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http://dx.doi.org/10.6109/jkiice.2010.14.1.016

EMD based Cardiac Arrhythmia Classification using Multi-class SVM  

Lee, Geum-Boon (조선대학교 컴퓨터공학과)
Cho, Beom-Joon (조선대학교 컴퓨터공학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.14, no.1, 2010 , pp. 16-22 More about this Journal
Abstract
Electrocardiogram(ECG) analysis and arrhythmia recognition are critical for diagnosis and treatment of ill patients. Cardiac arrhythmia is a condition in which heart beat may be irregular and presents a serious threat to the patient recovering from ventricular tachycardia (VT) and ventricular fibrillation (VF). Other arrhythmias like atrial premature contraction (APC), Premature ventricular contraction (PVC) and superventricular tachycardia (SVT) are important in diagnosing the heart diseases. This paper presented new method to classify various arrhythmias contrary to other techniques which are limited to only two or three arrhythmias. ECG is decomposed into Intrinsic Mode Functions (IMFs) by Empirical Mode Decomposition (EMD). Burg algorithm was performed on IMFs to obtain AR coefficients which can reduce the dimension of feature vector and utilized as Multi-class SVM inputs which is basically extended from binary SVM. We chose optimal parameters for SVM classifier, applied to arrhythmias classification and achieved the accuracies of detecting NSR, APC, PVC, SVT, VT and VP were 96.8% to 99.5%. The results showed that EMD was useful for the preprocessing and feature extraction and multi-class SVM for classification of cardiac arrhythmias, with high usefulness.
Keywords
ECG; Cardiac Arrhythmias; AR coefficient; Multi-class SVM; EMD;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 M.I.Oiws, A.H.Abou-Zied, A.M.Youssef, "Study of Features based on Nonlinear Dynamical Modeling in ECG Arrhythmia Detection and Classification," IEEE Trans. Biomedical Eng., vol. 49, no. 7, pp. 733-736, 2002.   DOI   ScienceOn
2 H.X.Zhang, Y.S.Zhu, Z.M.Wang, "Complexity measure and complexity rate information based detection of ventricular tachycardia and fibrillation," Medical and Biological Eng. and Comp., vol. 38, no. 5, pp. 553-557, 2000.   DOI   ScienceOn
3 이금분, 조범준, "EMD방법을 이용한 ECG 신호 필터링," 한국해양정보통신학회논문지, vol. 13 no. 12, pp.2671-2676, 2009.
4 M.G.Tsipouras, V.P.Oikonomou, Di Fotiadis, L.K.Michalis, D.Sideris, "Classification of atrial tachyarrhythmias in electrocardiograms using time frequency analysis," Comp. in Cardiology, pp. 245-248, 2004.
5 N.E.Huang, Z.Shen, S.R.Long, M.C.Wu, E.H.Shih, Q.Zheng, Yen. N.-C., C.C.Tung and H.H.Liu, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," Proc. Roy. Soc., London. A, vol. 454, pp. 903-995, 1998.   DOI   ScienceOn
6 R.Silipo, C.Marchesi, "Artificial Neural Networks for Automatic ECG Analysis," IEEE Trans. Signal Processing, vol. 46, pp. 1417-1425, 1998.   DOI   ScienceOn
7 M.Nambakhsh, V.Tavakoli, N.Sahba, "FPGA -core defibrillator using wavelet-fuzzy ECG arrhythmia classification," in Proc. of IEEE Conf. on Engineering in Medicine and Biology Society, pp. 2673-2676, 2008.
8 H.Zhang and Q.Gai, "Research on properties of empirical mode decomposition method," Proc. of the 6th World Congress on Intelligent Control and Automation, vol. 2, pp. 10001-10004, 2006.
9 M.B.Anuradha, V.C.Veera Reddy, "Cardiac Arrhythmia Calssification using Fuzzy Classifiers," Journal of Theoretical and Applied Information Technology, vol. 4, no. 4, pp. 353-359, 2005.
10 I.Chouvarda, N.Maglaveras, C.Pappas, "Adaptive time-frequency ECG analysis," Computers in Cardiology 2001, pp.265-268, 2001.
11 S.L.Melo, L.P.Caloba, J.Nadal, "Arrhythmia analysis using artificial neural network and decimated electrocardiographic data," Comp. in Cardiology, vol.27, pp. 355-358, 2000.