a low complexity anfis approach for premature ventricular contraction detection based on backward elimination

premature ventricular contraction (pvc) is one of the common cardiac arrhythmias. the occurrence of pvc is dangerous in people who have recently undergone heart. a pvc beat can easily be diagnosed by a doctor based on the shape of the electrocardiogram signal. but in automatic detection, extracting several important features from each beat is required. in this paper, a method for automatic detection of pvc using adaptive neuro-fuzzy inference systems (anfis) is presented. in the proposed model first feature selection has been done using backward elimination algorithm, and then an anfis has been trained with selected attributes. the performance of the proposed method has been compared with two other methods. simulation results show that the proposed algorithm, in addition to maintaining the classification accuracy compared to existing methods uses fewer features and requires less computing time, which is suitable for implementation on hardware with limited processing capability.