Depression Recognition Using Machine Learning Algorithms With Eye Tracking, Visual Evoked Potentials, and Auditory P300 Among Chinese Medical Students.
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| Title: | Depression Recognition Using Machine Learning Algorithms With Eye Tracking, Visual Evoked Potentials, and Auditory P300 Among Chinese Medical Students. |
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| Authors: | Liu, Rongxun (AUTHOR), Yan, Jinnan (AUTHOR), Qin, Shisen (AUTHOR), Luo, Peng (AUTHOR), Chen, Yuanle (AUTHOR), Yang, Luhan (AUTHOR), Ji, Guangjun (AUTHOR), Wang, Chao (AUTHOR), Huang, Xuebing (AUTHOR), Wang, Fei (AUTHOR), Meng, Yong (AUTHOR), Wei, Yange (AUTHOR), Ai, Sizhi (AUTHOR) |
| Source: | Depression & Anxiety (1091-4269). 11/20/2025, Vol. 2025, p1-15. 15p. |
| Subjects: | Mental depression, Machine learning, Classification algorithms, Eye tracking, Visual evoked potentials, Medical students, Auditory evoked response, Neurophysiologic monitoring |
| Abstract: | Background: Current assessment of depression primarily relies on psychological scales. Although the use of machine learning in depression has grown, limited reports are available on multiple neurophysiological measurements. We employed machine learning algorithms incorporating eye tracking, visual evoked potentials (VEPs), and auditory P300 to classify depression among Chinese medical students. Methods: A total of 66 students with depression and 72 matched controls were recruited; eye tracking, VEPs, and auditory P300 data were collected. Descriptive analyses and group comparisons were performed between the depression and control groups. Then, multivariate logistic regression (LR) analysis was conducted to evaluate the relationship between eye tracking, VEPs, and auditory P300 features and Patient Health Questionnaire‐9 (PHQ‐9) scores. Furthermore, the study employed six classifiers to differentiate between depression and nondepression. Five‐fold cross‐validation was employed. Model performance was assessed using receiver operating characteristic (ROC) curves, area under the curve (AUC), precision, accuracy, recall, and F1 score. We applied SHapley Additive exPlanations (SHAP) values to explain the model. Results: Depression group was characterized by lower response search scores, higher D values, and prolonged P100 latencies in both eyes. No significant differences were observed in auditory P300 features. Random forest (RF) classifier demonstrated superior classification performance relative to the other five machine learning algorithms. Models utilizing combined features showed enhanced performance compared with those based solely on eye tracking or VEP features. Utilizing the SHAP method, we identified that P100 latency in the right eye was the most significant feature across all machine learning models. Conclusions: Chinese medical students with depression exhibited reduced responsive search scores and extended P100 latencies, suggesting impairments in attention and visual information processing associated with depression. The combined eye tracking and VEPs proved to be more effective than single features for distinguishing depression and nondepression. P100 latency in the right eye may be the most significant predictor of depression. [ABSTRACT FROM AUTHOR] |
| Copyright of Depression & Anxiety (1091-4269) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 189504581 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Depression Recognition Using Machine Learning Algorithms With Eye Tracking, Visual Evoked Potentials, and Auditory P300 Among Chinese Medical Students. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Rongxun%22">Liu, Rongxun</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yan%2C+Jinnan%22">Yan, Jinnan</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qin%2C+Shisen%22">Qin, Shisen</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Luo%2C+Peng%22">Luo, Peng</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Yuanle%22">Chen, Yuanle</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Luhan%22">Yang, Luhan</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ji%2C+Guangjun%22">Ji, Guangjun</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Chao%22">Wang, Chao</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Xuebing%22">Huang, Xuebing</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Fei%22">Wang, Fei</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Meng%2C+Yong%22">Meng, Yong</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wei%2C+Yange%22">Wei, Yange</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ai%2C+Sizhi%22">Ai, Sizhi</searchLink> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Depression+%26+Anxiety+%281091-4269%29%22">Depression & Anxiety (1091-4269)</searchLink>. 