基于模糊聚类技术的肌电信号完全分解算法

肌电(EMG)信号分解是EMG信号产生的逆过程。通过EMG分解获取完整的运动单元(MU)的波形和发放信息,需完成复杂的叠加波形分解过程。首先,基于小波滤波和小波阈值估计技术去除EMG信号中的噪声;接着,利用幅度-斜率双阈值法检测出MUAP波形;然后,采用分类功能强的模糊K均值聚类技术对波形进行聚类,再利用最近邻法将未分配波形分类;最后,采用基于伪相关相似性度量的剥落法,进行叠加电位波形分解,实现肌电信号的完全分解,获取完整的MUAP波形和发放模式。利用对来自正常人的真实EMG信号和模拟EMG信号进行实验,系统平均正确率可达87%以上。

Complete electromyography decomposition algorithm based on fuzzy k-means clustering technique

ElectroMyoGraphy (EMG) signal decomposition is the inverse process of the generation of EMG signals. The complete EMG decomposition was completed based on the superposition waveforms resolution in order to obtain information about Motor Unit (MU) template waveform and firing pattern. Firstly, noise was removed from the original EMG signals based on wavelet filtering and wavelet threshold estimation; then all the Motor Unit Action Potential (MUAP) waveforms were detected using amplitude-slope double threshold filtering, and all the detected MUAPs were classified into their constituent Motor Unit Action Potential Trains (MUAPT) through fuzzy K-means clustering and minimum distance classifier. Finally the superposition waveforms resolution procedure was finished using pseudo-correlation technique and peeling-off technique. This decomposition system has been evaluated using synthetic and real EMG signals. The average accuracy of the EMG decomposition system was above 87%.