Classification of Depression Using Machine Learning Methods Based on Eye Movement Variance Entropy

Depression is a common mental illness with widespread effects worldwide. At present, most relevant studies are performed by using free-view experiments to identify depression based on eye movements, which identify depression by analyzing the fixation patterns of subjects. However, technical and cost barriers make the rapid and objective detection of depression challenging. This study aimed to develop an objective and convenient method that utilizes eye tracking technology to detect depression. In this study, we designed an eye-tracking algorithm to localize pupil position and presented a variance entropy method to measure the fluctuating characteristics of eye movements. Video data were collected from 115 participants and 35 controls (mean age 24.99 years, standard deviation 6.14 years). The pupil position time series data were formed by the eye-tracking algorithm, and the variance entropy of the pupil position time series was calculated. Based on eye movement variance entropy, we used three machine learning models for classification detection: Logistic Regression, K-Nearest Neighbors, and Random Forest. The accuracy of machine learning in identifying whether a person had depression or not was 97.5%. The accuracy of the four class depression classification (severe, moderate, mild, and no depression) was 82.5%.The accuracy of identifying whether a person had anxiety or not was 95%. The accuracy of identifying whether a person had suicidal tendencies or not was 85%. The results showed that variance entropy based on the eye movement time series can be used as a valid bioindicator for detecting depression.