  Amyotrophic Lateral Sclerosis (ALS) and Myopathy are debilitating
neuromuscular disorders that demand accurate and efficient diagnostic
approaches. In this study, we harness the power of deep learning techniques to
detect ALS and Myopathy. Convolutional Neural Networks (CNNs) have emerged as
powerful tools in this context. We present ResEMGNet, designed to identify ALS
and Myopathy directly from raw electromyography (EMG) signals. Unlike
traditional methods that require intricate handcrafted feature extraction,
ResEMGNet takes raw EMG data as input, reducing computational complexity and
enhancing practicality. Our approach was rigorously evaluated using various
metrics in comparison to existing methods. ResEMGNet exhibited exceptional
subject-independent performance, achieving an impressive overall three-class
accuracy of 94.43\%.
