独立分量分析对多通道表面肌电信号运动系统的分离的应用：第一部分-测量技术

中文字数:8219,中文页数:15  英文字数:4991页数:21
摘  要
 本文主要研究的是ICA的应用是否对从多通道表面肌电信号中分离MUAPTs有所帮助。在本文中。8通道表面肌电信号是在胫骨前端肌肉做等角度背部一样的的弯曲期间自动收缩最大分别为5%，10%，15%和20%时记录下来的。记录MUAP通过肌肉纤维垂直的表面肌电信号通道而得到的在安装好的两个通道之间带有短时间延迟的波形。独立分量是通过快速ICA与表面肌电信号和由PCA计算得出的主要分量相比较而估算出来的，通过我们的结果可以看出，它表明快速ICA可以分离许多组简单的MUAP，当PCA不能有效地把许多组分离为重要的分量时，表面肌电信号的波形被分离为单个独立分量。更好的减少不同的MUAP波形之间的干扰已经通过快速ICA的使用被证明了。因此，它表明了快速ICA能为表面肌电信号的证明、i.e波形、间隔时间等提供比PCA和原始表面肌电信号更好的有MUAPTs作用的识别率.

关键词：运动神经；多通道；表面肌电信号；独立分量分析

Abstract
 The purpose of this study is to examine whether or not the application of independent component analysis (ICA) is useful for separation of motor unit action potential trains (MUAPTs) from the multi-channel surface EMG (sEMG) signals. In this study, the eight-channel sEMG signals were recorded from tibialis anterior muscles during isometric dorsi-flexions at 5%, 10%, 15% and 20% maximal voluntary contraction. Recording MUAP waveforms with little time delay mounted between the channels were obtained by vertical sEMG channel arrangements to muscle fibers. The independent components estimated by FastICA were compared with the sEMG signals and the principal components calculated by principal component analysis (PCA). From our results, it was shown that FastICA could separate groups of similar MUAP waveforms of the sEMG signals separated into each independent component while PCA could not sufficiently separate the groups into the principal components. A greater reduction of interferences between different MUAP waveforms was demonstrated by the use of FastICA. Therefore, it is suggested that FastICA could provide much better discrimination of the properties of MUAPTs for sEMG signal decomposition, i.e. waveforms, discharge intervals, etc., than not only PCA but also the original sEMG signals.
 
 Keywords:Motor unit;  Multi-channel;  Surface EMG signals;
          Independent component analysis

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