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Accelerometry-Based Digital Gait Characteristics for Classification of Parkinson's Disease: What Counts?
- Source :
- IEEE Open Journal of Engineering in Medicine and Biology, Vol 1, Pp 65-73 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Objective: Gait may be a useful biomarker that can be objectively measured with wearable technology to classify Parkinson's disease (PD). This study aims to: (i) comprehensively quantify a battery of commonly utilized gait digital characteristics (spatiotemporal and signal-based), and (ii) identify the best discriminative characteristics for the optimal classification of PD. Methods: Six partial least square discriminant analysis (PLS-DA) models were trained on subsets of 210 characteristics measured in 142 subjects (81 people with PD, 61 controls (CL)). Results: Models accuracy ranged between 70.42-88.73% (AUC: 78.4-94.5%) with a sensitivity of 72.84-90.12% and a specificity of 60.3-86.89%. Signal-based digital gait characteristics independently gave 87.32% accuracy. The most influential characteristics in the classification models were related to root mean square values, power spectral density, step velocity and length, gait regularity and age. Conclusions: This study highlights the importance of signal-based gait characteristics in the development of tools to help classify PD in the early stages of the disease.
Details
- Language :
- English
- ISSN :
- 26441276
- Volume :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- IEEE Open Journal of Engineering in Medicine and Biology
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.f15ca456d29c4324b428f1adce9f77d5
- Document Type :
- article
- Full Text :
- https://doi.org/10.1109/OJEMB.2020.2966295