COMPARATIVE STUDY ON CLASSIFICATION OF ECG ARRHYTHMIA USING SINGLE CLASSIFIER AND ENSEMBLE OF CLASSIFIERS

An electrocardiogram (ECG) is a bioelectrical signal which records the heart's electrical activity versus time. The interpretation of ECG signal is an application of pattern recognition. The techniques used in this paper comprise: signal pre- processing, R peak detection, QRS reconstruction, RR interval detection, feature extraction and linear classifier model versus ensemble of classifier model. The processed signal source came from the Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) arrhythmia database which was developed for research in cardiac electro-physiology. The results of recognition rates are compared to find a better structure for ECG classification. Among different classifier model, it was found that ensemble of classifier with DECORATE meta-learner model possessed the best performance with highest recognition rate of 90.36% for cardiac conditions and moderate level of agreement between computerized prediction and cardiologist interpretation. Based on this result, the method of using important ECG features plus a suitable ensemble of classifier model outperforms the single classifier model and which can increase the testing speed and the accuracy rate.