iEMG-Based Diagnosis of ALS and Myopathy using 1D-CNN

Amyotrophic Lateral Sclerosis (ALS) and myopathy are debilitating neuromuscular disorders that require accurate and timely diagnosis for effective management. Traditional electromyography (EMG)-based diagnostic methods rely on manual interpretation, which is time-consuming and prone to variability. This study proposes an approach that directly classifies EMG signals using a one-dimensional convolutional neural network (1D-CNN) without feature extraction, addressing the limitations of existing methods that depend on handcrafted features and focus primarily on binary classification. The proposed model is evaluated on a publicly available EMG dataset, achieving an overall accuracy of 99.27%, with macro and weighted precision, recall, and F1-scores exceeding 99% across ALS, myopathy, and healthy subjects. Unlike previous approaches that require extensive preprocessing, our method maintains high classification performance while reducing computational complexity, offering a clinically relevant multiclass classification framework. Although our method achieves high classification performance, it also maintains a strong balance between sensitivity and specificity, ensuring reliable and accurate neuromuscular disorder diagnosis, making it a practical tool for clinical applications. Future research will focus on improving model generalizability, expanding dataset diversity, and integrating real-time deployment for enhanced diagnostic utility.