Medical engineering & physics
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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. ⋯ Better performance indicated by a significantly higher correlation coefficient (p<0.001) with the original EMG recordings was found in the SEMG data cleaned by the ICA-based method, than that by BW HPF 30. The automated ECG-artifact removal method for trunk SEMG recordings proposed in this study was demonstrated to produce a very good detection rate and preserved essential EMG components while keeping its distortion to minimum. The automatic nature of our method has solved the problem of visual inspection by standard ICA methods and brings great clinical benefits.