Explainable machine learning for movement disorders - Classification of tremor and myoclonus
The excellent classification results provide a proof of concept for the discrimination of ET and CM by applying a GMLVQ machine learning analysis to power spectra derived from accelerometry recordings. Such a system could support clinicians to improve the diagnostic accuracy.
Authors
- Inge Tuitert (ORCID: https://orcid.org/0000-0001-6338-3131)
- Jelle R. Dalenberg (ORCID: https://orcid.org/0000-0001-8580-5358)
- Jan Willem J. Elting (ORCID: https://orcid.org/0000-0003-3991-7097)
- Marina A.J. Tijssen (ORCID: https://orcid.org/0000-0001-5783-571X)
- A.M. Madelein van der Stouwe (ORCID: https://orcid.org/0000-0001-9497-2019)
- Ramesh S. Marapin (ORCID: https://orcid.org/0000-0002-6344-7323)
- Tatiana Svoreňová
- Marrit R. Klamer
- Elina L. van den Brandhof (ORCID: https://orcid.org/0000-0002-3881-585X)
- Michael Biehl (ORCID: https://orcid.org/0000-0001-5148-4568)
Institutions
- University Medical Center Groningen (NL)
- University of Groningen (NL)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2025-05-08
- DOI
- https://doi.org/10.1016/j.compbiomed.2025.110180
- Citations
- 6
- Primary Topic
- Neurological disorders and treatments
- Type
- article
- Field-Weighted Citation Impact
- 8.34