ECG beat classification using wavelets and SVM - Faziludeen, Shameer; Sabiq, P. V. (2013 IEEE Conference on Information and Communication Technologies, 2013)
Electrocardiogram (ECG) is one of the most important noninvasive tools for the diagnosis of cardiac arrhythmia. Automatic beat classification in ECG is a topic of continuing research. In this paper, automatic classification of 3 beat types - normal sinus rhythm, premature ventricular contraction and left bundle branch block is implemented. QRS detection is done using the Pan Tompkins algorithm. Wavelet decomposition using daubechies 4 wavelet is done. 25 features are extracted for each beat from wavelet analysis, namely - mean, variance, standard deviation, minimum and maximum of detail coefficients and of approximation coefficients. 3 RR interval features are also extracted for each beat. Beat classification is implemented by using OAO (One Against One) SVM (Support Vector Machine). 3 SVM's are designed and final grouping is done by maximum voting. Novel method of feature selection is introduced. Feature selection for a particular SVM is done based on the beats to be classified by that SVM. ECG signals are obtained from the open source MIT-BIH cardiac arrhythmia database. 6355 beats (2036 LBB, 3865 N, 454 PVC) are used for testing the implementation. Accuracy of 98.46%, 98.47% and 99.92% are obtained for left bundle branch block, normal and premature ventricular contraction beats respectively.