A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  > In Press, Accepted Manuscript
Q. Li, X. Liu, X. Hu, M. A. R. Ahad, M. Ren, L. Yao, and Y. Huang, “Machine learning-based prediction of depressive disorders via various data modalities: A survey,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 6, pp. 1–28, Jun. 2025.
Citation: Q. Li, X. Liu, X. Hu, M. A. R. Ahad, M. Ren, L. Yao, and Y. Huang, “Machine learning-based prediction of depressive disorders via various data modalities: A survey,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 6, pp. 1–28, Jun. 2025.
shu

Machine Learning-Based Prediction of Depressive Disorders via Various Data Modalities: A Survey

Funds:  This work was supported by the National Natural Science Foundation of China (62276025, 62206022), the Shenzhen Technology Plan Program (KQTD20170331093217368), and the China Postdoctoral Science Foundation (BX20230044, 2023M730290)
More Information
    Abstract
  • Depression, a pervasive mental health disorder, has substantial impacts on both individuals and society. The conventional approach to predicting depression necessitates substantial collaboration between health care professionals and patients, leaving room for the influence of subjective factors. Consequently, it is imperative to develop a more efficient and accessible prediction methodology for depression. In recent years, numerous investigations have delved into depression prediction techniques, employing diverse data modalities and yielding notable advancements. Given the rapid progression of this domain, the present article comprehensively reviews major breakthroughs in depression prediction, encompassing multiple data modalities such as electrophysiological signals, brain imaging, audiovisual data, and text. By integrating depression prediction methods from various data modalities, it offers a comparative assessment of their advantages and limitations, providing a well-rounded perspective on how different modalities can complement each other for more accurate and holistic depression prediction. The survey begins by examining commonly used datasets, evaluation metrics, and methodological frameworks. For each data modality, it systematically analyzes traditional machine learning methods alongside the increasingly prevalent deep learning approaches, providing a comparative assessment of detection frameworks, feature representations, context modeling, and training strategies. Finally, the survey culminates with the identification of prospective avenues that warrant further exploration. It provides researchers with valuable insights and practical guidance to advance the field of depression prediction.

     

    • FullText(HTML)
  • loading
    • Qiong Li and Xiaotong Liu contributed equally to this work.
    References(361)
  • [1]
    Q. Wang, “The social determinants of depressive disorders in china,” Lancet psychiatry, 2021.
    [2]
    D. Bhugra, et al, “Globalisation and mental disorders: overview with relation to depression,” The British Journal of Psychiatry, vol. 184, no. 1, pp. 10–20, 2004.
    [3]
    T. S. Rao, et al, “Understanding nutrition, depression and mental illnesses,” Indian journal of psychiatry, vol. 50, no. 2, p. 77, 2008.
    [4]
    W. H. Organization. (2021) Depression. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/depression
    [5]
    R. Peveler, et al, “Depression in medical patients,” Bmj, vol. 325, no. 7356, pp. 149–152, 2002.
    [6]
    K. Demyttenaere, et al, “Comorbid painful physical symptoms and depression: prevalence, work loss, and help seeking,” Journal of affective disorders, vol. 92, no. 2-3, pp. 185–193, 2006.
    [7]
    A. Singh, et al, “Loneliness, depression and sociability in old age,” Industrial psychiatry journal, vol. 18, no. 1, p. 51, 2009.
    [8]
    D. Eisenberg, et al, “Prevalence and correlates of depression, anxiety, and suicidality among university students,” American journal of orthopsychiatry, vol. 77, no. 4, pp. 534–542, 2007.
    [9]
    I. O. Bergfeld, et al, “Treatment-resistant depression and suicidality,” Journal of affective disorders, vol. 235, pp. 362–367, 2018.
    [10]
    L. Hemming, et al, “Alexithymia and its associations with depression, suicidality, and aggression: an overview of the literature,” Frontiers in psychiatry, vol. 10, p. 203, 2019.
