Wavelet-independent component analysis to remove electrocardiography contamination in surface electromyography

Abstract

Removing artifacts from biomedical signals, such as surface electromyography (sEMG), has become a major research topic in biomedical signal processing. In electromyography signals, a source of contamination is the electrophysiological signal of the heart (ECG signals). This contamination influences features extracted from the sEMG, especially during low-activity measurements of the muscles such as during mental stress. As the heart is a muscle, the frequency content of the heart signals overlaps the frequency content of the muscle signals, so basic frequency filtering is not possible. In this paper, we present the results of a recently developed algorithm: wavelet-independent component analysis. We compare these results with the widely described algorithm of ECG template subtraction for removing ECG contamination.