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Amyotrophic Lateral Sclerosis (ALS) and Myopathy present considerablechallenges in the realm of neuromuscular disorder diagnostics. In this study,we employ advanced deep-learning techniques to address the detection of ALS andMyopathy, two debilitating conditions. Our methodology begins with theextraction of informative features from raw electromyography (EMG) signals,leveraging the Log-spectrum, and Delta Log spectrum, which capture thefrequency contents, and spectral and temporal characteristics of the signals.Subsequently, we applied a deep-learning model, SpectroEMG-Net, combined withConvolutional Neural Networks (CNNs) and Attention for the classification ofthree classes. The robustness of our approach is rigorously evaluated,demonstrating its remarkable performance in distinguishing among the classes:Myopathy, Normal, and ALS, with an outstanding overall accuracy of 92\%. Thisstudy marks a contribution to addressing the diagnostic challenges posed byneuromuscular disorders through a data-driven, multi-class classificationapproach, providing valuable insights into the potential for early and accuratedetection.

EMG Signal Classification for Neuromuscular Disorders with Attention-Enhanced CNN