EMG Diagnosis using Neural Network Classifier with Time Domain and AR Features

Abstract The shapes of motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide an important source of information for the diagnosis of neuromuscular disorders. To extract this information from the EMG signals, the first step is identification of the MUAPs composed by the EMG signal, second step is clustering of MUAPs with similar shapes, third step is extraction of the features of MUAP clusters and last step is classification of MUAPs. In this work, the MUAPs are identified by using a data driven segmentation algorithm, statistical pattern recognition technique is used for clustering of MUAPs. Followed by the extraction of time domain and autoregressive (AR) features of the MUAP clusters. Finally, a neural network (NN) classifier is used for classification of MUAPs. A total of 12 EMG signals obtained from 3 normal (NOR), 5 myopathic (MYO) and 4 motor neuron diseased (MND) subjects were analyzed. The success rate for the segmentation technique is 95.90% and for the statistical technique is 93.13%. The classification accuracy of NN is 66.72 % with time domain parameters and 75.06 % with AR parameters. Index Terms Electromyography, motor unit actio