PcHD: Personalized classification of heartbeat types using a decision tree

The computer-aided interpretation of electrocardiogram (ECG) signals provides a non-invasive and inexpensive technique for analyzing heart activity under various cardiac conditions. Further, the proliferation of smartphones and wireless networks makes it possible to perform continuous Holter monitoring. However, although considerable attention has been paid to automated detection and classification of heartbeats from ECG data, classifier learning strategies have never been used to deal with individual variations in cardiac activity. In this paper, we propose a novel method for automatic classification of an individual׳s ECG beats for Holter monitoring. We use the Pan-Tompkins algorithm to accurately extract features such as the QRS complex and P wave, and employ a decision tree to classify each beat in terms of these features. Evaluations conducted against the MIT-BIH arrhythmia database before and after personalization of the decision tree using a patient׳s own ECG data yield heartbeat classification accuracies of 94.6% and 99%, respectively. These are comparable to results obtained from state-of-the-art schemes, validating the efficacy of our proposed method.