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Amyotrophic Lateral Sclerosis (ALS) and Myopathy are debilitatingneuromuscular disorders that demand accurate and efficient diagnosticapproaches. In this study, we harness the power of deep learning techniques todetect ALS and Myopathy. Convolutional Neural Networks (CNNs) have emerged aspowerful tools in this context. We present ResEMGNet, designed to identify ALSand Myopathy directly from raw electromyography (EMG) signals. Unliketraditional methods that require intricate handcrafted feature extraction,ResEMGNet takes raw EMG data as input, reducing computational complexity andenhancing practicality. Our approach was rigorously evaluated using variousmetrics in comparison to existing methods. ResEMGNet exhibited exceptionalsubject-independent performance, achieving an impressive overall three-classaccuracy of 94.43\%.

ResEMGNet: A Lightweight Residual Deep Learning Architecture for Neuromuscular Disorder Detection from Raw EMG Signals