Use of support vector machines and neural network in diagnosis of neuromuscular disorders.
In this study the performance of support vector machine (SVM)and back-propagation neural network were applied to analyze the classification of the electromyogram (EMG) signals obtained from normal, neuropathy and myopathy subjects. By using autoregressive (AR) modeling, AR coefficients were obtained from EMG signals. Moreover, the support vector machine and artificial neural network (ANN) were used as base classifiers. The AR coefficients were benefited as inputs for SVM and ANN. Besides, these coefficients were tested both in ANN and SVM. 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 ANN.
Güler
Nihal Fatma
NF
Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, Teknikokullar, Ankara, Turkey. fnguler@gazi.edu.tr
Koçer
Sabri
S
D000293:Adolescent; D000328:Adult; D002648:Child; D002675:Child, Preschool; D004576:Electromyography; D006801:Humans; D007223:Infant; D008875:Middle Aged; D016571:Neural Networks, Computer; D009468:Neuromuscular Diseases / Q000175:diagnosis*; D012372:ROC Curve