Multi-Scale and Multi-Level Feature Assessment Framework for Classification of Parkinson's Disease State From Short-Term Motor Tasks
The explanatory analysis of digital features in this study provides more prior knowledge for PD self-assessment in a free-living environment.
Authors
- Po Yang (ORCID: https://orcid.org/0000-0002-8553-7127)
- Fengtao Nan (ORCID: https://orcid.org/0000-0003-3415-3593)
- Xiyang Peng (ORCID: https://orcid.org/0000-0001-8015-3688)
- Ziheng Li (ORCID: https://orcid.org/0009-0001-7050-6870)
- Yun Yang (ORCID: https://orcid.org/0000-0002-9893-3436)
- Xulong Wang (ORCID: https://orcid.org/0000-0002-7385-4926)
- Yuting Zhao (ORCID: https://orcid.org/0009-0004-8644-8937)
- Zhong Zhao (ORCID: https://orcid.org/0009-0009-7298-7469)
Institutions
- Yunnan University (CN)
- First People's Hospital of Yunnan Province (CN)
- University of Sheffield (GB)
Publication Details
- Journal
- IEEE Transactions on Biomedical Engineering
- Published
- 2025-02-18
- DOI
- https://doi.org/10.1109/tbme.2024.3418688
- Citations
- 9
- Primary Topic
- Parkinson's Disease Mechanisms and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 12.50
Funders
- National Natural Science Foundation of China