Deep Learning Classification of Simulated Surface EMG Signals across Maximum Voluntary Contraction Levels

Abstract

Electromyography (EMG) is a fundamental tool in diagnosing neuromuscular disorders (NMD). Due to the complex nature of EMG signals, different approaches, based on artificial intelligence and machine learning, were developed for EMG signal analysis and NMD diagnosis. Considering the critical role of maximum voluntary contraction (MVC) as a fundamental metric in assessing muscle fatigue, in this work, classification of simulated surface EMG (sEMG) into MVC levels is performed. Unlike previous studies, which focus primarily on binary classification of fatigue and non-fatigue states, our approach employs a deep convolutional neural network for the classification of sEMG signals into ten MVC levels, where the model outputs categorical predictions, with each class representing a specific MVC level. sEMG signals were generated using a computer muscle model that we developed using MATLAB, which allows for greater control over variability, ensuring robustness and generalizability of the model. The obtained results demonstrate that the model achieved high performance in differentiating between the ten classes (MVC levels), with an accuracy, F1-score, recall, and precision of 88.88%, 88.75%, 88.80% and 88.86%, respectively. These findings reveal that the model can accurately differentiate across MVC levels, indicating a potential method for accurate assessment of muscle fatigue intensity.