Salvador Jillian, de Bruin Hubert
Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada.
Conf Proc IEEE Eng Med Biol Soc. 2006;2006:2304-7. doi: 10.1109/IEMBS.2006.259534.
A system was previously designed to obtain estimates of the number of motor units (MUNE) in a superficial muscle and hence number of functioning motor neurons to that muscle. This method uses incremental stimulation of a motor nerve and subsequent recognition and classification of the elicited M-waves. In this earlier work we used the Fourier power coefficients as pattern classifiers. The presented work compares the Fourier transform classifier results with those obtained using a wavelet transform classifier. Data to test the two approaches were obtained from the thenar muscles of ten normal subjects. The results show that the wavelet transform is superior to the Fourier in classifying M-waves with significantly improved inter and intra-class variances.
先前设计了一种系统，用于估计浅表肌肉中的运动单位数量（MUNE），从而估计该肌肉中功能正常的运动神经元数量。该方法使用对运动神经的递增刺激以及对诱发的M波进行后续识别和分类。在早期的这项工作中，我们使用傅里叶功率系数作为模式分类器。目前的工作将傅里叶变换分类器的结果与使用小波变换分类器获得的结果进行了比较。用于测试这两种方法的数据来自十名正常受试者的鱼际肌。结果表明，在对M波进行分类时，小波变换优于傅里叶变换，类间和类内方差都有显著改善。
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