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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Armitage, R., Hoffmann, R.F.: Sleep EEG, depression and gender. Sleep Med. Rev. 5(3), 237–246 (2001)
Association, A.P., et al.: DSM-5. diagnostic and statistical manual of mental disorders. Washington, DC: Author (2013)
Bartzokis, G., et al.: In vivo evaluation of brain iron in alzheimer disease using magnetic resonance imaging. Arch. Gen. Psychiatry 57(1), 47–53 (2000)
Beck, A.T., Steer, R.A., Brown, G.K., et al.: Beck depression inventory-ii. San Antonio, vol. 78, No. 2, pp. 490–498 (1996)
Bianchi, R., Laurent, E.: Emotional information processing in depression and burnout: an eye-tracking study. Eur. Arch. Psychiatry Clin. Neurosci. 265(1), 27–34 (2015)
Ding, X., Yue, X., Zheng, R., Bi, C., Li, D., Yao, G.: Classifying major depression patients and healthy controls using EEG, eye tracking and galvanic skin response data. J. Affect. Disord. 251, 156–161 (2019)
Fan, D.P., Wang, W., Cheng, M.M., Shen, J.: Shifting more attention to video salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8554–8564 (2019)
Gotlib, I.H.: Eeg alpha asymmetry, depression, and cognitive functioning. Cognition Emotion 12(3), 449–478 (1998)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1–3), 389–422 (2002)
Han, D., Kim, J.: Unified simultaneous clustering and feature selection for unlabeled and labeled data. IEEE Trans. Neural Netw. Learn. Syst. 29(12), 6083–6098 (2018)
Harikrishna, S., Farquad, M., et al.: Credit scoring using support vector machine: a comparative analysis. In: Advanced Materials Research, vol. 433, pp. 6527–6533. Trans Tech Publ (2012)
Harris, M.S., Reilly, J.L., Thase, M.E., Keshavan, M.S., Sweeney, J.A.: Response suppression deficits in treatment-naive first-episode patients with schizophrenia, psychotic bipolar disorder and psychotic major depression. Psychiatry Res. 170(2–3), 150–156 (2009)
Kellough, J.L., Beevers, C.G., Ellis, A.J., Wells, T.T.: Time course of selective attention in clinically depressed young adults: an eye tracking study. Behav. Res. Ther. 46(11), 1238–1243 (2008)
Koster, E.H., De Raedt, R., Goeleven, E., Franck, E., Crombez, G.: Mood-congruent attentional bias in dysphoria: maintained attention to and impaired disengagement from negative information. Emotion 5(4), 446 (2005)
Ling, H., Qian, C., Kang, W., Liang, C., Chen, H.: Combination of support vector machine and k-fold cross validation to predict compressive strength of concrete in marine environment. Constr. Build. Mater. 206, 355–363 (2019)
Liu, X., et al.: Eye movement pattern and mental retardation in depression. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 970–974. IEEE (2017)
Ma, Y., et al.: Easysvm: a visual analysis approach for open-box support vector machines. Comput. Visual Media 3(2), 161–175 (2017)
Mundra, P.A., Rajapakse, J.C.: SVM-RFE with MRMR filter for gene selection. IEEE Trans. Nanobiosci. 9(1), 31–37 (2009)
Organization, W.H., et al.: Depression and other common mental disorders: global health estimates. World Health Organization, Technical report (2017)
Radloff, L.S.: The CES-D scale: a self-report depression scale for research in the general population. Appl. Psychol. Meas. 1(3), 385–401 (1977)
Sahran, S., Albashish, D., Abdullah, A., Shukor, N.A., Pauzi, S.H.M.: Absolute cosine-based SVM-RFE feature selection method for prostate histopathological grading. Artif. Intell. Med. 87, 78–90 (2018)
Trotzek, M., Koitka, S., Friedrich, C.M.: Utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences. arXiv preprint arXiv:1804.07000 (2018)
Wang, W., Shen, J., Yang, R., Porikli, F.: Saliency-aware video object segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 40(1), 20–33 (2017)
Wulsin, L.R., Vaillant, G.E., Wells, V.E.: A systematic review of the mortality of depression. Psychosom. Med. 61(1), 6–17 (1999)
Zeng, S., Niu, J., Zhu, J., Li, X.: A study on depression detection using eye tracking. In: Tang, Y., Zu, Q., Rodríguez García, J.G. (eds.) HCC 2018. LNCS, vol. 11354, pp. 516–523. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-15127-0_52
Zung, W.W.: A self-rating depression scale. Arch. Gen. Psychiatry 12(1), 63–70 (1965)
Zuroff, D.C., Mongrain, M., Santor, D.A.: Conceptualizing and measuring personality vulnerability to depression: comment on coyne and whiffen (1995) (2004)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Lu, S. et al. (2020). Detection of High-Risk Depression Groups Based on Eye-Tracking Data. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12306. Springer, Cham. https://doi.org/10.1007/978-3-030-60639-8_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-60639-8_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60638-1
Online ISBN: 978-3-030-60639-8
eBook Packages: Computer ScienceComputer Science (R0)