EMG signals classification for neuromuscular diseases detectionusing deep learning

Abstract

Neuromuscular diseases are particular impairments that affect the muscle tissue or nervous system part connected to muscles. Electromyography (EMG) signals are valuable biosignals for the diagnosis of neuromuscular diseases. However, the classification of EMG signals is a challenging task due to the complexity of the signals and the variability of the diseases. In this project, we address the problem of EMG signals classification for the detection of neuromuscular diseases using deep learning techniques. The main goal of our project is to develop a robust deep-learning model that performs well on unseen data, thereby improving the reliability of diagnosis in real-life scenarios. To achieve this, we design a model which we train and evaluate on a dataset of EMG signals from patients with different neuromuscular diseases. We assess the performance of our designed model using two different methods : the train-test split approach, commonly employed in the existing literature, and the subject-independent evaluation method, which ensures that the model is tested on completely unseen data. The results show that the model achieves excellent performance on the train-test split approach. However, the second method produces varied and uneven scores for different patients, suggesting that EMG data of certain individuals may be more challenging to classify accurately. Nonetheless, some patients exhibit highly accurate classifications, demonstrating the potential performance of our designed model. The obtained results indicate the potential of the developed tool for the diagnosis of neuromuscular diseases.