Discrete hidden Markov model classifier for premature ventricular contraction detection

Abstract

The automatic detection and classification of cardiac arrhythmia is important for diagnosis of cardiac abnormalities. Premature Ventricular Contraction (PVC) can be seen in Electrocardiogram (ECG) as an abnormal wave shape of the QRS complex. A new scheme is proposed for the detection of premature ventricular beats, which is a vital function in rhythm monitoring of cardiac patients. This work is focused on the classification of normal sinus rhythm and PVCs of the human heart. A new approach for the automated classification of ECG signals is proposed, based on the framework of probabilistic modelling. The aim is to classify normal from PVC beats using Discrete Hidden Markov Models (DHMMs). The ECG data are taken from standard MIT-BIH arrhythmia database for the evaluation of the proposed method. The experimental results have shown that our approach is simple and effective in clarifying the final decision of the classifier while preserving its accuracy at a satisfactory level.