Comparison of seven approaches for holter ECG clustering and classification

Abstract

In this work we present a comparative study, testing selected methods for clustering and classification of holter electrocardiogram (ECG). More specifically we focus on the task of discriminating between normal 'N' beats and premature ventricular 'V' beats Some of the tested methods represent the state of the art in pattern analysis, while others are novel algorithms developed by us. All the algorithms were tested on the same datasets, namely the MIT-BIH and the AHA databases. The results for all the employed methods are compared and evaluated using the measures of sensitivity and specificity.