Support vector machine in computer aided clinical electromyography

Motor unit action potentials (MUAPs) recorded during routine electromyography (EMG) examination provides important information for the assessment of neuromuscular disorders. In this preliminary study, support vector machines (SVMs) based on multi-class classifier is activated for the identification of normal subjects and patients suffering from motor neuron diseases (MND) and myopathies (MVO). The results in experiments prove the classification validity of SVMs which guarantee high generalization ability on the testing samples. Furthermore, its performance is compared with a back-propagation (BP) neural network. More excellent recognition accuracy indicates the potential of the SVMs techniques in clinical neuromuscular disorders evaluation.