Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey.
J Med Syst. 2010 Jun;34(3):321-9. doi: 10.1007/s10916-008-9244-7.
This work investigates the performance of neuro-fuzzy system for analyzing and classifying EMG signals recorded from normal, neuropathy, and myopathy subjects. EMG signals were obtained from 177 subjects, 60 of them had suffered from neuropathy disorder, 60 of them had suffered from myopathy disorder, and rest of them had been normal. Coefficients that were obtained from the EMG signals using Autoregressive (AR) analysis was applied to neuro-fuzzy system. The classification performance of the feature sets was investigated for three classes.
本研究旨在探讨神经模糊系统在分析和分类正常、神经病变和肌病患者肌电图（EMG）信号方面的性能。研究共采集了 177 名受试者的 EMG 信号，其中 60 名患有神经病变，60 名患有肌病，其余为正常受试者。使用自回归（AR）分析从 EMG 信号中获取系数，并将其应用于神经模糊系统。针对三类患者，对特征集的分类性能进行了研究。
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