%P 223-228 %T Electromyography (EMG) based Classification of Neuromuscular Disorders using Multi-Layer Perceptron %A I. Elamvazuthi %A N.H.X. Duy %A Z. Ali %A S.W. Su %A M.K.A.A. Khan %A S. Parasuraman %I Elsevier B.V. %V 76 %D 2015 %R 10.1016/j.procs.2015.12.346 %O cited By 52; Conference of IEEE International Symposium on Robotics and Intelligent Sensors, IEEE IRIS 2015 ; Conference Date: 18 October 2015 Through 20 October 2015; Conference Code:123218 %L scholars6136 %J Procedia Computer Science %X Electromyography (EMG) signals are the measure of activity in the muscles. The aim of this study is to identify the neuromuscular disease based on EMG signals by means of classification. The neuromuscular diseases that have been identified are myopathy and neuropathy. The classification was carried out using Artificial Neural Network (ANN). There are five feature extraction techniques that were used to extract the signals such as Autoregressive (AR), Root Mean Square (RMS), Zero Crossing (ZC), Waveform length (WL) and Mean Absolute Value (MAV). A comparative analysis of these different techniques were carried out based on the results. The Multilayer Perceptron (MLP) was used for carrying out the classification. %K Classification (of information); Electromyography; Extraction; Feature extraction; Intelligent control; Multilayers; Neural networks; Neuromuscular rehabilitation; Neurophysiology; Robotics; Signal processing; Smart sensors, Absolute values; Autoregressive methods; Multi layer perceptron; Neuromuscular disease; Root Mean Square; Zero-crossings, Biomedical signal processing