    [11]
    J. X. Wiebenga, et al, “Associations of three major physiological stress systems with suicidal ideation and suicide attempts in patients with a depressive and/or anxiety disorder,” Brain, behavior, and immunity, vol. 102, pp. 195–205, 2022.
    [12]
    D. Faust, et al, “The expert witness in psychology and psychiatry,” Science, vol. 241, no. 4861, pp. 31–35, 1988.
    [13]
    P. Cassano, et al, “Depression and public health: an overview,” Journal of psychosomatic research, vol. 53, no. 4, pp. 849–857, 2002.
    [14]
    D. C. Mohr, et al, “Barriers to psychotherapy among depressed and nondepressed primary care patients,” Annals of Behavioral Medicine, vol. 32, no. 3, pp. 254–258, 2006.
    [15]
    R. L. Kravitz, et al, “Relational barriers to depression help-seeking in primary care,” Patient education and counseling, vol. 82, no. 2, pp. 207–213, 2011.
    [16]
    http://www.cbsr.ia.ac.cn/users/ynyu/dataset/.
    [17]
    N. Cheng, et al, “Addressing the nationwide shortage of child and adolescent psychiatrists: determining factors that influence the decision for psychiatry residents to pursue child and adolescent psychiatry training,” Academic psychiatry, vol. 46, no. 1, pp. 18–24, 2022.
    [18]
    T. Butryn, et al, “The shortage of psychiatrists and other mental health providers: causes, current state, and potential solutions,” Int. Journal of Academic Medicine, vol. 3, no. 1, p. 5, 2017. doi: 10.4103/IJAM.IJAM_49_17
    [19]
    S. Abuse et al., “Key substance use and mental health indicators in the united states: results from the 2019 national survey on drug use and health,” 2020.
    [20]
    B. A. Pescosolido, et al, “The “backbone of stigma: identifying the global core of public prejudice associated with mental illness,” American journal of public health, vol. 103, no. 5, pp. 853–860, 2013.
    [21]
    S. Wang, et al, “Cyberbullying and cyberviolence detection: A triangular user-activity-content view,” IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 8, pp. 1384–1405, 2022.
    [22]
    U. R. Acharya, et al, “Automated eeg-based screening of depression using deep convolutional neural network,” Computer methods and programs in biomedicine, vol. 161, pp. 103–113, 2018.
    [23]
    R. Wang, et al, “Depression analysis and recognition based on functional near-infrared spectroscopy,” JBHI, vol. 25, no. 12, pp. 4289–4299, 2021.
    [24]
    W. Melo et al., “Combining global and local convolutional 3d networks for detecting depression from facial expressions,” in 2019 14th ieee international conference on automatic face & gesture recognition. IEEE, 2019, pp. 1–8.
    [25]
    F. Ringeval et al., “Avec 2017: Real-life depression, and affect recognition workshop and challenge,” in Proc. the 7th annual workshop on AVEC, 2017, pp. 3–9.
    [26]
    H. Lu et al., “Postgraduate student depression assessment by multimedia gait analysis,” IEEE MultiMedia, 2022.
    [27]
    G. Rao, et al, “Mgl-cnn: a hierarchical posts representations model for identifying depressed individuals in online forums,” IEEE Access, vol. 8, pp. 32395–32403, 2020.
    [28]
    L. Jin, S. Li, J. Yu, and J. He, “Robot manipulator control using neural networks: A survey,” Neurocomputing, vol. 285, pp. 23–34, 2018.
    [29]
    Y. Zhang, S. Li, and X. Zhou, “Recurrent-neural-network-based velocity-level redundancy resolution for manipulators subject to a joint acceleration limit,” IEEE Trans. Industrial Electronics, vol. 66, no. 5, pp. 3573–3582, 2018.
    [30]
    S. Li, Z. Shao, and Y. Guan, “A dynamic neural network approach for efficient control of manipulators,” IEEE Trans. Systems, Man, and Cybernetics: Systems, vol. 49, no. 5, pp. 932–941, 2017.
