Heartbeat Classification of ECG Signals Using Rational Function Systems

Abstract

The main idea of this paper is to show that rational orthogonal function systems, called Malmquist-Takenaka (MT) systems can effectively be used for ECG heartbeat classification. The idea behind using these systems is the adaptive nature of them. Then the constructed feature vector consists of two main parts, called dynamic and morphological parameters. The latter ones contain the coefficients of the orthogonal projection with respect to the MT systems. Then Support Vector Machine algorithm was used for classifying the heartbeats into the usual 16 arrhythmia classes. The comparison test were performed on the MIT-BIH arrhythmia database. The results show that our algorithm outperforms the previous ones in many respects.