一种肌肉动态收缩的高密度表面肌电信号分解新方法  

A new method for high-density surface electromyography decomposition in dynamic muscle contraction

作　　者：何金保[1] 管冰蕾[1] 黄凯[2] 骆再飞[1] HE Jinbao;GUAN Binglei;HUANG Kai;LUO Zaifei(The School of Electronic and Information Engineering,Ningbo University of Technology,Ningbo,Zhejiang 315211,P.R.China;Ningbo First Hospital,Ningbo,Zhejiang 315010,P.R.China)

机构地区：[1]宁波工程学院电信学院,浙江宁波315211 [2]宁波市第一医院,浙江宁波315010

出　　处：《生物医学工程学杂志》2021年第6期1081-1086,共6页Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(61403218);宁波市自然科学基金(2019A610096)。

摘　　要：针对肌肉动态收缩情况下的高密度表面肌电(sEMG)信号,本文提出了一种基于空间位置的sEMG信号分解方法。首先,根据肌肉运动单元(MU)在各个通道上的波形相关性,提取发放时刻,然后利用肌肉MU的空间位置分类发放时刻,最后得到MU发放序列。仿真结果表明,分类后单个MU发放序列准确率大于91.67%。针对实际sEMG信号,通过“二源法”找到同一个MU发放序列的准确率达到(88.3±2.1)%以上。本文为动态sEMG信号分解提供了一种新思路。In this paper, a new surface electromyography(sEMG) signal decomposition method based on spatial location is proposed for the high-density sEMG signals in dynamic muscle contraction. Firstly, according to the waveform correlation of each muscle motor units(MU) in each channel, the firing times are extracted, and then the firing times are classified by the spatial location of MU. The MU firing trains are finally obtained. The simulation results show that the accuracy rate of a single MU firing train after classification is more than 91.67%. For real sEMG signals, the accuracy rate to find a same MU by the “two source” method is over(88.3 ± 2.1)%. This paper provides a new idea for dynamic sEMG signal decomposition.

关 键 词：运动单元 表面肌电 动态信号分解 空间位置 

分 类 号：R318[医药卫生—生物医学工程] TN911.7[医药卫生—基础医学]