@inproceedings{451547b7a46445ee9a882d0c97bad9be,
title = "Classification study on eye movement data: Towards a new approach in depression detection",
abstract = "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.",
keywords = "Attention bias, Classification, Depression detection, Eye movement, Healthcare",
author = "Xiaowei Li and Tong Cao and Shuting Sun and Bin Hu and Martyn Ratcliffe",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Congress on Evolutionary Computation, CEC 2016 ; Conference date: 24-07-2016 Through 29-07-2016",
year = "2016",
month = nov,
day = "14",
doi = "10.1109/CEC.2016.7743927",
language = "English",
series = "2016 IEEE Congress on Evolutionary Computation, CEC 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1227--1232",
booktitle = "2016 IEEE Congress on Evolutionary Computation, CEC 2016",
address = "United States",
}
