Temporal and Spectral Analysis of EMG for Classification of Muscular Paralysis

Abstract

Loss of muscle function is the condition referred to as paralysis. Parts of the body may be completely paralysed or only partially. The quality of life is enhanced by the early identification of paralysis. In people with paralysis and neuromuscular diseases, EMG signals can be used to analyse muscular activation. In this study, EMG signals are analysed by feature extraction and divided into two categories: normal and paralysed. The obtained findings demonstrate that the extracted features in the suggested work perform better for EMG signal categorization.  The conditions of Amyotrophic Lateral Sclerosis (ALS) and Myopathy are taken into consideration in this study to examine the paralysis state. Using time and frequency domain approaches, characteristics were retrieved from the EMG of healthy and paralysed participants. Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (XGBOOST), and K-Nearest Neighbour (KNN) Classifiers are the classifier models used in the study. With time domain EMG information, classifiers like MLP, SVM, RF, XGBOOST, and KNN are used. The frequency domain EMG characteristics are applied to the MLP, RF, XGBOOST, and KNN classifiers. With time domain EMG features, MLP achieved a classification accuracy of 76.5%, SVM with 77.2%, RF with 76.1%, XGBOOST with 77.1%, and KNN with 75.8%.  In comparison to classifier models employing time domain EMG information, the SVM classifier performs better. The classification accuracy for MLP, RF, XGBOOST, and KNN using frequency domain EMG features is 77.7%, 76.6%, and 75%, respectively. In comparison to other classifier models with frequency domain features, the MLP and RF classifiers perform better. Time and frequency domains of the EMG of Normal, ALS, and Myopathy diseases are investigated. It has been noted that the EMG signal and its characteristics differ significantly (p<0.05) between the Normal and Paralysis conditions. EMG is utilised in the current study to analyse and categorise paralysis, which helps with early diagnosis and improved treatment options.