SVM based methods for arrhythmia classification in ECG

Abstract

In this study, Support Vector Machine (SVM) based methods have been used to classify the electrocardiogram (ECG) arrhythmias. Among various existing SVM methods, three well-known and widely used algorithms one-against-one, one-against-all, and fuzzy decision function are used here to distinguish between the presence and absence of cardiac arrhythmia and classifying them into one of the arrhythmia groups. The various types of arrhythmias in the Cardiac Arrhythmias ECG database chosen from University of California at Irvine (UCI) to train SVM, include ischemie changes (coronary artery disease), old inferior myocardial infarction, sinus bradycardy, right bundle branch block, and others. The results obtained through implementation of all three methods are thus compared as per their accuracy rate in percentages and the performance of the SVM classifier using one-against-all (OAA) method was found to be better than other techniques. ECG arrhythmia data sets are of generally complex nature and SVM based one-against-all method is found to be of vital importance for classification based diagnosing diseases pertaining to abnormal heart beats.