基于经验模态分解的牵张反射起始点检测研究  

Stretch reflex onset detection based on empirical mode decomposition

作　　者：杜明家 胡保华[1] 肖飞云 刘正士[1] 王勇[1] Du Mingjia;Hu Baohua;Xiao Feiyun;Liu Zhengshi;Wang Yong(School of Mechanical Engineering,Hefei University of Technology,Hefei 230009,China)

机构地区：[1]合肥工业大学机械工程学院,合肥230009

出　　处：《电子测量与仪器学报》2020年第4期27-32,共6页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金(U1713210)资助项目。

摘　　要：针对痉挛状态患者表面肌电信号易出现虚假的肌电峰值,引起牵张反射起始点前后的信号差异变小,提出经验模态分解去噪与改进样本熵识别的牵张反射起始点检测方法。首先用经验模态分解对肌电信号进行分解;然后以受试者静息状态下的表面肌电信号为参考,设定软阈值对分解的信号进行去噪;最后用改进样本熵识别牵张反射起始点。实验结果表明,经验模态分解算法可以有效地去除肌电信号噪声,而且在改进样本熵的最优参数下牵张反射起始点平均识别率为94%。In view of the possibility of false peaks on the surface electromyography(sEMG)of patients with spasticity,leading to decreased signal differences before and after stretch reflex onset(SRO),a method for detecting SRO based on empirical mode decomposition(EMD)denoising and modified sample entropy recognition is proposed.First,the EMG signal is decomposed via EMD.Then,the soft threshold is set to denoise the decomposed signal on the basis of the sEMG signal of the subjects in resting state.Lastly,modified sample entropy is used to identify SRO.The experimental results show that the EMD algorithm can effectively remove noise from the EMG signal,and the average recognition rate of SRO under the optimal parameter of the modified sample entropy is 94%.

关 键 词：痉挛状态 牵张反射起始点 表面肌电信号 经验模态分解 

分 类 号：TN06[电子电信—物理电子学] R318.04[医药卫生—生物医学工程]