    [31]
    J. Li, et al, “New discretization-formula-based zeroing dynamics for real-time tracking control of serial and parallel manipulators,” IEEE Trans. Industrial Informatics, vol. 14, no. 8, pp. 3416–3425, 2017.
    [32]
    A. J. Moshayedi, N. M. I. Uddin, A. S. Khan, J. Zhu, and M. Emadi Andani, “Designing and developing a vision-based system to investigate the emotional effects of news on short sleep at noon: An experimental case study,” Sensors, vol. 23, no. 20, p. 8422, 2023.
    [33]
    J. Luo, et al, “Big data application in biomedical research and health care: a literature review,” Biomedical informatics insights, vol. 8, p. BII-S31559, 2016.
    [34]
    S. C. Liao, et al, “Major depression detection from eeg signals using kernel eigen-filter-bank common spatial patterns,” Sensors, vol. 17, no. 6, p. 1385, 2017.
    [35]
    M. Wei, et al, “Identifying major depressive disorder using hurst exponent of resting-state brain networks,” Psychiatry Research: Neuroimaging, vol. 214, no. 3, pp. 306–312, 2013.
    [36]
    M. Valstar et al., “Avec 2013: the continuous audio/visual emotion and depression recognition challenge,” in Proc. the 3rd ACM international workshop on AVEC, 2013, pp. 3–10.
    [37]
    S. Tsugawa et al., “Recognizing depression from twitter activity,” in Proc. the 33rd annual ACM conference on human factors in computing systems, 2015, pp. 3187–3196.
    [38]
    G. Xu et al., “The object detection, perspective and obstacles in robotic: a review,” EAI Endorsed Trans. AI and Robotics, vol. 1, no. 1, 2022.
    [39]
    A. J. Moshayedi, A. S. Khan, S. Yang, and S. M. Zanjani, “Personal image classifier based handy pipe defect recognizer (hpd): Design and test,” in 2022 7th Int. Conf. on Intelligent Computing and Signal Processing. IEEE, 2022, pp. 1721–1728.
    [40]
    A. J. Moshayedi, K. S. Reza, A. S. Khan, and A. Nawaz, “Integrating virtual reality and robotic operation system (ros) for agv navigation,” EAI Endorsed Trans. AI and Robotics, vol. 2, 2023.
    [41]
    A. J. Moshayedi, A. S. Roy, L. Liao, A. S. Khan, A. Kolahdooz, and A. Eftekhari, “Design and development of foodiebot robot: From simulation to design,” IEEE Access, 2024.
    [42]
    G. Sharma, et al, “Dephnn: a novel hybrid neural network for electroencephalogram (eeg)-based screening of depression,” Biomedical signal processing and control, vol. 66, p. 102393, 2021.
    [43]
    M. Mousavian et al., “Depression detection using atlas from fmri images,” in 19th IEEE ICMLA, 2020, pp. 1348–1353.
    [44]
    P. Zhang et al., “Depa: Self-supervised audio embedding for depression detection,” in Proc. the 29th ACM MM, 2021, pp. 135–143.
    [45]
    J. Yang, et al, “Data augmentation for depression detection using skeleton-based gait information,” Medical & Biological Engineering & Computing, vol. 60, no. 9, pp. 2665–2679, 2022.
    [46]
    U. Lee, et al, “Toward data-driven digital therapeutics analytics: Literature review and research directions,” IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 1, pp. 42–66, 2023. doi: 10.1109/JAS.2023.123015
    [47]
    K. M. Hasib, M. R. Islam, S. Sakib, M. A. Akbar, I. Razzak, and M. S. Alam, “Depression detection from social networks data based on machine learning and deep learning techniques: An interrogative survey,” IEEE Trans. Computational Social Systems, vol. 10, no. 4, pp. 1568–1586, 2023.
    [48]
    L. He, et al, “Deep learning for depression recognition with audiovisual cues: A review,” Information Fusion, vol. 80, pp. 56–86, 2022.
