Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
BACKGROUND Depression is a major psychiatric disorder threatening people’s health. Presently, existing medical guidelines‘ diagnostic criteria are largely qualitative and potentially contribute to over-diagnosis and misdiagnosis. OBJECTIVE This study aims to present a method of depression recognition based on eye movement for clinical use. METHODS 120 participants performed smooth pursuit eye movement (SPEM) and visual fixation (VF) tasks while their eye movements were recorded using iViewX RED 500. Data from 106 participants (53 in depression group VS 53 control group) passed quality control and were included and extracted by the Matlab program platform with machine learning analysis. RESULTS Depressed subjects showed significantly different eye movement from controls. In SPEM task, depressed patients had significantly greater RMSE (P<0.001), higher blink frequency (P<0.001), and greater gain value (P<0.001), but significant lower saccade frequency (P<0.001) than control group. In VF task, depressed patients had significantly greater RMSE (P<0.01), higher blink frequency (P<0.05), and greater saccade amplitude (P<0.001) than those in control group, but had significant lower fixation frequency (P<0.001) and shorter fixation time (P<0.001). Accuracy of identifing depression reached 88.68% (sensitivity 92.31% , specificity 85.19%). CONCLUSIONS Characterized eye-tracking patterns have been identified in depressed subjects via SPEM and VF tasks, as compared to healthy subjects. Recognition accuracy of depression by machine learning reached 88.68%. Future studies should validate these results in larger samples and in clinical populations. CLINICALTRIAL the Ethics Committee of Shandong University of Traditional Chinese Medicine.
BACKGROUND Depression is a major psychiatric disorder threatening people’s health. Presently, existing medical guidelines‘ diagnostic criteria are largely qualitative and potentially contribute to over-diagnosis and misdiagnosis. OBJECTIVE This study aims to present a method of depression recognition based on eye movement for clinical use. METHODS 120 participants performed smooth pursuit eye movement (SPEM) and visual fixation (VF) tasks while their eye movements were recorded using iViewX RED 500. Data from 106 participants (53 in depression group VS 53 control group) passed quality control and were included and extracted by the Matlab program platform with machine learning analysis. RESULTS Depressed subjects showed significantly different eye movement from controls. In SPEM task, depressed patients had significantly greater RMSE (P<0.001), higher blink frequency (P<0.001), and greater gain value (P<0.001), but significant lower saccade frequency (P<0.001) than control group. In VF task, depressed patients had significantly greater RMSE (P<0.01), higher blink frequency (P<0.05), and greater saccade amplitude (P<0.001) than those in control group, but had significant lower fixation frequency (P<0.001) and shorter fixation time (P<0.001). Accuracy of identifing depression reached 88.68% (sensitivity 92.31% , specificity 85.19%). CONCLUSIONS Characterized eye-tracking patterns have been identified in depressed subjects via SPEM and VF tasks, as compared to healthy subjects. Recognition accuracy of depression by machine learning reached 88.68%. Future studies should validate these results in larger samples and in clinical populations. CLINICALTRIAL the Ethics Committee of Shandong University of Traditional Chinese Medicine.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.