Time-Domain Statistical Analysis of EMG Signals for Muscular Dysfunction Detection

Electromyogram (EMG) signals provide a visual representation of the electrical activity of muscles and serve as a key tool for analyzing muscular activity in subjects with paralysis and neuromuscular diseases. This study focuses on the analysis of EMG signals in individuals with amyotrophic lateral sclerosis (ALS), myopathy, and healthy subjects. Twelve statistical features in the time domain are extracted from the EMG signals of these subjects, and the significance of these features is tested using an F-test. The results show that all twelve features are statistically significant (p < 0.05) in distinguishing between normal, ALS, and myopathy conditions. These findings suggest that time-domain statistical features can be effectively used to analyze paralysis conditions, potentially aiding in the development of better treatment options. Since biomedical signals are continuous by nature, graphical representations of these signals, such as time-amplitude plots, are essential for the analysis of time-series data. The analysis of EMG signals in the time domain reveals important information about the variations in amplitude over time, providing insights into muscular dysfunction in various paralysis conditions.