Available online at ScienceDirect. Procedia Computer Science 85 (2016 )

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1 Aville online t ScienceDirect Procedi Computer Science 85 (6 ) 86 8 Interntionl Conference on Computtionl Modeling nd Security (CMS 6) Arrhythmi Detection Using Wigner-Ville Distriution Bsed Neurl Network Snjit K.Dsh, *,G.Ssihusn Ro Deprtment of ECE,Rjdhni Engineering College,Bhuneswr-757,Indi Deprtment of ECE,College of Engineering, Andhr University, Viskhptnm-533,Indi Astrct Different techniques hve een used y the reserchers in recent yers to detect rrhythmis from electrocrdiogrm(ecg) signl. In this pper we hve used Wigner-Ville time-frequency energy distriution(wvd) nd neurl network to clssify four different ECG ets. These four ets re Norml(N), Left Bundle Brnch Block(L), Right Bundle Brnch Block(R) nd Ventriculr Premture Contrction(V). The -D wll slice from the WVD of ECG signl is considered s the fetures to trin the Bck propgtion Neurl network(bpnn) for rrhythmi et clssifiction. ECG signl smples from MIT-BIH rrhythmi dtse re used. Two different testing dtsets re considered to evlute the performnce of the technique. The experimentl results chieved mximum nd minimum ccurcy of 99.77% nd 87.5% respectively. 6 5 The The Authors. Authors. Pulished Pulished y Elsevier y Elsevier B.V. B.V. This is n open ccess rticle under the CC BY-NC-ND license ( Peer-review under responsiility of orgnizing committee of the 6 Interntionl Conference on Computtionl Modeling nd Peer-review Security (CMS under 6). responsiility of the Orgnizing Committee of CMS 6 Keywords: Time-frequency nlysis,winger-ville distriution,ecg Arrhythmi, Bck propggtion neurl network, pttern clssifiction. Introduction Arrhythmis is cused y electricl conductive system disorder of the hert my e ftl for the ptient. These rrhythmis cn e detected non invsively y studying the ECG wveform s it is reflected in it. But due to the nonsttionry nture of the wveform the reflection occur t rndom. Therefore ECG signl hve to e monitor for * Corresponding uthor. Tel.: E-mil ddress:snjitdsh64@gmil.com The Authors. Pulished y Elsevier B.V. This is n open ccess rticle under the CC BY-NC-ND license ( Peer-review under responsiility of the Orgnizing Committee of CMS 6 doi:.6/j.procs

2 Snjit K. Dsh nd G. Ssihusn Ro / Procedi Computer Science 85 ( 6 ) hours to detect the rrhythmis if ny, which is time consuming nd there is possiility of humn error. Hence computer sed system is required to e developed for error free dignosis of rrhythmis. Different techniques hs een proposed y different reserchers to detect the rrhythmis Krimifrd & Ahmdin[] used hermitin is function nd K-nerest neighor(knn) clssifier to detect seven different types of rrhythmis with sensitivity nd specificity of 99.% nd 99.84% respectively. In[] principl component nlysis(pca) is used in hyrid multilyered perceptron network(hmlp) to reduce the numer of fetures from 4 to 5 nd chieved n ccurcy of 95%. Thoms, Ds & Ari[3] used dul tree complex wvelet trnsform(dtcwt) for feture extrction nd then used rtificil neurl network(ann) clssifier to mp five different types of ECG ets. In their study the ccurcy is 94.64%. In [4] fuzzy clssifier is used in first stge with n ccurcy of 93.34% which is improved to 98.64% in second stge y pplying genetic lgorithm. In [5] PCA is pplied to the sttisticl feture extrcted from the spectrl correltion of ECG dt nd then support vector mchine(svm) is pplied to clssify the five different ECG ets with n ccurcy of 98.6%. In [6]the ccurcy of ECG clssifiction is 96.% 3.4%. In [7] Yu nd Chou pplied Independent Component Anlysis(ICA) & FBNN for clssifying eight different ECG ets with mximum ccurcy 98.37%. In[8] the uthors hve used Higher order sttistics(3 rd order cumulnt) to chieve n ccurcy of 94.5%. Most of the signls including ECG re non-sttionry nd the nlysis of non-sttionry signls requires oth time-frequency representtion[9-]. WVD is very importnt tool for the time-frequency of the time-vrying signl nd non-sttionry process. In ddition to Time-frequency nlysis WVD hs een suggested for instntneous frequency estimtion, detection nd clssifiction, spectrl nlysis of non-sttionry rndom signls. It hs lso wide rnge of ppliction such s speech processing, rdr, sonr nd pttern recognition. Here we hve used the WVD to extrct the stule chnges in the different ets used to id the clssifiction using neurl network. The pper is orgnized in the following mnner. Section presents the methods including pre-processing, Wigner-Ville Distriution, feture extrction nd neurl network. Section 3 provides the experimentl results nd discussion. Lstly section 4 gives the conclusion.. Methods. Pre-Processing MIT-BIH[3] rrhythmi dtse is used where the ECG signl is smpled t 36 Hz. Pn-Tompkins QRS detector[4] is used to detect the R pek nd QRS segments. As the mximum QRS energy is ville within 5-5Hz, the 3 db nd-pss filter is designed y cscding the low-pss filter(lpf) of cut-off frequency out Hz with high-pss filter(hpf) of low cutoff frequency of out 5Hz to chieve this ndwidth. The nd-pss filter removes seline wnder, muscle noise nd interference signl of 6Hz. After filtering, the signl is differentited to provide the QRS complex slope informtion. The integrtor which is moving window integrtion gives the wveform feture informtion in ddition to slope of R wve.fig.. shows the rw nd pre-processed ECG signl of one QRS segment of.556seconds.. Wigner-Ville Distriution WVD is time-frequency energy distriution defined for signl s

