Comparative Study of Qrs Detection in Single Lead and 12-lead Ecg Based on Entropy and Combined Entropy Criteria Using Support Vector Machine

Application of Support Vector Machine (SVM) for QRS detection in single lead and 12-lead Electrocardiogram (ECG) using entropy and 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 (Se) of 99.70% and positive prediction (+P) of 97.75% is achieved when tested using single lead ECG with entropy criteria. It improves to 99.79% and 99.15% respectively for combined entropy criteria. Similarly for simultaneously recorded 12-lead ECG signal, sensitivity of 99.93% and positive prediction of 99.13% is achieved when tested using entropy criteria and sensitivity of 99.93% and positive prediction of 99.46% respectively is achieved for combined entropy criteria. The percentage of false positive and false negative are reduced substantially when simultaneously recorded 12-lead ECG signal is used. The proposed algorithms perform better as compared with published results of other QRS detectors tested on the same database.