Cardiac arrhythmia classification using wavelets and hidden markov models – a comparative approach

Abstract

This paper reports a comparative study of feature extraction methods regarding cardiac arrhythmia classification, using state of the art Hidden Markov Models. The types of beat being selected are normal (N), premature ventricular contraction (V) which is often precursor of ventricular arrhythmia, two of the most common class of supra-ventricular arrhythmia (S), named atrial fibrillation (AF), atrial flutter (AFL), and normal rhythm (N). The considered feature extraction methods are the standard linear segmentation and wavelet based feature extraction. The followed approach regarding wavelets was to observe simultaneously the signal at different scales, which means with different level of focus. Experimental results are obtained in real data from MIT-BIH Arrhythmia Database and show that wavelet transform outperforms the conventional standard linear segmentation.