Classification of ECG beats using cross wavelet transform and support vector machines

In this paper, heart beat classification is performed using cross wavelet transform (XWT), and support vector machines (SVM). XWT is used for the analysis and classification of electrocardiogram (ECG) signals. The cross-correlation between two time domain signals gives a measure of similarity between two waveforms. The proposed algorithm uses XWT to analyze ECG data and determine wavelet coherence (WCOH) and wavelet cross spectrum (WCS). WCOH and WCS obtained are used mathematically to determine the parameter(s) for the purpose of classification. SVM is used to classify the beats based on the parameters calculated from WCOH and WCS. MIT-BIH arrhythmia database is used for evaluation of results. An overall accuracy of 94.8% for SVM based classification and 96.2% for two dimensional SVM based classification was obtained using the proposed method.