Abstract

This paper analyzes the efficacy of applying one class classifiers (OCCs) to the problem of abnormal beat detection in ECG. It also proposes a novel OCC called Quadratic Programming Dissimilarity representation based Data Descriptor (QPDDD). A comparison of the proposed classification technique with existing classifiers over the MIT-BIH arrhythmia database is presented. Results show that OCCs coupled with wavelet domain features present a practical, robust and scalable solution for handling inter-individual variability in ECG patterns of different types of cardiac beats. An equal error rate of 90-95% was obtained for the MIT-BIH arrhythmia database depending upon the amount of training data used. A major advantage of the proposed scheme is that it requires only normal beats during its training. Another advantage is that it is able to handle inter-individual differences in ECG morphologies as the training takes place separately for each individual.