Wavelet-independent component analysis to remove electrocardiography contamination in surface electromyography.
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.
Taelman
Joachim
J
Department of Biomedical Kinesiology, Katholieke Universiteit Leuven, 3001 Heverlee, Belgium. joachim.taelman@faber.kuleuven.be
Van Huffel
Sabine
S
Spaepen
Arthur
A
D000328:Adult; D000465:Algorithms; D004562:Electrocardiography / Q000379:methods*; D004576:Electromyography / Q000379:methods*; D001330:Electronic Data Processing / Q000379:methods*; D006801:Humans; D008297:Male; D009119:Muscle Contraction; D013315:Stress, Psychological / Q000503:physiopathology*