Peng, X., Zhao, Y., Li, Z. et al. (5 more authors) (2025) Multi-scale and multi-level feature assessment framework for classification of Parkinson’s disease state from short-term motor tasks. IEEE Transactions on Biomedical Engineering. ISSN 0018-9294
Abstract
Objective: Recent quantification research on Parkinson's disease (PD) integrates wearable technology with machine learning methods, indicating a strong potential for practical applications. However, the effectiveness of these techniques is influenced by environmental settings and is hardly applied in real-world situations. This paper aims to propose an effective feature assessment framework to automatically rate the severity of PD motor symptoms from short-term motor tasks, and then classify different PD severity levels in the real world. Methods: This paper identified specific PD motor symptoms using a novel feature-assessment framework at both segment-level and sample-level. Features were selected after calculating SHapley Additive exPlanation(SHAP) value, and verified by different machine learning methods with appropriate parameters. This framework has been verified on real-world data from 100 PD patients performing Unified Parkinson's Disease Rating Scale(UPDRS)-recommended short motor tasks, each task lasting 20-50 seconds. Results: The sensitivity for recognizing motor fluctuations reached 88% in tremor recognition. Additionally, LightGBM achieved the highest accuracy for early detection(92.59%) and achieved 71.58% in fine-grained severity classification using 31 selected features. Conclusion: This paper reports the first effort to assess multi-level and multi-scale features for automatic quantification of motor symptoms and PD severity levels. The proposed framework has been proven effective in assessing key PD information for recognition during short-term tasks. Significance: The explanatory analysis of digital features in this study provides more prior knowledge for PD self-assessment in a free-living environment.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Author(s). Except as otherwise noted, this author-accepted version of a journal article published in IEEE Transactions on Biomedical Engineering is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | decomposition; disease recognition; feature fusion; healthcare wearables system; machine learning; multi-dimensional; parkinson’s disease |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number EPSRC/Industrial 165332 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 20 Jun 2024 15:37 |
Last Modified: | 03 Mar 2025 15:35 |
Status: | Published online |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TBME.2024.3418688 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:213422 |