3 88 Snjit K. Dsh nd G. Ssihusn Ro / Procedi Computer Science 85 ( 6 ) 86 8 WVD cn lso defined s Where F is the Fourier trnsform of signl. RAW ECG Pre-Processed ECG.8.6 Mgnitude (mv).4. Mgnitude (mv) Smple Smple Fig () Rw ECG Signl, () ECG signl fter pre-processing In prticulr, the WVD is lwys rel-vlued, it preserves time nd frequency shifts nd stisfies the mrginl properties. By integrting the WVD of f ll over the time-frequency plne, we otin the energy of f :.3 Feture Extrction After the detection of the R pek, the signl is divided into numer of segments equl to numer of R peks, tking 49 smples efore nd 78 smples fter ech pek, thus the totl numer of smples for ech segment s 8. Then the WVD is pplied to ech segment. Fig (),3(),4() & 5() shows the WVD plots of N et, L et, R et nd V et respectively. -D Wll slice of WVD is tken s the set of fetures. Fig (),3(),4() & 5() gives the grphs of -D wll slice tken from respective WVD. The figures clerly indicte tht the -D wll slice is different for different clss of ets..4 Bck-propgtion Neurl Network A three lyer neurl network is used with feed-forwrd ckpropgtion steepest descent lgorithm. The hidden lyer is fixed to neurons nd output lyer to 4. Sigmoidl ctivtion function of fixed prmeter is tken.

4 Snjit K. Dsh nd G. Ssihusn Ro / Procedi Computer Science 85 ( 6 ) All the weights nd ises re initilized to smll rndom vlues. After initiliztion the input vectors nd corresponding desired responses re presented to the network for trining. After trining two different dtsets re used for tsting the performnce of the clssifier. Rel prt Signl in time D Wll Slice Liner scle WV, lin. scle, surf, Threshold=5% Energy spectrl density Mgnitude (mv) Time (Smple) Fig () WVD of N-Bet with signl in Time nd Energy Spectrl Density, ()-D wll slice from TFR of N Bet Rel prt. -. Signl in time.5.5 -D Wll Slice Liner scle WV, lin. scle, surf, Threshold=5% Energy spectrl density Mgnitude (mv) Time (Smple) Fig 3 () WVD of L-Bet with signl in Time nd Energy Spectrl Density, () -D wll slice from TFR of L Bet 3. Results nd Discussion WVD sed neurl network is used for clssifiction in this work. WVD is computed for ech et using MATLAB R with time-frequency toolox. we cn see from Fig [-5] tht the WVD tke negtive vlues nd tht the locliztion otined in the time-frequency plne t the extreme end for ech et signl is lmost perfect. We lso see tht the energy is concentrted to the low frequencies only. Different positive nd negtive peks with different mplitude nd time for different clss re oserved. This shows, feture vectors re different for different clss of et. Ech feture vector consists of 8 fetures which re the input to the neurl network for trining. The trining dt contins 6 different ptterns tking 5 from ech clss for lnced trining. After trining two different test dt with different within clss imlnce rtio re used to evlute the performnce of the system. Tle I & II gives the ssessment mtrix for test dt I & II respectively. From ssessment tle we oserved tht the highest

