K is of prticulr dimension c r nd is termed s the bsis mtrix becuse its ro contins set of bsis vectors. Q is of prticulr dimension r d, nd is termed s the eight mtrix becuse its ro hs coefficient sequences. For the fctoriztion, the rnk r is chosen so tht ( c + d) r cd. To the columns of L, the columns of Q re lys in one-to-one correspondence. Therefore, the result (KQ) in esily interpreted s eighted sum of every bsis vector in K. The eight represents the corresponding columns of Q. The resulting dditive properties obtined from the non-negtive constrints of negtive mtrix fctoriztion results in specific bsis vector tht represents the locl component of the stndrd originl dt. 3. POST CLASSIFICATION WITH LINEAR LAYER NEURAL NETWORK The dimensionlly reduced vlues re then fed inside the Liner Neurl Netorks to clssify the risk of epilepsy from EEG signls. A liner neurl netork is n ffine mpping technique [19]. The trining of this netork cn be done in to ys, firstly ith the lest squres methodology or secondly using the Widro-Hoff lgorithm. Lest squres cn be implemented using the pseudo inverse nd Widro-Hoff lgorithm is nothing but simple pproximte grdient descent present on the lest squres error. In time series, the ppliction includes k prediction nd removl of noise, here initilly the time series is embedded to nd so this liner lyer netorks is predominntly used in the nlysis of signl community. ) editor@ieme.com

5 An Insight to Mutul Informtion nd Mtrix Fctoriztion ith Liner Neurl Netorks for Epilepsy Clssifiction from EEG Signls The liner neurl node hs schemtic form here the dt flos generlly from left to right. The rel numbers z 1,..., zn to the input lyers is presented initilly. The 1 k corresponds to specific eight. A eighted edge mens it is multiplied by 1 k nd generlly it corresponds to the edge hich trvels from node k to node i. After prticulr mode Q, the totl sum of the incoming signls is considered nd then dded to prticulr vlue d. The resting stte of the cell is tken s 'd' nd finlly the result is pssed long the xon. The end result is expressed s z ( z z + + z d) n n , hich cn be visulized s n n -dimensionl plne. So only liner neurl node is specificlly considered s n ffine mp. Similrly, multiple computtionl nodes re combined together in order to get liner neurl netork. The ffine problem is represented s Fz + g = t )) nd is quite equivlent to solve liner nture problem F z = t, here F ) is m n nd g ) is m1, here the totl number of nodes in the input lyer is given s 'n' nd the totl number of nodes in the output lyer is given s 'm'. To best determine both the respective bises nd eights tht best mtch given input-output set, trining liner netork is perfectly done. Trining corresponds to prticulr computtion of pseudo inverse hen ll the dt is vilble. 4. RESULTS AND DISCUSSION When Mutul Informtion nd Mtrix Fctoriztion re considered s dimensionlity reduction techniques nd hen it is clssified ith Liner Neurl Netorks clssifier nd bsed on the prmeters like Performnce Index, Accurcy, Qulity Vlues, Time Dely, Specificity nd Sensitivity the verge results re computed in Tbles 1 nd 2. The mthemticl formule for the Performnce Index (PI), Sensitivity, Specificity nd Accurcy re given s follos PI = PC MC PC FA 100 here Perfect Clssifiction is expressed by PC, Missed Clssifiction is denoted by MC nd the Flse Alrm is expressed by FA. The Sensitivity, Specificity nd Accurcy mesures re mthemticlly explined by the folloing Sensitivity = Specificity = PC 100 PC + FA PC 100 PC + MC Sensitivity + Specificity Accurcy = 2 The Qulity Vlue QV is mthemticlly defined s follos C Qv = ( Rf + 0.2)*( Tdly * Pdct + 6* Pmsd ) here C specifies the scling constnt, Rf mentions the number of flse lrm per set, Tdly denotes the verge dely of the onset clssifiction in seconds, Pdct explins the percentge of perfect clssifiction nd Pmsd specifies the percentge of perfect risk level missed editor@ieme.com

