Development of entropy based algorithm for cardiac beat detection in 12-lead electrocardiogram

This paper presents an entropy-based algorithm for the detection of QRS complexes (cardiac beat) in the 12-lead Electrocardiogram (ECG) using support vector machine (SVM). Digital filtering techniques are used to remove power line interference and baseline wander in the ECG signal. Entropy criterion is used to enhance the QRS complexes. SVM is used as a classifier to delineate QRS and non-QRS regions. The performance of the proposed algorithm was tested using 12-lead real ECG recordings from the standard CSE ECG database. The numerical results indicated that the algorithm achieved 99.93% of detection rate. The percentage of false positive and false negative is 0.87% and 0.06%, respectively. The performance of the proposed algorithm is found to be better than published results of the other QRS detectors tested on the same database.