DocumentCode
1383625
Title
Influence of smoothing window length on electromyogram amplitude estimates
Author
St-Amant, Y. ; Rancourt, Denis ; Clancy, Edward A.
Author_Institution
Dept. of Mech. Eng., Laval Univ., Que., Canada
Volume
45
Issue
6
fYear
1998
fDate
6/1/1998 12:00:00 AM
Firstpage
795
Lastpage
799
Abstract
A systematic, experimental study of the influence of smoothing window length on the signal-to-noise ratio (SNR) of electromyogram (EMG) amplitude estimates is described. Surface EMG waveforms were sampled during nonfatiguing, constant-force, constant-angle contractions of the biceps or triceps muscles, over the range of 10%-75% maximum voluntary contraction. EMG amplitude estimates were computed with eight different EMG processor schemes using smoothing length durations spanning 2.45-500 ms. An SNR was computed from each amplitude estimate (deviations about the mean value of the estimate were considered as noise). Over these window lengths, average ± standard deviation SNR´s ranged from 1.4±0.28 to 16.2±5.4 for unwhitened single-channel EMG processing and from 3.2±0.7 to 37.3±14.2 for whitened, multiple-channel EMG processing (results pooled across contraction level). It was found that SNR increased with window length in a square root fashion. The shape of this relationship was consistent with classic theoretical predictions, however none of the processors achieved the absolute performance level predicted by the theory. These results are useful in selecting the length of the smoothing window in traditional surface EMG studies. In addition, this study should contribute to the development of EMG processors which dynamically tune the smoothing window length when the EMG amplitude is time varying.
Keywords
electromyography; medical signal processing; 2.45 to 500 ms; EMG signal-to-noise ratio; biceps; constant-force constant-angle contractions; electromyogram amplitude estimates; maximum voluntary contraction; smoothing window length; surface EMG waveforms; time varying amplitude; triceps; unwhitened single-channel EMG processing; whitened multiple-channel EMG processing; Amplitude estimation; Bandwidth; Biological system modeling; Electromyography; Low pass filters; Muscles; Noise level; Signal to noise ratio; Smoothing methods; Surface waves; Adult; Analysis of Variance; Electrodes; Electromyography; Female; Humans; Male; Models, Neurological; Movement; Muscle Contraction; Reference Values; Signal Processing, Computer-Assisted; Surface Properties;
fLanguage
English
Journal_Title
Biomedical Engineering, IEEE Transactions on
Publisher
ieee
ISSN
0018-9294
Type
jour
DOI
10.1109/10.678614
Filename
678614
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