6 The time dely is mthemticlly ritten s follos Time PC MC Dely = Severl resons justify the use of this formul. i. QV monotoniclly increses here Rf decreses. The loer the flse lrm rte is better the clssifier performnce ii. The constnt flse lrm of 0.2 per set is dded to Rf in the QV formul. In our method, the flse lrm rte is lo nd usully rnges form A rte higher thn 0.5 is uncceptble iii. Tdly * Pdct + 6*Pmsd is ctully the eighted verge of the dely of onset clssifiction hile Tdly in the verge dely of onset clssifiction is used s dely for missed risk level by the method. The eights for the verge dely of clssifiction or missed risk level or the percentges of perfectly clssified nd missed risk levels consequently the eighted verge dely reflects the qulity of clssifier ith respect to the clssifiction dely. iv. As result of (iii), QV is inversely proportionl to the eighted verge of the dely of onset clssifiction. This reflects the fct tht the shorter the onset clssifiction delys the better the clssifier. v. Theoreticlly, the eighted dely, Tdly * Pdct + 6*Pmsd could be zero if no level is missed (Pmsd is zero) nd ll clssifiction delys Tdly re zero. Hoever, this cnnot hppen becuse Tdly cn never be zero. In order to clssify perfect risk level ith the shortest possible dely, the clssifiction indo, this lsts 2.0 seconds. As result, the eighted dely Tdly * Pdct + 6*Pmsd cnnot be very smll. A constnt C is empiriclly set to 10 becuse this scle is the vlue of QV to n esy reding rnge. The higher vlue of QV, the better the clssifier mong the different clssifier, the clssifier ith the highest QV should be the best. Tble 1 Performnce Anlysis of MI ith Liner Neurl Netorks Prmeters Epoch 1 Epoch 2 Epoch 3 Averge PC (%) MC (%) FA (%) PI (%) Specificity (%) Sensitivity (%) Time Dely (sec) Qulity Vlues Accurcy (%) editor@ieme.com

7 An Insight to Mutul Informtion nd Mtrix Fctoriztion ith Liner Neurl Netorks for Epilepsy Clssifiction from EEG Signls Tble 2 Performnce Anlysis of MF ith Liner Neurl Netorks Prmeters Epoch 1 Epoch 2 Epoch 3 Averge PC (%) MC (%) FA (%) PI (%) Specificity (%) Sensitivity (%) Time Dely (sec) Qulity Vlues Accurcy (%) CONCLUSION It is thus concluded tht hen Mutul Informtion is utilized s dimensionlity reduction technique nd hen it is clssified ith Liner Neurl Netorks, n verge clssifiction ccurcy of 96.60% long ith n verge qulity vlue of is obtined. Similrly, hen Mtrix Fctoriztion is utilized s dimensionlity reduction technique nd hen it is clssified ith Liner Neurl Netorks, clssifiction ccurcy of 97.47% long ith n verge qulity vlue of is obtined. Thus, the performnce of the Mtrix Fctoriztion surpsses the performnce of the Mutul Informtion hen clssified ith Liner Neurl Netorks. Future orks im to ork ith different dimensionlity reduction techniques nd clssify ith Liner Neurl Netorks to clssify the epilepsy risk level from EEG signls. REFERENCES [1] S.K Prbhkr, H Rjguru, ICA, LGE nd FMI s Dimensionlity Reduction Techniques folloed by GMM s Post Clssifier for the Clssifiction of Epilepsy Risk Levels from EEG Signls, 9th IEEE Europen Modelling Symposium 2015, October 6-8, Mdrid, Spin, /15, DOI: /EMS [2] S.K Prbhkr, H Rjguru, Clssifiction of Epilepsy Risk using Vrible Thresholding Bsed Feture Extrction Technique nd suitble Post Clssifiers, 9th IEEE Europen Modelling Symposium 2015, October 6-8, Mdrid, Spin, , DOI: /EMS [3] S.K Prbhkr, H Rjguru, Entropy Bsed PAPR Reduction for STTC System Utilized for Clssifiction of Epilepsy from EEG Signls Using PSD nd SVM, IFBME Proceedings (Springer), 3rd Interntionl Conference on Movement, Helth nd Exercise (MoHE), September 28-30, 2016, Mlysi. [4] S.K Prbhkr, H Rjguru, Wireless Systems ith Reduced PAPR Using K-mens Modified PTS Implemented for Epilepsy Clssifiction from EEG Signls, IFBME Proceedings (Springer), Interntionl Conference on Advncements of Medicine nd Helth Cre through Technology (MEDITECH), October 12-15, 2016, Romni editor@ieme.com