11/20/2025, Vol. 2025, p1-15. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Mental+depression%22">Mental depression</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Eye+tracking%22">Eye tracking</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+evoked+potentials%22">Visual evoked potentials</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+students%22">Medical students</searchLink><br /><searchLink fieldCode="DE" term="%22Auditory+evoked+response%22">Auditory evoked response</searchLink><br /><searchLink fieldCode="DE" term="%22Neurophysiologic+monitoring%22">Neurophysiologic monitoring</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Current assessment of depression primarily relies on psychological scales. Although the use of machine learning in depression has grown, limited reports are available on multiple neurophysiological measurements. We employed machine learning algorithms incorporating eye tracking, visual evoked potentials (VEPs), and auditory P300 to classify depression among Chinese medical students. Methods: A total of 66 students with depression and 72 matched controls were recruited; eye tracking, VEPs, and auditory P300 data were collected. Descriptive analyses and group comparisons were performed between the depression and control groups. Then, multivariate logistic regression (LR) analysis was conducted to evaluate the relationship between eye tracking, VEPs, and auditory P300 features and Patient Health Questionnaire‐9 (PHQ‐9) scores. Furthermore, the study employed six classifiers to differentiate between depression and nondepression. Five‐fold cross‐validation was employed. Model performance was assessed using receiver operating characteristic (ROC) curves, area under the curve (AUC), precision, accuracy, recall, and F1 score. We applied SHapley Additive exPlanations (SHAP) values to explain the model. Results: Depression group was characterized by lower response search scores, higher D values, and prolonged P100 latencies in both eyes. No significant differences were observed in auditory P300 features. Random forest (RF) classifier demonstrated superior classification performance relative to the other five machine learning algorithms. Models utilizing combined features showed enhanced performance compared with those based solely on eye tracking or VEP features. Utilizing the SHAP method, we identified that P100 latency in the right eye was the most significant feature across all machine learning models. Conclusions: Chinese medical students with depression exhibited reduced responsive search scores and extended P100 latencies, suggesting impairments in attention and visual information processing associated with depression. The combined eye tracking and VEPs proved to be more effective than single features for distinguishing depression and nondepression. P100 latency in the right eye may be the most significant predictor of depression. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Depression & Anxiety (1091-4269) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/da/8637398 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1 Subjects: – SubjectFull: Mental depression Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Classification algorithms Type: general – SubjectFull: Eye tracking Type: general – SubjectFull: Visual evoked potentials Type: general – SubjectFull: Medical students Type: general – SubjectFull: Auditory evoked response Type: general – SubjectFull: Neurophysiologic monitoring Type: general Titles: – TitleFull: Depression Recognition Using Machine Learning Algorithms With Eye Tracking, Visual Evoked Potentials, and Auditory P300 Among Chinese Medical Students. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Rongxun – PersonEntity: Name: NameFull: Yan, Jinnan – PersonEntity: Name: NameFull: Qin, Shisen – PersonEntity: Name: NameFull: Luo, Peng – PersonEntity: Name: NameFull: Chen, Yuanle – PersonEntity: Name: NameFull: Yang, Luhan – PersonEntity: Name: NameFull: Ji, Guangjun – PersonEntity: Name: NameFull: Wang, Chao – PersonEntity: Name: NameFull: Huang, Xuebing – PersonEntity: Name: NameFull: Wang, Fei – PersonEntity: Name: NameFull: Meng, Yong – PersonEntity: Name: NameFull: Wei, Yange – PersonEntity: Name: NameFull: Ai, Sizhi IsPartOfRelationships: – BibEntity: Dates: – D: 20 M: 11 Text: 11/20/2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10914269 Numbering: – Type: volume Value: 2025 Titles: – TitleFull: Depression & Anxiety (1091-4269) Type: main |
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