期刊
IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY
卷 1, 期 -, 页码 65-73出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/OJEMB.2020.2966295
关键词
Classification; Machine Learning; Digital Gait; Parkinson's disease; Partial least square-discriminant analysis (PLS-DA)
资金
- Keep Control project, European Union Horizon 2020 research and innovation ITN program [721577]
- Innovative Medicines Initiative 2 Joint Undertaking (JU) [820820]
- European Union's Horizon 2020 research and innovation programme
- EFPIA
- Parkinson's UK [J-0802, G-1301]
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.
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