Electromyography Signal Classification With Artificial Intelligence for Detection of Neuromuscular Disorders Using a Large Clinically‐Acquired Database
AI can assist in EMG analysis, but the performance gap between curated and clinically-acquired datasets underscores the need for robust models capable of handling signal variability and complexity in authentic clinical contexts. Future efforts should focus on clinically-oriented AI development to improve translational applicability.
Authors
- John A. Morren (ORCID: https://orcid.org/0000-0002-9738-9547)
- Abdülhamit Subaşı
- Xiaofeng Wang (ORCID: https://orcid.org/0000-0001-8212-6931)
- Shuaiqi Huang (ORCID: https://orcid.org/0000-0001-5161-246X)
- Mohamed Taha (ORCID: https://orcid.org/0009-0006-8629-5066)
Institutions
- Cleveland Clinic (US)
- Harvard University (US)
- University at Albany, State University of New York (US)
- Massachusetts General Hospital (US)
- The Neurological Institute (US)
Publication Details
- Journal
- Muscle & Nerve
- Published
- 2025-11-28
- DOI
- https://doi.org/10.1002/mus.70087
- Citations
- 1
- Primary Topic
- Muscle activation and electromyography studies
- Type
- article
- Field-Weighted Citation Impact
- 0.67
Funders
- Cleveland Clinic