Abstract
Cardiac arrhythmias stand a great admonish for human beings nowadays. The proposed work intends to classify four commonly occurring arrhythmia classes along with normal class. For each beat of 300 samples, both morphological and rhythmic features are determined. A total of 129 morphological features are formed by 114 wavelets coefficients and 15 independent components having 300 coefficients of basis functions obtained by using ICA. PCA is applied on the morphological features to derive the best 11 principal components and to this, four rhythmic features are combined to have a final 15 feature coefficients. SVM classifier gets trained using the 15 features of 30% beats of every class in the total number of beats. The remaining 70% of beats are used for evaluating the individual class performance. Finally the SVM classifier with only 15 features is able to produce the overall accuracy of 99.29% for a total 82,978 beats.
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