Classification of myopathy and neuropathy EMG signals using neural network

Abstract

Electromyography plays a key role in biomedical engineering to analyze the various neuromuscular disease. It is necessary to identify the complex signals from EMG and identify the myopathy and neuropathy signals. In this paper three samples of signals are taken from healthy person, patient with myopathy and patient with neuropathy. These signals are preprocessed suitable for applying to the MATLAB. The signals are further analyzed using neural network tool box. The back propagation algorithm is used here for training the network and the weight matrix is used for further classifying the signals. This paper also how much deviation is from the accurate diagnosis.