Auxiliary diagnostic method of Parkinson's disease based on eye movement analysis in a virtual reality environment.
Neurosci Lett
; 842: 137956, 2024 Sep 02.
Article
en En
| MEDLINE
| ID: mdl-39233045
ABSTRACT
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Neurosci Lett
Año:
2024
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Irlanda