Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6579
Title: ECG beats classification using multiclass support vector machines with error correcting output codes
Authors: Übeyli, Elif Derya
Keywords: multiclass support vector machine (SVM)
wavelet coefficients
electrocardiogram (ECG) beats
Issue Date: 2007
Publisher: Academic Press Inc Elsevier Science
Abstract: A new approach based on the implementation of multiclass support vector machine (SVM) with the error correcting output codes (ECOC) is presented for classification of electrocardiogram (ECG) beats. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were analyzed. The ECG signals were decomposed into time-frequency representations using discrete wavelet transform (DWT) and wavelet coefficients were calculated to represent the signals. The aim of the study is the classification of ECO beats by the combination of wavelet coefficients and multiclass SVM. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrated that the wavelet coefficients are the features which well represent the ECG signals and the multiclass SVM trained on these features achieved high classification accuracies. (c) 2006 Elsevier Inc. All rights reserved.
URI: https://doi.org/10.1016/j.dsp.2006.11.009
https://hdl.handle.net/20.500.11851/6579
ISSN: 1051-2004
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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