基于模糊近似熵与辛几何的肌肉疲劳分析  

Analysis of fatigue muscle based on fuzzy approximate entropy and symplectic genmetry

作　　者：汪晨[1] 和卫星[1] 陈晓平[1] 

机构地区：[1]江苏大学电气信息工程学院生物医学工程系,镇江212013

出　　处：《现代仪器与医疗》2013年第4期1-4,共4页Modern Instruments & Medical Treatment

摘　　要：为探索人体肌肉在运动后疲劳程度的变化,本文运用非线性动力学中的模糊近似熵和辛几何2种方法 ,对12名健康受试者在腕关节负重60%最大自主性施力、直至力竭的情况下,采集静态表面肌电信号作为样本,进行处理分析。模糊近似熵是近似熵算法的改进,可以更好地衡量一个序列的复杂程度;辛几何则运用相空间重构原理,提取主分量谱。试验结果初步表明,应用上述2种方法可相互补充,对受试者的肌肉疲劳程度做出有效、定性的评估,而且2种方法对肌肉信号处理结果的一致性有助于进一步描述肌肉系统的生物力学特征。To make exploratory work for the change in fatigue level of human muscle after exercise,this paper uses two methods： fuzzy approximate entropy（fApEn） and symplectic geometry（SG） from nonlinear dynamics.In the experiment,static surface EMG signals collected from twelve healthy subjects under the wrist weight-bearing 60% of maximum autonomy force until exhaustion,as the samples for processing and analysis.Fuzzy approximate entropy is improved by the approximate entropy algorithm,it can be better to measure the complex degree of a sequence and the symplectic geometry uses the principle of phase space reconstruction to extract the main component spectrum of a sequence.The preliminary results shows that the application of the above methods can make effective and qualitative assessment for the muscle fatigue degree of the subjects,and can complement in practical applications.Using the two methods,the consistency result of the processing of the muscle signal will contribute to further describe the biomechanical characteristics of the muscular system.

关 键 词：表面肌电信号 肌肉疲劳 模糊近似熵 辛几何 香农熵 

分 类 号：R318[医药卫生—生物医学工程]