Spatially Selective Noise Filtration Based on Wavelet Transform Using in SEMG De-noising
Spatially selective noise filtration (SSNF) for SEMG de-noising is put forwarded based on wavelet transform in order to eliminate the noise included in SEMG signal and hold details of the signal. The paper briefly summarizes the basic theory of wavelet transform and SSNF at first. Secondly, the paper discusses the process of SEMG de-noising with SSNF in detail. With wavelet analysis, the signal and noise is separated by SSNF. At the same time, the arithmetic of estimating noise-energy threshold of SEMG signal is proposed. The experimental results show that the fringe characters of SEMG signal are reserved effectively through SSNF, which offer good conditions for withdrawing the Characters of SEMG signal.
surface electromyography (SEMG) wavelet transform spatially selective noise filtration (SSNF) correlation coefficient
Zhizeng Luo Zhongning Li
Robot Research Institute Hangzhou Dianzi University Hangzhou, Zhejiang Province,China,310018
国际会议
The International Colloquium on Onformation Fusion 2007(2007年国际信息融合研讨会)
西安
英文
345-348
2007-08-22（万方平台首次上网日期，不代表论文的发表时间）