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Enhancement of spectral analysis of myoelectric signals during static contractions using wavelet methods
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
Umeå University, Faculty of Science and Technology, Mathematical statistics.
Thayer School of Engineering, Dartmouth College.
1999 (English)In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 46, no 6, 670-684 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we introduce wavelet packets as an alternative method for spectral analysis of surface myoelectric(ME) signals. Both computer synthesized and real ME signals are used to investigate the performance. Our simulation results show that wavelet packet estimate has slightly less mean squareerror (MSE) than Fourier method, and both methods perform similarly on the real data. Moreover, wavelet packets give us some advantages over the traditional methods such as multiresolutionof frequency, as well as its potential use for effecting time-frequency decomposition of the nonstationary signals such as the ME signals during dynamic contractions. We also introduce wavelet shrinkage method for improving spectral estimates bysignificantly reducing the MSE’s for both Fourier and wavelet packet methods.

Place, publisher, year, edition, pages
1999. Vol. 46, no 6, 670-684 p.
Keyword [en]
Autoregressive moving average (ARMA), Fourier transform, myoelectric signal (ME), power spectral density, spectral estimation, wavelet transform (WT), wavelet packets (WP), WP spectrum, wavelet shrinkage, wavelet spectrum.
National Category
Medical Engineering
Research subject
Signal Processing
URN: urn:nbn:se:umu:diva-63699DOI: 10.1109/10.764944ISI: 000080461900007OAI: diva2:582395
Available from: 2013-01-04 Created: 2013-01-04 Last updated: 2013-10-08Bibliographically approved

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