空域相关滤波在下肢表面肌电信号中的应用*

作者：徐文良，叶明 日期：2009-11-16/span> 浏览：3470 查看PDF文档

空域相关滤波在下肢表面肌电信号中的应用*

徐文良，叶明
（杭州电子科技大学 智能控制与机器人研究所，浙江 杭州 310018）

摘要：针对消噪过程中信号细节难以保留的问题，采用了一种基于小波变换的空域相关消噪方法。通过运用信号小波分解后与噪声的小波系数随尺度变化规律不同的特性，实现了信号与噪声的分离，同时给出了表面肌电信号噪声能量阈值的估计算法。实验结果表明，该消噪处理方法不仅能有效地去除肌电信号中的噪声，而且可以较好地保留肌电信号的边缘特征，为下肢表面肌电信号特征的提取创造了良好的条件。
关键词：表面肌电信号；小波变换；消噪；空域相关滤波
中图分类号：TP274文献标识码：A文章编号：1001-4551(2009)09-0034-04

Application of spatial correlation filtering in SEMG of the lower limb
XV Wenliang,YE Ming
(Institute of Intelligence Control and Robots, Hangzhou Dianzi University, Hangzhou 310018, China)
Abstract: Aiming at the problem of difficult to retain the details of signal in the process of denoising, a denoise method of the spatial correlation filtering(SCF) based on wavelet transformation was introduced. Through using the fact that signal and noise after wavelet decomposition have different characteristics with the wavelet scale, the separation of signal and noise was realized, and the arithmetic of estimating noiseenergy threshold of superficial electromyography(SEMG) was proposed. The experimental results indicate that the method of the signal denoising can be more effective in the denoising SEMG noise, and it can reserve the fringe character of SEMG, which offers good condition for withdrawing the characters of SEMG of the lower limb.
Key words: superficial electromyography(SEMG); wavelet transformation; denoising; spatial correlation filtering(SCF)
参考文献(References):
［1］DELUCA C. Physiology and mathematics of myoelectric signals［J］. IEEE Trans. Biomed. Eng.,1979,26(3):313-325.
［2］POPOVIC M R, KELLER T, PAPPAS I P, et al. Surfacestimulation technology for grasping and walking neuroprosthesis［J］. IEEE Engineering in Medicine and Biology,2001,20(1):82-93.
［3］潘泉,张磊,孟晋丽,等.小波滤波方法及应用［M］.1版.北京:清华大学出版社,2005.
［4］杨福生.小波变换的工程分析与应用［M］.1版.北京:科学出版社,1999.
［5］MALLAT S, HWANG W L. Singularity detection and processing with wavelets［J］. IEEE Trans. Inform. Theory,1992,38(2):617-643.
［6］WITKIN A. Scale space filtering［C］//Proc. 8th Int. Joint Conf. Artificial Intell. Karlsrune Germany,1988:1019-1021.
［7］XU Y S, WEAVER J B, HEALY D M, et al. Wavelet transform domain filters: a spatially selective noise filtration technique［J］. IEEE Trans. Image Processing,1994,3(6):747-758.
［8］陈晓楠,索继东.空域相关滤波法在突变检测中的应用［J］.大连海事大学学报,2007,33(12):139-141.
［9］李仲宁,罗志增.基于小波变换的空域相关法在肌电信号中的应用［J］.电子学报,2007,35(7):1414-1418.