ECG Arrhythmia Detection and Classification Using Relevance Vector Machine

Abstract

The Electrocardiogram (ECG) is one of the most effective diagnostic tools to detect cardiac diseases. It is a method to measure and record different electrical potentials of the heart. The electrical potential generated by electrical activity in cardiac tissue is measured on the surface of the human body. This ECG can be classified as normal and abnormal signals. In this paper, a thorough experimental study was conducted to show the superiority of the generalization capability of the Relevance Vector Machine (RVM) in the automatic classification of ECG beats. To achieve the maximum accuracy the RVM classifier design by searching for the best value of the parameters that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. The experiments were conducted on the ECG data from the Massachusetts Institute of Technology–Beth Israel Hospital (MIT–BIH) arrhythmia database to classify five kinds of abnormal waveforms and normal beats. The obtained results clearly confirm the superiority of the RVM approach when compared to traditional classifiers.