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...

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Bibliographic Details
Main Authors: Taelman, J., Van Huffel, S., Spaepen, A.
Format: Conference Proceeding
Language:English
Subjects:
Electrocardiography
Electromyography
Frequency
Heart
Muscles
Signal processing
Signal processing algorithms
Surface contamination
Surface waves
Wavelet analysis
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Summary: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.
ISSN:1094-687X
1558-4615
DOI:10.1109/IEMBS.2007.4352382