Detection of High-Risk Depression Groups Based on Eye-Tracking Data

Simeng Lu, Shen Huang, Yun Zhang, Xiujuan Zheng, Danmin Miao, Jiajun Wang, Zheru Chi

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Depression is the most common psychiatric disorder in the general population. An effective treatment of depression requires early detection. In reschedule this paper, a novel algorithm is presented based on eye-tracking and a self-rating high-risk depression screening scale (S-hr-DS) for early depression screening. In this algorithm, a subject scan path is encoded by semantic areas of interest (AOIs). AOIs are dynamically generated by the POS (part-of-speech) tagging of Chinese words in the S-hr-DS items. The proposed method considers both temporal and spatial information of the eye-tracking data and encodes the subject scan path with semantic features of items. The support vector machine recursive feature elimination (SVM-RFE) algorithm is employed for feature selection and model training. Experimental results on a data set including 69 subjects show that our proposed algorithm can achieve an accuracy of 81% with 76% in sensitivity and 79% in F1-score, demonstrating a potential application in high-risk depression detection.

Original languageEnglish
Title of host publicationPattern Recognition and Computer Vision - 3rd Chinese Conference, PRCV 2020, Proceedings
EditorsYuxin Peng, Hongbin Zha, Qingshan Liu, Huchuan Lu, Zhenan Sun, Chenglin Liu, Xilin Chen, Jian Yang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages492-503
Number of pages12
ISBN (Print)9783030606381
DOIs
Publication statusPublished - 2020
Event3rd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2020 - Nanjing, China
Duration: 16 Oct 202018 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12306 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2020
Country/TerritoryChina
CityNanjing
Period16/10/2018/10/20

Keywords

  • Eye-tracking
  • Feature selection
  • High-risk depression groups
  • Word-fixation driven feature coding

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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