Classification of EMG signals using neuro-fuzzy system and diagnosis of neuromuscular diseases.
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.
Koçer
Sabri
S
Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey. skocer@gazi.edu.tr
D004576:Electromyography / Q000592:standards*; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer; D009468:Neuromuscular Diseases / Q000145:classification / Q000175:diagnosis*; D012815:Signal Processing, Computer-Assisted