  Amyotrophic Lateral Sclerosis (ALS) and Myopathy present considerable
challenges in the realm of neuromuscular disorder diagnostics. In this study,
we employ advanced deep-learning techniques to address the detection of ALS and
Myopathy, two debilitating conditions. Our methodology begins with the
extraction of informative features from raw electromyography (EMG) signals,
leveraging the Log-spectrum, and Delta Log spectrum, which capture the
frequency contents, and spectral and temporal characteristics of the signals.
Subsequently, we applied a deep-learning model, SpectroEMG-Net, combined with
Convolutional Neural Networks (CNNs) and Attention for the classification of
three 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\%. This
study marks a contribution to addressing the diagnostic challenges posed by
neuromuscular disorders through a data-driven, multi-class classification
approach, providing valuable insights into the potential for early and accurate
detection.
