Arrhythmia classification based on multi-domain feature extraction

Abstract

Abstract Arrhythmia is a common abnormality of cardiac electrical activity. Arrhythmia classification has enormous significance for the clinical diagnosis of cardiovascular diseases. In this study, a method of multi-domain electrocardiogram feature extraction was put forward as to classify arrhythmia precisely. The RR intervals were extracted as time domain feature. The fifth level approximation coefficients of wavelet decomposition were adopted to represent frequency domain feature. Besides, the sample entropy values of six wavelet coefficients were employed as nonlinear feature. These three features were fed to classifier for automated diagnosis. Furthermore, ten-fold cross-validation scheme was adopted to train and test classifier whose parameters were optimized by genetic algorithm. In this study, eight classes of the most frequently occurring arrhythmia from MIT-BIH arrhythmia database were validated. The result turned out that the SVM classifier yields an average accuracy of 99.70%. Compared with the existing methods, the proposed method shows better results.