Abstract—An accurate and computationally efficient means of feature extraction of electromyographic (EMG) signal patterns has been the subject of considerable research effort in recent years. Quantitative analysis of EMG signals provides an important source of information for the classification of neuromuscular disorders. The objective of this study is to discriminate between normal (NOR), myopathic (MYO) and neuropathic (NEURO) subjects. The experiment consisted of 22 pathogenic (11 MYO and 11 NEURO) and 12 healthy persons. The signals were recorded at 30% Maximum Voluntary Contraction (MVC) for 5 seconds. Features of MUAPs extracted in time have been quantitatively analysed. We have used binary SVM for classification. Separation of normal subjects from neuromuscular disease subjects has an accuracy of 83.45%, whereas separation of subjects from the two types of subjects (myopathic and neuropathic) has an accuracy of 68.29% which is again high.

Index Terms—electromyography, myopathic, neuropathic, RBFNN, SVM, SVM ensemble

Cite: Anjana Goen, "Classification of EMG Signals for Assessment of Neuromuscular Disorders," International Journal of Electronics and Electrical Engineering, Vol. 2, No. 3, pp. 242-248, September 2014. doi: 10.12720/ijeee.2.3.242-248