Classification of EMG signals using PCA and FFT

被引：90

作者：

Güler N.F. ^{[1
]}

Koçer S. ^{[1
]}

机构：

[1] Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Teknikokullar

来源：

Journal of Medical Systems | 2005年 / 29卷 / 3期

关键词：

Backpropagation (BP); Fast Fourier transform (FFT); Multilayer perceptron (MLP); Principal components analysis (PCA); Support vector machine (SVM);

D O I：

10.1007/s10916-005-5184-7

中图分类号：

学科分类号：

摘要：

In this study, the fast Fourier transform (FFT) analysis was applied to EMG signals recorded from ulnar nerves of 59 patients to interpret data. The data of the patients were diagnosed by the neurologists as 19 patients were normal, 20 patients had neuropathy and 20 patients had myopathy. The amount of FFT coefficients had been reduced by using principal components analysis (PCA). This would facilitate calculation and storage of EMG data. PCA coefficients were applied to multilayer perceptron (MLP) and support vector machine (SVM) and both classified systems of performance values were computed. Consequently, the results show that SVM has high anticipation level in the diagnosis of neuromuscular disorders. It is proved that its test performance is high compared with MLP. © 2005 Springer Science+Business Media, Inc.

引用

页码：241 / 250

页数：9

共 22 条

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