    [49]
    S. Chancellor, et al, “Methods in predictive techniques for mental health status on social media: a critical review,” NPJ digital medicine, vol. 3, no. 1, p. 43, 2020. doi: 10.1038/s41746-020-0233-7
    [50]
    A. Safayari, et al, “Depression diagnosis by deep learning using eeg signals: A systematic review,” Medicine in Novel Technology and Devices, vol. 12, p. 100102, 2021. doi: 10.1016/j.medntd.2021.100102
    [51]
    A. Ashraf, et al, “On the review of image and video-based depression detection using machine learning,” IJEECS, vol. 19, no. 3, pp. 1677–1684, 2020. doi: 10.11591/ijeecs.v19.i3.pp1677-1684
    [52]
    S. Aleem, et al, “Machine learning algorithms for depression: diagnosis, insights, and research directions,” Electronics, vol. 11, no. 7, p. 1111, 2022. doi: 10.3390/electronics11071111
    [53]
    P. Wu, et al, “Automatic depression recognition by intelligent speech signal processing: A systematic survey,” CAAI Trans. Intelligence Technology, vol. 8, no. 3, pp. 701–711, 2023. doi: 10.1049/cit2.12113
    [54]
    A. Sarkar, et al, “A deep learning-based comparative study to track mental depression from eeg data,” Neuroscience Informatics, vol. 2, no. 4, p. 100039, 2022. doi: 10.1016/j.neuri.2022.100039
    [55]
    R. Salas-Zárate et al., “Detecting depression signs on social media: a systematic literature review,” in Healthcare, vol. 10, no. 2. MDPI, 2022, p. 291.
    [56]
    S. J. Pinto and M. Parente, “Comprehensive review of depression detection techniques based on machine learning approach,” Soft Computing, pp. 1–25, 2024.
    [57]
    S. Bhadra, et al, “An insight into diagnosis of depression using machine learning techniques: a systematic review,” Current medical research and opinion, vol. 38, no. 5, pp. 749–771, 2022. doi: 10.1080/03007995.2022.2038487
    [58]
    A. Widmann, et al, “Digital filter design for electrophysiological data-a practical approach,” Journal of neuroscience methods, vol. 250, pp. 34–46, 2015. doi: 10.1016/j.jneumeth.2014.08.002
    [59]
    D. M. Schnyer, et al, “Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor mri measures in individuals with major depressive disorder,” Psychiatry Research: Neuroimaging, vol. 264, pp. 1–9, 2017. doi: 10.1016/j.pscychresns.2017.03.003
    [60]
    J. R. Sato, et al, “Machine learning algorithm accurately detects fmri signature of vulnerability to major depression,” Psychiatry Research: Neuroimaging, vol. 233, no. 2, pp. 289–291, 2015. doi: 10.1016/j.pscychresns.2015.07.001
    [61]
    R. Ramasubbu, et al, “Accuracy of automated classification of major depressive disorder as a function of symptom severity,” NeuroImage: Clinical, vol. 12, pp. 320–331, 2016. doi: 10.1016/j.nicl.2016.07.012
    [62]
    B. Vai, et al, “Predicting differential diagnosis between bipolar and unipolar depression with multiple kernel learning on multimodal structural neuroimaging,” European Neuropsychopharmacology, vol. 34, pp. 28–38, 2020. doi: 10.1016/j.euroneuro.2020.03.008
    [63]
    Y. Wei, et al, “Functional near-infrared spectroscopy (fnirs) as a tool to assist the diagnosis of major psychiatric disorders in a chinese population,” European archives of psychiatry and clinical neuroscience, vol. 271, no. 4, pp. 745–757, 2021. doi: 10.1007/s00406-020-01125-y
    [64]
    Y. Wang, et al, “Multi-modal domain adaptation variational autoencoder for eeg-based emotion recognition,” IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 9, pp. 1612–1626, 2022. doi: 10.1109/JAS.2022.105515
    [65]
