The Evaluation of Telecommunication Signal Processing Techniques for Emg Disease Classification

Abstract

Electromyography (EMG) is a biological signal widely used in medical imaging. It is used by doctors for the classification and diagnosis of myopathic and neuropathic diseases. Many different techniques have been used to ease the diagnosis of these diseases like machine learning and support vector machines (SVM). In this work, various methods used in telecommunication systems for digital modulation identification have been used to extract features from EMG signals as potential features. The results show success in classifying between different types of EMG waveforms.