Classification of EMG signals is essential for diagnosis of motor neuron diseases like neuropathy and myopathy. Although a number of strategies have been implemented for classification, none of them are efficient enough to be implemented in clinical environment. In the present study, we use ensemble approach of support vector machines for classification of three classes (normal, myopathic and neuropathic) of clinical electromyogram (EMG). Our proposed approach uses time and time–frequency features extracted from EMG signals. By employing two types of feature set for same class discriminators, we are able to select the best feature set-discriminator pairs. The decision made by each selected classifier is used to generate the final class for an input EMG signal through majority voting. Our proposed method yields higher accuracy of 94.67% over 89.67% for multiclass SVM classifier.
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