Swarm fuzzy inference system and R wave features for ventricular premature beat detection

Abstract

This article introduces a new strategy to detect a ventricular premature beat (VPB). The strategy utilized a swarm fuzzy inference system (SFIS) and features of the R wave of electrocardiogram. SFIS was a FIS optimized using particle swarm optimization (PSO). The PSO was used to find the optimal parameters of the FIS. The fuzzification part of the FIS used a Gaussian function. The inputs of the FIS were the width and the gradient of the R wave. Using clinical data, the proposed strategy performed well for VPB detection with sensitivity, specificity and accuracy of 99.05%, 99.64% and 99.59%, respectively.