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

doi:10.1007/978-3-030-60639-8_41

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

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-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 language	English
Title of host publication	Pattern Recognition and Computer Vision - 3rd Chinese Conference, PRCV 2020, Proceedings
Editors	Yuxin Peng, Hongbin Zha, Qingshan Liu, Huchuan Lu, Zhenan Sun, Chenglin Liu, Xilin Chen, Jian Yang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	492-503
Number of pages	12
ISBN (Print)	9783030606381
DOIs	https://doi.org/10.1007/978-3-030-60639-8_41
Publication status	Published - Oct 2020
Event	3rd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2020 - Nanjing, China Duration: 16 Oct 2020 → 18 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12306 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2020
Country/Territory	China
City	Nanjing
Period	16/10/20 → 18/10/20

Keywords

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

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

More information

10.1007/978-3-030-60639-8_41

Cite this

Lu, S., Huang, S., Zhang, Y., Zheng, X., Miao, D., Wang, J., & Chi, Z. (2020). Detection of High-Risk Depression Groups Based on Eye-Tracking Data. In Y. Peng, H. Zha, Q. Liu, H. Lu, Z. Sun, C. Liu, X. Chen, & J. Yang (Eds.), Pattern Recognition and Computer Vision - 3rd Chinese Conference, PRCV 2020, Proceedings (pp. 492-503). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12306 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60639-8_41

Lu, Simeng ; Huang, Shen ; Zhang, Yun et al. / Detection of High-Risk Depression Groups Based on Eye-Tracking Data. Pattern Recognition and Computer Vision - 3rd Chinese Conference, PRCV 2020, Proceedings. editor / Yuxin Peng ; Hongbin Zha ; Qingshan Liu ; Huchuan Lu ; Zhenan Sun ; Chenglin Liu ; Xilin Chen ; Jian Yang. Springer Science and Business Media Deutschland GmbH, 2020. pp. 492-503 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Detection of High-Risk Depression Groups Based on Eye-Tracking Data",

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.",

keywords = "Eye-tracking, Feature selection, High-risk depression groups, Word-fixation driven feature coding",

author = "Simeng Lu and Shen Huang and Yun Zhang and Xiujuan Zheng and Danmin Miao and Jiajun Wang and Zheru Chi",

note = "Funding Information: Acknowledgement. The work reported in this paper was supported by the Awareness and Cognitive Neuroscience based Multimodal Co-sensing Technology Project (ACNMCT) in charge of professor Danmin Miao of Department of military medical psychology, Air Force Medial University, the Natural Science Foundation of China No. 61473243, and the Natural Science Foundation of Jiangsu Province No. BK20171249. The authors thank the anonymous reviewers for their constructive comments and valuable suggestions. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 3rd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2020 ; Conference date: 16-10-2020 Through 18-10-2020",

year = "2020",

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doi = "10.1007/978-3-030-60639-8_41",

language = "English",

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publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "492--503",

editor = "Yuxin Peng and Hongbin Zha and Qingshan Liu and Huchuan Lu and Zhenan Sun and Chenglin Liu and Xilin Chen and Jian Yang",

booktitle = "Pattern Recognition and Computer Vision - 3rd Chinese Conference, PRCV 2020, Proceedings",

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Lu, S, Huang, S, Zhang, Y, Zheng, X, Miao, D, Wang, J & Chi, Z 2020, Detection of High-Risk Depression Groups Based on Eye-Tracking Data. in Y Peng, H Zha, Q Liu, H Lu, Z Sun, C Liu, X Chen & J Yang (eds), Pattern Recognition and Computer Vision - 3rd Chinese Conference, PRCV 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12306 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 492-503, 3rd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2020, Nanjing, China, 16/10/20. https://doi.org/10.1007/978-3-030-60639-8_41

Detection of High-Risk Depression Groups Based on Eye-Tracking Data. / Lu, Simeng; Huang, Shen; Zhang, Yun et al.
Pattern Recognition and Computer Vision - 3rd Chinese Conference, PRCV 2020, Proceedings. ed. / Yuxin Peng; Hongbin Zha; Qingshan Liu; Huchuan Lu; Zhenan Sun; Chenglin Liu; Xilin Chen; Jian Yang. Springer Science and Business Media Deutschland GmbH, 2020. p. 492-503 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12306 LNCS).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Lu, Simeng

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AU - Zheng, Xiujuan

AU - Miao, Danmin

AU - Wang, Jiajun

AU - Chi, Zheru

N1 - Funding Information: Acknowledgement. The work reported in this paper was supported by the Awareness and Cognitive Neuroscience based Multimodal Co-sensing Technology Project (ACNMCT) in charge of professor Danmin Miao of Department of military medical psychology, Air Force Medial University, the Natural Science Foundation of China No. 61473243, and the Natural Science Foundation of Jiangsu Province No. BK20171249. The authors thank the anonymous reviewers for their constructive comments and valuable suggestions. Publisher Copyright: © 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/10

Y1 - 2020/10

N2 - 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.

AB - 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.

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DO - 10.1007/978-3-030-60639-8_41

M3 - Conference article published in proceeding or book

AN - SCOPUS:85093972176

SN - 9783030606381

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Lu S, Huang S, Zhang Y, Zheng X, Miao D, Wang J et al. Detection of High-Risk Depression Groups Based on Eye-Tracking Data. In Peng Y, Zha H, Liu Q, Lu H, Sun Z, Liu C, Chen X, Yang J, editors, Pattern Recognition and Computer Vision - 3rd Chinese Conference, PRCV 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 492-503. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-60639-8_41

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

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