8 [5] E. Ferlzzo, N. Mmmone, V. Cinci, S. Gsprini, A. Gmbrdell, A. Lbte, M. A. Ltell, V. Sofi, M. Eli, F. C. Morbito, nd U. Agugli, Permuttion entropy of sclp EEG: A tool to investigte epilepsies. Suggestions from bsence epilepsies, Clin. Neurophysiol., vol. 125, no. 1, pp , Jn [6] R.Hrikumr, P.Sunil Kumr, Fuzzy Techniques nd Aggregtion Opertors in Clssifiction of Epilepsy Risk Levels for Dibetic Ptients Using EEG Signls nd Cerebrl Blood Flo, Journl of Biomterils nd Tissue Engineering, Vol.5, No.4, pges : , April 2015 [7] P. Smi, B. K. Pnigrhi, A. K. Godiyl, M. Bhti, J. Snthosh, nd S. Annd, Robust Expert System Design for Automted Detection of Epileptic Seizures using SVM Clssifier, IEEE Proc. Int. Conf. Prllel, Distributed nd Grid Comput., Soln, Indi, pp , Dec [8] H. Rjguru, S.K Prbhkr, A Frmeork for Epilepsy Clssifiction Using Modified Sprse Representtion Clssifiers nd Ntive Byesin Clssifier from EEG Signls, Journl of Medicl Imging nd Helth Informtics, Vol.6, no.8, pp: , December 2016 Issue [9] R. Shu, T. Prij, B. Mohptr, B. Rout, S. Shu, R. Pnd, P. Pl, nd T. Gndhi, Chos Bsed Nonliner Anlysis of Epileptic Seizure, IEEE Proc. Int. Conf. Emerging Trends Engg. Tech., Go, Indi, pp , Nov [10] R.Hrikumr, P.Sunil Kumr, Frequency behviors of electroencephlogrphy signls in epileptic ptients from velet Thresholding perspective, Applied Mthemticl Sciences, Vol. 9, 2015, no.50, , HIKARI Ltd, [11] T. Gndhi, P. Bhomik, A. Mohptr, S. Ds, S. Annd, nd B. K. Pnigrhi, Epilepsy Dignosis Using Combined Duffing Oscilltor nd PNN Model, Bioinform. Intell. Control., vol. 1, no. 1, pp , June 2012 [12] S.K Prbhkr, H. Rjguru, Utilizing Genetic Algorithms ith Dimensionlity Reduction Techniques for Epilepsy Clssifiction from EEG Signls, Interntionl Journl of Phrmcy nd Technology, Vol.8, Issue.1, Mrch 2016, pg: [13] T. Tzlls, M.G. Tsipours, nd D.I. Fotidi, Epileptic Seizure Detection in EEGs Using Time Frequency Anlysis, IEEE Trns. Info. Tech. Biomedicine, vol. 13, no. 5, pp , Sep [14] S.K Prbhkr, H Rjguru, A Different Approch to Epilepsy Risk Level Clssifiction Utilizing Vrious Distnce Mesures s Post Clssifiers, Proceedings of the 8th Biomedicl Engineering Interntionl Conference (BMEiCON), November 25-27, 2015, Ptty, Thilnd. [15] H.Rjguru, S.K Prbhkr, Byesin Liner Discriminnt Anlysis ith Hybrid ABC- PSO Clssifier for Clssifying Epilepsy from EEG Signls, IEEE Proceedings of the Interntionl Conference on Computing Methodologies nd Communiction (ICCMC 2017), Erode, Indi [16] H.Rjguru, S.K Prbhkr, Sprse PCA nd Soft Decision Tree Clssifiers for Epilepsy Clssifiction from EEG signls, IEEE Proceedings of the Interntionl Conference on Electronics, Communiction nd Aerospce Technology (ICECA 2017), Coimbtore, Indi, pp [17] Yiping Ke, Jmes Cheng, Wilfred Ng, An informtion-theoretic pproch to quntittive ssocition rule mining,22 July 2007, Springer-Verlg London Limited [18] M. Berry, M. Brone, A. Lngville, P. Puc, nd R.J. Plemmons, Algorithms nd pplictions for pproximte nonnegtive mtrix fctoriztion, Computtionl Sttistics nd Dt Anlysis, vol. 52, pp , editor@ieme.com

9 An Insight to Mutul Informtion nd Mtrix Fctoriztion ith Liner Neurl Netorks for Epilepsy Clssifiction from EEG Signls [19] V.V.Rmlingm, S.Gnesh kumr, nd V. Sugumrn, Anlysis of Eeg Signls Using Dt Mining Approch, Interntionl Journl of Computer Engineering nd Technology, pp