Neural networks and wavelet decomposition for classification of surface electromyography

To determine the status of a muscle, surface electromyography (SEMG) is a useful tool being non-invasive and easy to record. Clinicians are able to classify the signal visually but because of the large number of parameters of the signal, automatic classification becomes difficult. This paper reports our efforts at using wavelet transforms to process the signal before using neural networks for classification. We have found that by using specific wavelet functions and at specific levels of decomposition, the features of the signal correlating with muscle status were highlighted.