Abstract
Heart diseases are caused by a multitude of reasons including abnormal propagation of pacing impulses through the specialized cardiac conduction system. Such abnormalities where cardiac rhythm deviates from normal sinus rhythm are termed as arrhythmia. The present contribution concentrates on the application of Multicategory support vector machines (MC-SVMs) for arrhythmia classification. This system of classification comprises of several units including signal preprocessing, wavelet transform (WT) for feature extraction and support vector machine with Gaussian kernel approximation of each arrhythmia class. Training and testing has been done on standard MIT-BIH Arrhythmia database. A systematic and comprehensive evaluation of this algorithm has been conducted where 25 features are being extracted from each arrhythmia beat by wavelet transform, for multi-category classification. Upon implementing MC-SVM techniques one-versus-one, DAGSVM was found to be the most suitable algorithm in this domain. The overall accuracy of classification of the proposed method is 98.50%. This system is flexible, and implements a prototype graphical user interface (GUI) based on MATLAB. The results shown in this paper prove that the method can classify arrhythmia from given ECG data.
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Khadtare, M.S., Sahambi, J.S. (2004). ECG Arrhythmia Analysis by Multicategory Support Vector Machine. In: Manandhar, S., Austin, J., Desai, U., Oyanagi, Y., Talukder, A.K. (eds) Applied Computing. AACC 2004. Lecture Notes in Computer Science, vol 3285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30176-9_13
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DOI: https://doi.org/10.1007/978-3-540-30176-9_13
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