Classification study on eye movement data: Towards a new approach in depression detection

Depression is a common mental disorder with growing prevalence, however current diagnoses of depression face the problem of patient denial, clinical experience and subjective biases from self-report. Our study aims to develop an objective approach to depression detection that supports the process of...

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Bibliographic Details
Published in2016 IEEE Congress on Evolutionary Computation (CEC) pp. 1227 - 1232
Main Authors Xiaowei Li, Tong Cao, Shuting Sun, Bin Hu, Ratcliffe, Martyn
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2016
Subjects
attention bias
classification
depression detection
eye movement
Face
Feature extraction
Gaze tracking
healthcare
Logistics
Niobium
Radio frequency
Support vector machines
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Summary:Depression is a common mental disorder with growing prevalence, however current diagnoses of depression face the problem of patient denial, clinical experience and subjective biases from self-report. Our study aims to develop an objective approach to depression detection that supports the process of diagnosis and assists the monitoring of risk factors. By classifying eye movement features during free viewing tasks, an accuracy of 80.1% was achieved using Random Forest to discriminate depressed and nondepressed subjects. Results indicate that eye movement features hold the potential to form a complimentary method of detection, having a relatively low computation overhead. Furthermore, given the proliferation of cheap internet eye movement detection technologies, the method offers the possibility of cost effective remote sensing of the patient mental state.
DOI:10.1109/CEC.2016.7743927