title: Classification of EMG signals using PCA and FFT.
abstract: 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.
authors.affiliation: Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, Teknikokullar, Ankara, Turkey. fnguler@gazi.edu.tr
authors.forename: Nihal Fatma
authors.initials: NF
authors.lastname: Güler
authors.forename: Sabri
authors.initials: S
authors.lastname: Koçer
mesh_terms: D000293:Adolescent; D000328:Adult; D002648:Child; D002675:Child, Preschool; D004576:Electromyography / Q000379:methods; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D009468:Neuromuscular Diseases / Q000175:diagnosis* / Q000503:physiopathology; D025341:Principal Component Analysis; D012372:ROC Curve; D014459:Ulnar Nerve / Q000503:physiopathology*