5 8 Snjit K. Dsh nd G. Ssihusn Ro / Procedi Computer Science 85 ( 6 ) 86 8 sensitivity is % for N,L & R ets. The lowest sensitivity is 6% for V et. The highest nd lowest specificity re 99.3% nd respectively. similrly for ccurcy it is 99.77% nd 87.5%. The verge sensitivity, specificity nd ccurcy re 89.5%,93.34% nd 94.75% respectively for test-i dt nd 94.5%, 96.8% nd 97.7% respectively for test dt II. Signl in time Rel prt D Wll Slice Liner scle WV, lin. scle, surf, Threshold=5% 4 Energy spectrl density Mgnitude (mv) Time (Smple) Fig 4 () WVD of R-Bet with signl in Time nd Energy Spectrl Density, () -D wll slice from TFR of R Bet Energy spectrl density Liner scle 5 5 Rel prt Signl in time WV, lin. scle, surf, Threshold=5% Mgnitude (mv) D Wll Slice Time (Smple) Fig 5 () WVD of V-Bet with signl in Time nd Energy Spectrl Density, () -D wll slice from TFR of V Bet The performnce of the system is evluted from the ssessment mtrix. The terms used in evluting the system re defined s TP: true positive, TN: true negtive FP: flse positive, FN: flse negtive &

6 Snjit K. Dsh nd G. Ssihusn Ro / Procedi Computer Science 85 ( 6 ) ,, Tle Assessment mtrix for Test dt-i Bet Type Totl No. TP TN FP FN Sensitivity Specificity Accurcy N L R V Tle Assessment mtrix for Test Dt-II Bet Type Totl No. TP TN FP FN Sensitivity Specificity Accurcy N L R V Conclusion This pper hs presented the method of Wigner-Ville distriution chrcteriztion of the ECG signls. The locliztion energy t the wll of WVD gives the set of fetures well suited for rrhythmi detection nd et clssifiction. Though the highest ccurcy chieved is 99.77% the lowest sensitivity is 6% which is for V et. Efforts must e mde to clssify more clss with etter sensitivity nd ccurcy y selecting new fetures. References. S.Krimifrd,A.Ahmdin, Morphologicl Hert Arrhythmi Detection Using Hermitin Bsis Functions nd KNN clssifier, Proceedings of the 8 th IEEE EMBS Annul Interntionl Conference, New York City, USA, Aug 3-Sept 3,6,pp A.I.Amirudden,M.S.A.Megt,M.FSid,A.H.Jhidin & Z.H.Noor, Feture Reduction nd Arrhythmi Clssifiction vi yrid Multilyered Perceptron Network, IEEE 3 rd Interntionl Conference on System Engineering nd Technology,9- Aug 3,pp.9-94, Shh Alm, Mlysi. 3. M.Thoms,M.Ds & S.Ari, Automtic ECG rrhythmi clssifiction using dul tree complex wvelet sed fetures, Interntionl Journl of Electronics nd Communictions, Vol. 69, Issue 4, April 5, pp M.H. Vfie, M. Atei, H.R. Koofigr, Hert diseses prediction sed on ECG signls clssifiction using genetic-fuzzy system nd dynmicl model of ECG signls, Biomedicl Signl Processing nd Control, Vol. 4, Novemer 4,pp A.F.Khlf, M. I. Owis, I. A. Yssine, A novel technique for crdic rrhythmi clssifiction using spectrl correltion nd support vector mchine, vol.4,nov 5,pp Q.Li,C.Rjgoplnn,G.D.Clifford, Ventriculr Firilltion nd Tchycrdi Clssifiction Using Mchine Lerning Approch, IEEE Trnsction on Biomedicl Engineering, vol.6, NO.6, June 4,pp S.N.Yu & K.Chou, Comining Independent Component Anlysis nd Bcpropgtion Neurl Network for ECG Bet Clssifiction, proceedings of the 8 th IEEE EMBS Annul Interntionl Conference, New York city, USA, Aug 3- Sep 3,6. 8. Roshn J. Mrtis, U Rjendr Achry, L. C. Min, K. M. Mndn,Ajoy K. Ry, Chndn Chkrorty, Appliction of Higher order Cumulnt Fetures For Crdic Helth Dignosis Using ECG Signls, Interntionl Journl of Neurl Systems, Volume 3, Issue 4, August S.Mllt, A Wvelet Tour of Signl Processing, nd ed, Sn Diego, CA,USA Acdemic Press.. John O' Toole, Mostef Mesh nd Boulem Boshsh, A Discrete Time nd Frequency Wigner-Ville Distriution:Properties nd Implementtion, uthor version of rticle. Accessed from Lokenth Denth, Recent Developments in the Wigner-Ville Distriution nd Time-Frequency Anlysis, PINSA, 68,A, No., Jnury,pp, Leon Cohen, Time-Frequency Distriution A Review,Proceedings of the IEEE,Vol.77,No. 7, July, MIT-BIH Arrhythmi Dtse-Physionet, 4. J.Pn nd W.J.Tompkins, A Rel-Time QRS Detection Algorithm, IEEE Trnsction on Biomedicl Engineering, vol.3, NO.3, Mrch 985,pp.3-36.