Neuromuscular Diseases Diagnostic System Using Machine Learning and Deep Learning Techniques: A review
Year: 2025
Type: article
Abstract: This study reviews the application of machine learning and deep learning techniques to develop diagnostic system using electromyography (EMG) signals for neuromuscular diseases. The review utilizes these advanced computational methods improving early detection and treatment of neuromuscular disorders. The paper discusses various diagnostic process, including EMG signal acquisition, data pre-processing, feature extraction, and classification. Also highlights the use of machine learning algorithms such as support vector machines, random forests, and decision trees, as well as deep learning techniques including convolutional neural networks and recurrent neural networks for analysing EMG data to detect neuromuscular diseases. The potential of these techniques to enhance diagnostic accuracy, efficiency, and speed to identify neuromuscular disorders. The study the importance of integrating machine learning with clinical testing to develop more accurate and effective treatments for neuromuscular diseases. This study concludes that while utilization of machine learning and deep learning for detecting neuromuscular diseases diagnosis, further research is needed to address existing challenges and verify these systems in real world application.
Institution: Madhav Institute of Technology & Science
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4411310642