基于样本熵与经验模态分解的表面肌电信号降噪

De-noising method of sEMG based on sample entropy and EMD

表面肌电信号作为人机交互一种媒介获得了广泛的关注,为了有效去除表面肌电信号中的噪声,提出一种基于熵和经验模态分解的表面肌电信号去噪方法。该方法引入了样本熵的概念,根据经验模态分解获得的本征模态函数分量样本熵的特征,给出了确定表面肌电信号含噪本征模态分量的方法。该方法根据样本熵反映信号复杂程度的特性,来评价各本征模态分量的复杂性,进而自适应的确定主要的含噪本征模态分量,避免了凭借经验选择的不足。同时,该方法结合了一种改进的小波阈值函数,在去除表面肌电信号噪声的同时避免了过多有效信息的丢失。仿真实验和实测表面肌电实验都表明,该方法在去除噪声的同时能够保留表面肌电信号的原有特征,而且能够改善信号的信噪比。

Surface electromyography(s EMG)has caused wide public concern as a human-interactive media.To solve the problem of s EMG de-noising effectively,a novel s EMG de-noising method based on sample entropy and EMD is proposed.The method that introduces sample entropy concept proposes a certain way to determine the intrinsic mode function(IMF)with noise based on sample entropy characteristics of the IMF,and can adaptively determine the main noise components of IMF to avoid the shortcomings of experience.Furthermore,the method combines the advantage of improved wavelet threshold function,to maintain the useful signals while filtering noise.The experimental result with Simulation data and real data show that the method can keep the original features of s EMG when removing noises and improve the signal-noise ratio.