Journal
NEUROCOMPUTING
Volume 315, Issue -, Pages 1-8Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2018.03.032
Keywords
Parkinson's disease; diagnosis; Gait; temporal data; LSTM; CNN
Categories
Funding
- National Natural Science Foundation of China (NSFC) [61501417, 61271405]
- International Science & Technology Cooperation Program of China (ISTCP) [2014DFA10410]
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When diagnosing Parkinson's disease (PD), medical specialists normally assess several clinical manifestations of the PD patient and rate a severity level according to established criteria. This rating process is highly depended by doctors' expertise, which is subjective and inefficient. In this paper, we propose a machine learning based method to automatically rate the PD severity from gait information, in particular, the sequential data of Vertical Ground Reaction Force (VGRF) recorded by foot sensors. We developed a two-channel model that combines Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) to learn the spatio-temporal patterns behind the gait data. The model was trained and tested on three public VGRF datasets. Our proposed method outperforms existing ones in terms of prediction accuracy of PD severity levels. We believe the quantitative evaluation provided by our method will benefit clinical diagnosis of Parkinson's disease. (c) 2018 Published by Elsevier B.V.
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