Machine Learning Models for EMG-Based Diagnosis of Neuromuscular Disorders

Neuromuscular disorders (NMDs), such as myopathies and neuropathies, affect the communication between nerves and muscles and often lead to serious functional impairments. This study investigates the impact of preprocessing methods and machine learning models on the automated classification of NMDs using electromyography (EMG) data. We compare Convolutional Neural Networks, Extreme Gradient Boosting, and Light Gradient Boosting Machine on two binary classification tasks: Healthy vs. Myopathy and Healthy vs. Neuropathy. The analysis is conducted on an open dataset of invasive needle EMG recordings, serving as a preliminary step toward the future use of non-invasive surface EMG in wearable diagnostic tools. The results highlight the importance of preprocessing and show promising performance across all models.