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. 2022 Jun 15:307:237-243.
doi: 10.1016/j.jad.2022.03.077. Epub 2022 Apr 4.

Effective differentiation between depressed patients and controls using discriminative eye movement features

Dan Zhang  1 , Xu Liu  1 , Lihua Xu  1 , Yu Li  2 , Yangyang Xu  3 , Mengqing Xia  1 , Zhenying Qian  1 , Yingying Tang  1 , Zhi Liu  4 , Tao Chen  5 , HaiChun Liu  6 , TianHong Zhang  7 , Jijun Wang  8
Affiliations

Effective differentiation between depressed patients and controls using discriminative eye movement features

Dan Zhang et al. J Affect Disord. .

Abstract

Background: Depression is a common debilitating mental disorder caused by various factors. Identifying and diagnosing depression are challenging because the clinical evaluation of depression is mainly subjective, lacking objective and quantitative indicators. The present study investigated the value and significance of eye movement measurements in distinguishing depressed patients from controls.

Methods: Ninety-five depressed patients and sixty-nine healthy controls performed three eye movement tests, including fixation stability, free-viewing, and anti-saccade tests, and eleven eye movement indexes were obtained from these tests. The independent t-test was adopted for group comparisons, and multiple logistic regression analysis was employed to identify diagnostic biomarkers. Support vector machine (SVM), quadratic discriminant analysis (QDA), and Bayesian (BYS) algorithms were applied to build the classification models.

Results: Depressed patients exhibited eye movement anomalies, characterized by increased saccade amplitude in the fixation stability test; diminished saccade velocity in the anti-saccade test; and reduced saccade amplitude, shorter scan path length, lower saccade velocity, decreased dynamic range of pupil size, and lower pupil size ratio in the free-viewing test. Four features mentioned above entered the logistic regression equation. The classification accuracies of SVM, QDA, and BYS models reached 86.0%, 81.1%, and 83.5%, respectively.

Conclusions: Depressed patients exhibited abnormalities across multiple tests of eye movements, assisting in differentiating depressed patients from healthy controls in a cost-effective and non-invasive manner.

Keywords: Depression; Diagnosis; Eye-tracking; Machine learning; Prediction.

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