基于自适应滤波器的表面肌电信号消噪方法研究

Study on Surface EMG Signal Noise Canceling Based on Adaptive Filter

目的:应用自适应滤波器消除表面肌电信号中混有的50Hz工频干扰和心电信号干扰。方法:在没有信号特征先验知识的情况下,自适应滤波器能够得到比经典滤波器更好的滤波性能。当输入信号的统计特征未知,或者输入信号的统计特征变化时,自适应滤波器能够根据某种准则的要求自动地调节自身的滤波器参数,从而实现最优滤波。使用Biopac system MP150多导生理记录仪采集人体肱二头肌处表面肌电信号(采样频率为1000Hz)。采用一种新的变步长(LMS)自适应滤波器算法,分别设计自适应陷波器和自适应信号分离器。在MATLAB7.0环境下,编程实现自适应陷波器和自适应信号分离器算法,对采集到的表面肌电信号进行滤波处理。结果:实验表明,变步长自适应陷波器能消除表面肌电信号中的50Hz工频干扰;变步长自适应信号分离器能够将混叠在表面肌电信号中的心电信号分离出来。结论:自适应滤波器能够有效地消除表面肌电信号中混有的50Hz工频干扰和心电信号干扰,得到滤波效果较好的表面肌电信号,为表面肌电信号的进一步分析、处理和评估打下基础。

Objective： To cancel the 50 Hz power-line interference and the electrocardio signal interference in sEMG by using Adaptive Filter. Methods： On the condition without a priori information of signal, to use an Adaptive Filter is more efficient than to use a classic filter. When the statistical characteristics of the input signal are not known completely or change, Adaptive Filter can self-adjust the filter parameters to make it possible for the filter to perform satisfactorily. A set of sEMG signal of biceps muscle of arm was acquired by Biopac system MP 150（The sampling fi-equency is 1000 Hz）. In this paper, a new least-mean-square adaptive filter algorithm is taken for designing adaptive wave-trap and adaptive signal dividing filter respectively. Using MATLAB7.0, we realize the algorithm of adaptive wave-trap and adaptive signal dividing filter to cancel corresponding noise. Results： The experimental results show that LMS adaptive filter is convenient for eliminating 50 Hz power-line interference in sEMG and separating the electrocardio signal interference from sEMG. Conclusion： Adaptive filter can cancel the 50 Hz power-line interference and the electrocardio signal interference in sEMG effectively and we can acquire better filtered sEMG. The result may lay a foundation for further analysis, processing and evaluation of sEMG.