    A. Lenartowicz et al., “Brain imaging,” 2017.
    [66]
    E. J. Nestler, et al, “Neurobiology of depression,” Neuron, vol. 34, no. 1, pp. 13–25, 2002. doi: 10.1016/S0896-6273(02)00653-0
    [67]
    G. Nilsonne, et al, “Eeg-based model and antidepressant response,” Nature Biotechnology, vol. 39, no. 1, pp. 27–27, 2021. doi: 10.1038/s41587-020-00768-5
    [68]
    M. Shim, et al, “Machine-learning-based classification between post-traumatic stress disorder and major depressive disorder using p300 features,” NeuroImage: Clinical, vol. 24, p. 102001, 2019. doi: 10.1016/j.nicl.2019.102001
    [69]
    H. Jiang, et al, “Predictability of depression severity based on posterior alpha oscillations,” Clinical Neurophysiology, vol. 127, no. 4, pp. 2108–2114, 2016. doi: 10.1016/j.clinph.2015.12.018
    [70]
    X. Li et al., “Classification study on eye movement data: Towards a new approach in depression detection,” in IEEE CEC, 2016, pp. 1227–1232.
    [71]
    A. Y. Kim, et al, “Automatic detection of major depressive disorder using electrodermal activity,” Scientific reports, vol. 8, no. 1, pp. 1–9, 2018.
    [72]
    C. Sobin, et al, “Psychomotor symptoms of depression,” American Journal of Psychiatry, vol. 154, no. 1, pp. 4–17, 1997. doi: 10.1176/ajp.154.1.4
    [73]
    X. Ma et al., “Depaudionet: An efficient deep model for audio based depression classification,” in Proc. the 6th international workshop on AVEC, 2016, pp. 35–42.
    [74]
    X. Zhou, et al, “Visually interpretable representation learning for depression recognition from facial images,” IEEE TAC, vol. 11, no. 3, pp. 542–552, 2018.
    [75]
    B. Miao, et al, “Automatic mental health identification method based on natural gait pattern,” PsyCh Journal, vol. 10, no. 3, pp. 453–464, 2021. doi: 10.1002/pchj.434
    [76]
    Z. Liu, et al, “A facial expression emotion recognition based human-robot interaction system,” IEEE CAA J. Autom. Sinica, vol. 4, no. 4, pp. 668–676, 2017. doi: 10.1109/JAS.2017.7510622
    [77]
    S. Thomée, et al, “Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults-a prospective cohort study,” BMC public health, vol. 11, no. 1, pp. 1–11, 2011. doi: 10.1186/1471-2458-11-1
    [78]
    Z. Huang et al., “Depression detection from short utterances via diverse smartphones in natural environmental conditions.” in INTERSPEECH, 2018, pp. 3393–3397.
    [79]
    A. Dogrucu, et al, “Moodable: On feasibility of instantaneous depression assessment using machine learning on voice samples with retrospectively harvested smartphone and social media data,” Smart Health, vol. 17, p. 100118, 2020. doi: 10.1016/j.smhl.2020.100118
    [80]
    M. Trotzek, et al, “Utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences,” IEEE TKDE, vol. 32, no. 3, pp. 588–601, 2018.
    [81]
    S. Park et al., “Manifestation of depression and loneliness on social networks: a case study of young adults on facebook,” in ACM CSCW, 2015, pp. 557–570.
    [82]
    X. Yang, et al, “A big data analytics framework for detecting user-level depression from social networks,” IJIM, vol. 54, p. 102141, 2020.
    [83]
    X. Kang, et al, “Exploring latent semantic information for textual emotion recognition in blog articles,” IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 1, pp. 204–216, 2017.
    [84]
    P. A. Lalousis, et al, “Heterogeneity and classification of recent onset psychosis and depression: a multimodal machine learning approach,” Schizophrenia bulletin, vol. 47, no. 4, pp. 1130–1140, 2021. doi: 10.1093/schbul/sbaa185
    [85]
    Y. Meng, et al, “Bidirectional representation learning from transformers using multimodal electronic health record data to predict depression,” JBHI, vol. 25, no. 8, pp. 3121–3129, 2021.
