基于EEMD和二代小波变换的表面肌电信号消噪方法  

De-Noising Method of the sEMG Based On EEMD and Second Generation Wavelet Transform

作　　者：席旭刚[1] 朱海港[1] 罗志增[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》2012年第11期1488-1493,共6页Chinese Journal of Sensors and Actuators

基　　金：国家自然科学基金项目(60903084;61172134;61201300);浙江省自然科学基金项目(Y1111189;LY12F03006);浙江省科技计划项目(2010C33075)

摘　　要：为了更好地消除混杂在表面肌电信号(sEMG)中的噪声,提出了一种基于总体平均经验模式分解(EEMD)和二代小波变换的sEMG消噪新方法。首先对信号加入白噪声处理后进行经验模态分解(EMD),然后对高频的内蕴模式函数(IMF)分量进行二代小波阈值消噪处理,最后把处理后的高频IMF分量与低频IMF分量进行叠加,重构后的信号即为去噪信号。实验结果表明,该方法融合了二代小波与EEMD的优点,能更好的消除噪声,最大限度的保留有用信号,并具有更高的信噪比。In order to eliminate the noise mixed in surface electromyography （sEMG）, the paper presents a new sEMG de-noising method based on ensemble empirical mode decomposition（EEMD） and second generation wavelet transform. Firstly, the white noise-added sEMG signals are decomposed by the empirical mode decomposition （EMD）. Secondly, the high-frequency Intrinsic Mode Function （IMF）components are denoised by the second generation wavelet threshold method. Finally, the high frequency IMF components processed and low frequency IMF components are reconstructed to get the denoised signal. The experimental results show that the method combines the advantages of second generation wavelet and EEMD, which can better eliminate noise, retain the useful signal as much as possible, and has a higher signal-to-noise ratio.

关 键 词：表面肌电信号 消噪 总体平均经验模式分解 二代小波 

分 类 号：TP212.3[自动化与计算机技术—检测技术与自动化装置]