Sway frequencies may predict postural instability in Parkinson’s disease: a novel convolutional neural network approach
Heeding our small and heterogeneous sample, our findings suggest a CNN classifier based on cost-effective and conveniently obtainable posturographic data to be a promising approach to detect postural impairments in early to mid-stage PD and to gain novel insight into the subtle characteristics of impairments at this stage of the disease.
Authors
- David Engel (ORCID: https://orcid.org/0000-0002-9941-8102)
- R. Stefan Greulich (ORCID: https://orcid.org/0000-0001-5389-427X)
- Justus Student
- Josefine Waldthaler (ORCID: https://orcid.org/0000-0002-6505-9922)
- Kaleb Vinehout
- Frank Bremmer (ORCID: https://orcid.org/0000-0003-1597-7407)
- Lars Timmermann (ORCID: https://orcid.org/0000-0002-5301-3580)
- Alberto Parola (ORCID: https://orcid.org/0000-0003-0639-7777)
Institutions
- Philipps University of Marburg (DE)
- Oregon Health & Science University (US)
- Universitätsklinikum Gießen und Marburg (DE)
- Cold Spring Harbor Laboratory (US)
- Technische Universität Dresden (DE)
Publication Details
- Journal
- Journal of NeuroEngineering and Rehabilitation
- Published
- 2025-02-18
- DOI
- https://doi.org/10.1186/s12984-025-01570-7
- Citations
- 7
- Primary Topic
- Balance, Gait, and Falls Prevention
- Type
- article
- Field-Weighted Citation Impact
- 33.32
Funders
- Philipps-Universität Marburg
- European Commission
- Deutsche Forschungsgemeinschaft
- Hessisches Ministerium für Wissenschaft und Kunst
- Horizon 2020 Framework Programme
- H2020 Marie Skłodowska-Curie Actions