DocumentCode
1082437
Title
Single site electromyograph amplitude estimation
Author
Clancy, Edward A. ; Hogan, Neville
Author_Institution
Dept. of Electr. Eng., MIT, Cambridge, MA, USA
Volume
41
Issue
2
fYear
1994
Firstpage
159
Lastpage
167
Abstract
Previous investigators have experimentally demonstrated and/or analytically predicted that temporal whitening of the surface electromyograph (EMG) waveform prior to demodulation improves the EMG amplitude estimate. However, no systematic study of the influence of various whitening filters upon amplitude estimate performance has been reported. The authors describe a phenomenological mathematical model of a single site of the surface EMG waveform and reports on experimental studies which examined the performance of several temporal whitening filters. Surface EMG waveforms were sampled during nonfatiguing, constant-force, isometric contractions of the biceps or triceps muscles, over the range of 10-75% maximum voluntary contraction. A signal-to-noise ratio (SNR) was computed from each amplitude estimate (deviations about the mean value of the estimate were considered as noise). A moving average root mean square estimator (245 ms window) provided an average±standard deviation (A±SD) SNR of 10.7±3.3 for the individual recordings. Temporal whitening with one fourth-order whitening filter designed per site improved the A±SD SNR to 17.6±6.0.
Keywords
bioelectric potentials; muscle; physiological models; 245 ms; biceps muscle; moving average root mean square estimator; nonfatiguing constant-force isometric contractions; phenomenological mathematical model; signal-to-noise ratio; single site electromyograph amplitude estimation; surface EMG waveform; temporal whitening; triceps muscle; voluntary contraction; Amplitude estimation; Demodulation; Electromyography; Filters; Mathematical model; Muscles; Noise level; Root mean square; Signal to noise ratio; Surface waves; Adult; Biomechanics; Calibration; Elbow; Electromyography; Female; Fourier Analysis; Humans; Isometric Contraction; Male; Models, Biological; Models, Statistical; Reference Values; Signal Processing, Computer-Assisted;
fLanguage
English
Journal_Title
Biomedical Engineering, IEEE Transactions on
Publisher
ieee
ISSN
0018-9294
Type
jour
DOI
10.1109/10.284927
Filename
284927
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