HHT方法在人体下肢表面肌电信号分析中的应用  

Application of the HHT Method to the SEMG of the Lower Limb

作　　者：徐文良[1] 孟明[1] 马玉良[1] 

机构地区：[1]杭州电子科技大学智能控制与机器人研究所,杭州310018

出　　处：《传感技术学报》2010年第3期297-302,共6页Chinese Journal of Sensors and Actuators

基　　金：国家自然科学基金资助(60705010);浙江省自然科学基金资助(Y1080854)

摘　　要：针对表面肌电信号是非线性非稳态信号的特点,为了得到信号的有效特征并对信号特征进行有效分析,采用HHT(Hilbert-Huang Transformation)分析方法,通过经验模态分解将信号分解为一组内蕴模态函数。基于各内蕴模态函数的频率特征的分析,对它们进行HHT变换建立表面肌电信号的时间-频率-能量三维Hilbert谱,进而得到信号的边际谱。文中给出基于经验模态分解阈值消噪方法,和小波阈值方法相比,其消噪效果明显,在抑制噪声的同时,能够较好保留信号边缘和细节信息。初步实验表明HHT方法为表面肌电信号的特征提取和模式识别提供了可靠的依据,具有很好的应用前景。Surface electromyography （SEMG） was a nonlinear and nonstationary signal. In order to get effective features of the SEMG and analyze features, HHT method was applied. The signal could be decomposed by using the empirical mode decomposition （EMD） method into a series of intrinsic mode function （IMF） components, at the same time, the frequency of each IMF was analyzed. these IMFs were transformed into 3-D Hilbert spectra which exhibited the time-frequency-amplitude, and then the marginal spectra were obtained by integrating the Hilbert spectra with respect to time. A wavelet threshold filtering method based on EMD was proposed, this method has good effect on suppressing noise, comparing with wavelet threshold filter and has advantages of retaining edge and detail information. Experimental results show that HHT method provides reliable basis for the feature extraction and pattern recognition of SEMG of the lower limb and has a good prospect of application.

关 键 词：HILBERT-HUANG变换 表面肌电信号 时频谱 阈值消噪 

分 类 号：TP212.3[自动化与计算机技术—检测技术与自动化装置] TP274[自动化与计算机技术—控制科学与工程]