Enhancement of spectral-analysis of myoelectric signals during static contractions using wavelet methods

被引：93

作者：

Karlsson, S ^{[1
]}

Yu, J

Akay, M

机构：

[1] Umea Univ Hosp, Dept Biomed Engn & Informat, S-90185 Umea, Sweden

[2] Fac Hlth Sci, Dept Rehabil Med, S-58185 Linkoping, Sweden

[3] Umea Univ, Dept Math Stat, S-90187 Umea, Sweden

[4] Dartmouth Coll, Thayer Sch Engn, Hanover, NH 03755 USA

来源：

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | 1999年 / 46卷 / 06期

关键词：

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;

D O I：

10.1109/10.764944

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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 square error (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 multiresolution of 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 by significantly reducing the: MSE's for both Fourier and wavelet packet methods.

引用

页码：670 / 684

页数：15

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