Analysis and Classification of Muscular Paralysis Disease using Electromyography Signal with Machine Learning

Abstract

Electrical activity of the muscles is characterized by Electromyography (EMG) signals. The EMG signal analysis form the basis for the diagnosis of muscular paralysis. The EMG signals from amyotrophic lateral sclerosis (ALS), and Myopathy are considered to analyze the paralysis. The statistical analysis of EMG signals from ALS, Myopathy, and Normal conditions, aid in the analysis and classification of paralysis. This work intends to analyze and classify the paralysis and normal conditions using EMG features extracted in time and frequency domains. Twelve statistical features are extracted from the EMG signals considered. Machine Learning techniques and deep learning techniques (DLT) are employed to perform the classification. multi-layer perceptron (MLP), support vector machine (SVM), random forest (RF), gradient boosting (GB), and nearest neighbor (NN) classifier models are used for the classification. The accuracy of the classifiers is calculated. The accuracy values obtained are 72% for MLP, 73% for SVM, 72% for RF, 71% for GB, and 69% for NN. The performance accuracy is better in SVM compared to other classifiers.