Auxiliary diagnostic method of Parkinson's disease based on eye movement analysis in a virtual reality environment.

Autor: Jiang M; School of Information and Communication Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China., Liu Y; Department of Neurology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116024, China., Cao Y; School of Information and Communication Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China., Liu Y; Department of Neurology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116024, China., Wang J; Department of Neurology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116024, China., Li P; Department of Neurology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116024, China., Xia S; School of Information and Communication Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China., Lin Y; Department of Neurology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116024, China. Electronic address: lin19671024@163.com., Liu W; School of Information and Communication Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China. Electronic address: liuwl@dlut.edu.cn.
Jazyk: angličtina
Zdroj: Neuroscience letters [Neurosci Lett] 2024 Nov 01; Vol. 842, pp. 137956. Date of Electronic Publication: 2024 Sep 02.
DOI: 10.1016/j.neulet.2024.137956
Abstrakt: Eye movement dysfunction is one of the non-motor symptoms of Parkinson's disease (PD). An accurate analysis method for eye movement is an effective way to gain a deeper understanding of the nervous system function of PD patients. However, currently, there are only a few assistive methods available to help physicians conveniently and consistently assess patients suspected of having PD. To solve this problem, we proposed a novel visual behavioral analysis method using eye tracking to evaluate eye movement dysfunction in PD patients automatically. This method first provided a physician task simulation to induce PD-related eye movements in Virtual Reality (VR). Subsequently, we extracted eye movement features from recorded eye videos and applied a machine learning algorithm to establish a PD diagnostic model. Then, we collected eye movement data from 66 participants (including 22 healthy controls and 44 PD patients) in a VR environment for training and testing during visual tasks. Finally, on this relatively small dataset, the results reveal that the Support Vector Machine (SVM) algorithm has better classification potential.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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