Use of Support Vector Machines and Neural Network in Diagnosis of Neuromuscular Disorders

Authors:
Nihal Fatma Güler;Sabri Koçer
Affiliations:
Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey;Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey
Venue:
Journal of Medical Systems
Year:
2005

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Abstract

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.