Classification of electromyograph for localised muscle fatigue using neural networks

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. The paper reports that by using specific wavelets for transform and at specific levels of decomposition, the features of the signal correlating with muscle status were highlighted and classification of this data using neural networks gave excellent results.