Identification of Neuromuscular Disorder by Classifying EMG Signal usingneural network

Since past few years, researchers have been concentrating on the classification of Electromyography Signal. This method is very useful in diagnosing the neuro-muscular disorders, which consists of wide spread diseases affecting peripheral nervous system. Progressive muscle weakness is the major form of these disorders. Out of various proposed methods, scholars are commonly focusing on Neural Network for its accuracy. And the basic variant feature, Motor Unit Action Potential is selected for classification. Out of various available tools, this research uses Discrete Wavelet Transform as a tool for classification and for the training of N-Network, a multilayer feed forward neural network with back propagation algorithm is used. Keywords—Artificial neural network (ANN);Discrete wavelet transform (DWT); feed forward neural network (fNN); Motor unit Action Potential (MUAP); Amyotrophic Lateral Sclerosis (ALS); kNearest Neighbors (kNN);Electromyography (EMG);