Open AccessEMG Signal Classification for Detecting Neuromuscular Disorders
Author(s) -
Tanvir Ahmed,
Md. Kafiul Islam
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1921/1/012043
Subject(s) - electromyography , biceps , physical medicine and rehabilitation , computer science , artificial neural network , speech recognition , medicine , artificial intelligence
Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles to see muscle condition. Nervous system always controls the muscle activities such as relaxation or contraction. An efficient analysis of electromyography (EMG) signals plays an inevitable role in the diagnosis of neuromuscular disorders, prosthesis, and several related applications. Our aim in this study is to differentiate neuromuscular disorder patients from healthy people based on EMG signals. The EMG signals used in this research were recorded from biceps. Artificial Neural Network (ANN) was used for the classification. Eleven features were extracted from the EMG signals for the classification purpose. A comparative analysis was done based on the results. The outcome of this study encourages possible extension of this approach to improve stronger, more resilient and effective implementations.