Application of Support Vector Machine (SVM) for QRS detection in single lead and 12-lead Electrocardiogram (ECG) using combined entropy criterion is presented in this paper. The ECG signal is filtered using digital filtering techniques to remove power line interference and base line wander. SVM is used as a classifier for detection of QRS complexes in ECG. Using the standard CSE ECG database, both the algorithms performed highly effectively. The performance of the algorithm with sensitivity

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... of 99.79% and positive prediction (+P) of 99.15% is achieved when tested using single lead ECG. It improves to 99.93% and 99.46% respectively for simultaneously recorded 12-lead ECG signal. The percentage of false positive and false negative is low. The proposed algorithms perform better as compared with published results of other QRS detectors tested on the same database.