| 基于熵的动态收缩sEMG信号疲劳特征分析 |
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| 引用本文： | 陈伟婷,王志中,胡晓,李晓浦.基于熵的动态收缩sEMG信号疲劳特征分析[J].中国医学物理学杂志,2006,23(3):204-208. |
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| 作者姓名： | 陈伟婷 王志中 胡晓 李晓浦 |
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| 作者单位： | 1. 上海交通大学,生物医学工程系,上海,200240
2. 上海体育学院,运动科学系,上海,200438 |
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| 摘 要： | 频谱分析方法常被用来检测肌肉疲劳过程。本文将频率分析和非线性动力学方法结合起来，基于表面肌电（sEMG）信号在不同频率分布不均匀的特点将信号能量分解到不同频带。以此计算功率谱／小波包和熵相结合的功率谱熵／小波包熵来衡虽系统的复杂度，进而衡量肌肉的疲劳程度，为用EMG信号研究动态收缩过程中的肌肉疲劳程度提供了新的分析手段和方法。文中方法也适用于萁它生物医学信号的分析。
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| 关 键 词： | 表面肌电信号 动态收缩 疲劳度 功率谱熵 小波包熵 |
| 文章编号： | 1005-202X（2006）03-0204-05 |
| 收稿时间： | 2005-11-21 |
| 修稿时间： | 2005年11月21 |
Entropy Analysis of sEMG Signal during Dynamic Contractions for Assessing Muscle Fatigue |
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| CHEN Wei-ting,WANG Zhi-zhong,HU Xiao,LI Xiao-pu.Entropy Analysis of sEMG Signal during Dynamic Contractions for Assessing Muscle Fatigue[J].Chinese Journal of Medical Physics,2006,23(3):204-208. |
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| Authors: | CHEN Wei-ting WANG Zhi-zhong HU Xiao LI Xiao-pu |
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| Abstract: | Power spectral analysis of sEMG signal has been widely used as a predictor and indicator of muscle fatigue. Inspired by nonlinear dynamic analysis methods,we apply power spectral entropy and wavelet packet entropy that combine frequency analysis with entropy to analyze sEMG signal during dynamic contractions. The decomposition of sEMG signal to several frequency sub-bands is implemented first in that sEMG signal distributes among the whole frequency space unequally. The methods introduced here provide new measurements of muscle fatigue and can also be applied to other physiological signals. |
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| Keywords: | sEMG signal dynamic contraction fatigue index power spectral entropy wavelet packet entropy |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
