ECG Beat Classification Using Evidential K -Nearest Neighbours

The Electrocardiogram (ECG) signal provides a useful non-interventional method for identifying cardiac arrhythmias. In this paper, we look at automatic ECG beat classification into 2 categories-Normal and Premature ventricular contraction using Dempster Shafer Theory (DST). In biomedical signal classification problems, the cost of making an erroneous decision can be high. Deferring a decision rather than taking a wrong decision might be beneficial. This is done by using the evidential k nearest neighbours (EKNN) approach which is based on Dempster Shafer Theory for classifying the ECG beats. RR interval features are used. Analysis is done on the MIT-BIH database. Performance evaluation is done by considering error rates. Performance of EKNN is compared with traditional k nearest neighbours (maximum voting) approach. Effect of training datasize is assessed by using training sets of varying sizes. The EKNN based classification system is shown to consistently outperform the KNN based classification system.