NDLI: Applying Independent Component Analysis on ECG Cancellation Technique for the Surface Recording of Trunk Electromyography

Content Provider	IEEE Xplore Digital Library
Author	Yong Hu Li, X.H. Xie, X.B. Pang, L.Y. Yuzhen Cao Luk, K.D.K.
Copyright Year	2005
Description	Author affiliation: Inst. of Biomed. Eng., Chinese Acad. of Med. Sci. (Yong Hu; Li, X.H.; Xie, X.B.; Pang, L.Y.)
Abstract	Surface electromyography (sEMG) recorded from the trunk area may reflect underlying muscular function, and is the current standard for in vivo functional examination. However, sEMG of this area, including the low back musculature, usually encounters substantial interference from strong cardiac signals. It is therefore imperative to remove electrocardiogram (ECG) interference from sEMG data. This paper discusses a denoise method using independent component analysis (ICA) and a high-pass filter to effectively suppress the interference of ECG in sEMG recorded from trunk muscles. The performance of this technique was evaluated with simulation experiments. To compare the outcome of the ICA and filtering technique to the original sEMG signal, correlation coefficients in both time-domain waveform and frequency spectrum were computed. In addition, different filter bands were evaluated. The ICA ECG cancellation with a 30 Hz high-pass filter showed higher mean correlation coefficients in the time domain (0.97plusmn0.08) and in the frequency spectrum (0.99plusmn0.06) than any other techniques. This suggests that the ICA ECG cancellation technique with a 30 Hz high-pass filter would be the most appropriate method to extract useful sEMG signals from trunk muscles
Starting Page	3647
Ending Page	3649
File Size	296614
Page Count	3
File Format	PDF
ISBN	0780387414
DOI	10.1109/IEMBS.2005.1617272
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Independent component analysis Electrocardiography Electromyography Filters Muscles Frequency In vivo Interference suppression Computational modeling Filtering noise cancellation Surface electromyography (sEMG) independent component analysis (ICA) electrocardiogram (ECG)
Content Type	Text
Resource Type	Article

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