基于多尺度主元分析的表面肌电信号模式分类  

Pattern Recognition of Surface Electromygram Based on Multi-scale Principal Component Analysis

作　　者：田喜英[1] 雷敏[1] 

机构地区：[1]上海交通大学机械系统与振动国家重点实验室振动冲击噪声研究所,上海200240

出　　处：《中国医疗器械杂志》2009年第4期243-246,共4页Chinese Journal of Medical Instrumentation

基　　金：上海市自然基金(06ZR14042);国家自然科学基金;高等学校学科创新引智计划(B06012)

摘　　要：用基于小波变换的多尺度主元分析提取表面肌电信号特征,然后用贝叶斯分类器进行模式分类。实验结果显示,当选用Harr小波和bior2.6小波对肌电信号进行5层小波分解时,该方法对前臂6种动作模式(内翻,外翻,握拳,展拳,上切和下切)的正确识别率可以达到99.44%。研究表明,该方法优于基于小波系数统计特征和主元分析降维相结合的特征提取方法,能成功识别出多种动作模式。Multi-scale principal component analysis based on wavelet transform was applied in feature extraction ot sEMG, and bayes classifier was used for pattern classification in this paper. The experiment showed that when Harr wavelet or bior2.6 wavelet was employed to decompose EMG at 5 levels, this method resulted in good performance in the pattern recognition of six movements including varus, ectropion, hand grasps, hand extension, upwards flexion and downwards flexion, with the accuracy of 99.44 %. It was superior to the feature extraction based on the statistic feature of wavelet coefficients combined with dimension-reduce by PCA. The research indicated that the proposed method can successfully identify many kinds of movements.

关 键 词：表面肌电信号 基于小波变换的多尺度主元分析 主元分析 模式分类 

分 类 号：TN911.6[电子电信—通信与信息系统]