Automatic diagnosis of neuro-muscular diseases using neural network

An automatic diagnostic tool for neuromuscular diseases, based on the feature extraction and classification of myoelectric patterns using neural network is described. Electromyogram (EMG) signals are extracted from the patients during maximal contraction using needle electrodes. This EMG signal is digitized at a rate of 1000 samples/second. The myoelectric signal is divided into many time segments. Five time domain features are extracted from each of these segments and are averaged over the segments to obtain one feature set. This is applied to the neural network for classification. Results are presented for the diagnosis of polymyositis.