PSYCH OpenIR  > 社会与工程心理学研究室
Predicting Depression from Internet Behaviors by Timefrequency Features
Zhu, CY (Zhu, Changye)1; Li, BB (Li, Baobin)1; Li, A (Li, Ang)2; Zhu, TS (Zhu, Tingshao)3
First AuthorZhu, CY (Zhu, Changye)
2016-10
Conference NameIEEE/WIC/ACM International Conference on Web Intelligence (WI)
Correspondent Emailtszhu@psych.ac.cn
Source Publication2016 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2016)
Volume不详
Issue不详
Pages383-390
Conference DateOCT 13-16, 2016
Conference PlaceOmaha, NE
Abstract

Early detection of depression is important to improve human well-being. This paper proposes a new method to detect depression through time-frequency analysis of Internet behaviors. We recruited 728 postgraduate students and obtained their scores on a depression questionnaire (Zung Selfrating Depression Scale, SDS) and digital records of Internet behaviors. By time-frequency analysis, we built classification models for differentiating higher SDS group from lower group and prediction models for identifying mental status of depressed group more precisely. Experimental results show classification and prediction models work well, and time-frequency features are effective in capturing the changes of mental health status. Results of this paper might be useful to improve the performance of public mental health services.

DOI10.1109/WI.2016.59
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.psych.ac.cn/handle/311026/26554
Collection社会与工程心理学研究室
Affiliation1.Univ Chinese Acad Sci, Sch Comp & Control, Beijing 100190, Peoples R China
2.Beijing Forestry Univ, Dept Psychol, Beijing 100083, Peoples R China
3.Chinese Acad Sci, Inst Psychol, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Zhu, CY ,Li, BB ,Li, A ,et al. Predicting Depression from Internet Behaviors by Timefrequency Features[C],2016:383-390.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhu, CY (Zhu, Changye)]'s Articles
[Li, BB (Li, Baobin)]'s Articles
[Li, A (Li, Ang)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhu, CY (Zhu, Changye)]'s Articles
[Li, BB (Li, Baobin)]'s Articles
[Li, A (Li, Ang)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhu, CY (Zhu, Changye)]'s Articles
[Li, BB (Li, Baobin)]'s Articles
[Li, A (Li, Ang)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.