Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement

Li, Mi; Cao, Lei; Zhai, Qian; Li, Peng; Liu, Sa; Li, Richeng; Feng, Lei; Wang, Gang; Hu, Bin; Lu, Shengfu

doi:10.1155/2020/4174857

Cited by 14 publications

(6 citation statements)

References 24 publications

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“…For example, Li et al. ( 49 ) analyzed eye movement data while viewing emotionally charged images from 48 individuals with depressive symptoms and 48 normal individuals. The learning machine classification method achieved the highest classification accuracy at 84.21%.…”

Section: Discussionmentioning

confidence: 99%

Diagnosing and tracking depression based on eye movement in response to virtual reality

Zheng,

Liang,

Luo

et al. 2024

Front. Psychiatry

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IntroductionDepression is a prevalent mental illness that is primarily diagnosed using psychological and behavioral assessments. However, these assessments lack objective and quantitative indices, making rapid and objective detection challenging. In this study, we propose a novel method for depression detection based on eye movement data captured in response to virtual reality (VR).MethodsEye movement data was collected and used to establish high-performance classification and prediction models. Four machine learning algorithms, namely eXtreme Gradient Boosting (XGBoost), multilayer perceptron (MLP), Support Vector Machine (SVM), and Random Forest, were employed. The models were evaluated using five-fold cross-validation, and performance metrics including accuracy, precision, recall, area under the curve (AUC), and F1-score were assessed. The predicted error for the Patient Health Questionnaire-9 (PHQ-9) score was also determined.ResultsThe XGBoost model achieved a mean accuracy of 76%, precision of 94%, recall of 73%, and AUC of 82%, with an F1-score of 78%. The MLP model achieved a classification accuracy of 86%, precision of 96%, recall of 91%, and AUC of 86%, with an F1-score of 92%. The predicted error for the PHQ-9 score ranged from -0.6 to 0.6.To investigate the role of computerized cognitive behavioral therapy (CCBT) in treating depression, participants were divided into intervention and control groups. The intervention group received CCBT, while the control group received no treatment. After five CCBT sessions, significant changes were observed in the eye movement indices of fixation and saccade, as well as in the PHQ-9 scores. These two indices played significant roles in the predictive model, indicating their potential as biomarkers for detecting depression symptoms.DiscussionThe results suggest that eye movement indices obtained using a VR eye tracker can serve as useful biomarkers for detecting depression symptoms. Specifically, the fixation and saccade indices showed promise in predicting depression. Furthermore, CCBT demonstrated effectiveness in treating depression, as evidenced by the observed changes in eye movement indices and PHQ-9 scores. In conclusion, this study presents a novel approach for depression detection using eye movement data captured in VR. The findings highlight the potential of eye movement indices as biomarkers and underscore the effectiveness of CCBT in treating depression.

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Section: Discussionmentioning

confidence: 99%

Diagnosing and tracking depression based on eye movement in response to virtual reality

Zheng,

Liang,

Luo

et al. 2024

Front. Psychiatry

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“…The research of emotion recognition using eye movement signals has attracted more and more attention in recent years. Li et al [5] proposed a method that combined eye movement signals with other physiological signals to identify depression and achieved good classification accuracy. The method proves that eye behavior is one of the main features of depression, which has an important reference value for the automatic diagnostic system for establishing clinical applications.…”

Section: A Eye Movement-based Approachesmentioning

confidence: 99%

An Emotion Recognition Method Based on Eye Movement and Audiovisual Features in MOOC Learning Environment

Bao

Tao

Zhou

2024

IEEE Trans. Comput. Soc. Syst.

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In recent years, more and more people have begun to use massive online open course (MOOC) platforms for distance learning. However, due to the space-time isolation between teachers and students, the negative emotional state of students in MOOC learning cannot be identified timely. Therefore, students cannot receive immediate feedback about their emotional states. In order to identify and classify learners' emotions in video learning scenarios, we propose a multimodal emotion recognition method based on eye movement signals, audio signals, and video images. In this method, two novel features are proposed: feature of coordinate difference of eyemovement (FCDE) and pixel change rate sequence (PCRS). FCDE is extracted by combining eye movement coordinate trajectory and video optical flow trajectory, which can represent the learner's attention degree. PCRS is extracted from the video image, which can represent the speed of image switching. A feature extraction network based on convolutional neural network (CNN) (FE-CNN) is designed to extract the deep features of the three modals. The extracted deep features are inputted into the emotion classification CNN (EC-CNN) to classify the emotions, including interest, happiness, confusion, and boredom. In single modal identification, the recognition accuracies corresponding to the three modals are 64.32%, 74.67%, and 71.88%. The three modals are fused by feature-level fusion, decision-level fusion, and modellevel fusion methods, and the evaluation experiment results show that the method of decision-level fusion achieved the highest score of 81.90% of emotion recognition. Finally, the effectiveness of FCDE, FE-CNN, and EC-CNN modules is verified by ablation experiments.

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“…Este sistema logró una precisión del 87.20% para la detección de la depresión y pudo diferenciar entre MMD y pacientes sanos con una precisión del 93.00%. En el trabajo de M. Li et al (66) se informó de un método para la clasificación de la depresión basado en patrones de movimiento ocular. Los autores utilizaron estímulos emocionales y tecnologías de seguimiento ocular para registrar datos de comportamiento ocular junto con señales fisiológicas de 48 sujetos de control y 48 pacientes con depresión.…”

Section: Modelos Para La Detección De La Depresión a Través De Caract...unclassified

Comportamiento de la mirada y análisis mediante aprendizaje automático de la depresión en la juventud: una revisión sistemática

Lagunes-Ramírez,

González-Serna,

Rivera-Rivera

et al. 2024

xikua

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Esta revisión tuvo como objetivo comprender la relación entre las características oculares y la depresión en personas jóvenes, para su aplicación en el aprendizaje automático. Se realizó una revisión sistemática para examinar el comportamiento ocular en personas con síntomas depresivos e identificar patrones de movimiento ocular relacionados con trastornos mentales. La búsqueda se realizó utilizando las bases de datos de Google Scholar, Semantic Scholar, PubMed, SpringerLink, MDPI, EBSCO e IEEE Xplore. Se revisaron más de 50 publicaciones de los últimos cinco años. Se revisaron estudios de correlación sobre el comportamiento ocular en personas con depresión y grupos de control, lo que proporcionó información sobre el componente de atención en la depresión. Además, se revisaron investigaciones sobre la detección de la depresión mediante algoritmos de aprendizaje automático y datos oculares donde se utilizaron diferentes paradigmas experimentales de seguimiento ocular y conjuntos de datos para registrar información mientras los participantes observaban estímulos visuales emocionales. Esta revisión presenta relaciones entre diferentes estados mentales y el comportamiento ocular, haciendo hincapié en las poblaciones jóvenes. Mediante la tecnología de seguimiento ocular, es posible apoyar el diagnóstico de la depresión y, por lo tanto, prevenir su desarrollo. Se identificaron tendencias generales para las poblaciones jóvenes y adultas, que deben considerarse en futuras detecciones automáticas de trastornos mentales utilizando datos oculares.

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Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement

Cited by 14 publications

References 24 publications

Diagnosing and tracking depression based on eye movement in response to virtual reality

Diagnosing and tracking depression based on eye movement in response to virtual reality

An Emotion Recognition Method Based on Eye Movement and Audiovisual Features in MOOC Learning Environment

Comportamiento de la mirada y análisis mediante aprendizaje automático de la depresión en la juventud: una revisión sistemática

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