This study aimed at developing a method for automated electrocardiography (ECG) artifact
detection and removal from trunk electromyography signals. Independent Component
Analysis (ICA) method was applied to the simulated data set of ECG-corrupted surface
electromyography (SEMG) signals. Independent Components (ICs) correspond to ECG
artifact were then identified by an automated detection algorithm and subsequently
removed. The detection performance of the algorithm was compared to that by visual …