    [86]
    Y. J. Toenders, et al, “Predicting depression onset in young people based on clinical, cognitive, environmental, and neurobiological data,” Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, vol. 7, no. 4, pp. 376–384, 2022. doi: 10.1016/j.bpsc.2021.03.005
    [87]
    L. Yang et al., “Hybrid depression classification and estimation from audio video and text information,” in Proc. the 7th annual workshop on AVEC, 2017, pp. 45–51.
    [88]
    U. R. Acharya, et al, “A novel depression diagnosis index using nonlinear features in eeg signals,” European neurology, vol. 74, no. 1-2, pp. 79–83, 2015. doi: 10.1159/000438457
    [89]
    H. Cai, et al, “A multi-modal open dataset for mental-disorder analysis,” Scientific Data, vol. 9, no. 1, p. 178, 2022. doi: 10.1038/s41597-022-01211-x
    [90]
    S. Hong et al., “Automatic depression discrimination on fnirs by using fastica/wpd and svm,” in Proc. the 2015 Chinese Intelligent Automation Conf.: Intelligent Information Processing. Springer, 2015, pp. 257–265.
    [91]
    M. Valstar, B. Schuller, K. Smith, T. Almaev, F. Eyben, J. Krajewski, R. Cowie, and M. Pantic, “Avec 2014: 3d dimensional affect and depression recognition challenge,” in Proc. the 4th international workshop on AVEC, 2014, pp. 3–10.
    [92]
    M. Valstar, J. Gratch, B. Schuller, F. Ringeval, D. Lalanne, M. Torres Torres, S. Scherer, G. Stratou, R. Cowie, and M. Pantic, “Avec 2016: Depression, mood, and emotion recognition workshop and challenge,” in Proc. the 6th international workshop on AVEC, 2016, pp. 3–10.
    [93]
    F. Ringeval et al., “Avec 2019 workshop and challenge: state-of-mind, detecting depression with ai, and cross-cultural affect recognition,” in Proc. the 9th Int. on AVEC, 2019, pp. 3–12.
    [94]
    Y. Shen et al., “Automatic depression detection: An emotional audio-textual corpus and a gru/bilstm-based model,” in 2022-2022 IEEE ICASSP. IEEE, 2022, pp. 6247–6251.
    [95]
    A. Yates et al., “Depression and self-harm risk assessment in online forums,” arXiv preprint arXiv: 1709.01848, 2017.
    [96]
    M. Stankevich et al., “Feature engineering for depression detection in social media,” in ICPRAM, 2018.
    [97]
    G. Coppersmith et al., “Clpsych 2015 shared task: Depression and ptsd on twitter,” in Proc. the 2nd workshop on CLPsych: from linguistic signal to clinical reality, 2015, pp. 31–39.
    [98]
    Z. Jamil, “Monitoring tweets for depression to detect at-risk users,” Ph.D. dissertation, Université d’Ottawa/University of Ottawa, 2017.
    [99]
    Y. Yuan et al., “Depression identification from gait spectrum features based on hilbert-huang transform,” in HCC. Springer, 2018, pp. 503–515.
    [100]
    F. Ringeval et al., “Avec 2015: The 5th international audio/visual emotion challenge and workshop,” in Proc. the 23rd ACM MM, 2015, pp. 1335–1336.
    [101]
    F. Ringeval, B. Schuller, M. Valstar, R. Cowie, H. Kaya, M. Schmitt, S. Amiriparian, N. Cummins, D. Lalanne, A. Michaud et al., “Avec 2018 workshop and challenge: Bipolar disorder and cross-cultural affect recognition,” in Proc. the 2018 on AVEC, 2018, pp. 3–13.
    [102]
    J. Gratch et al., “The distress analysis interview corpus of human and computer interviews,” UNIVERSITY OF SOUTHERN CALIFORNIA LOS ANGELES, Tech. Rep., 2014.
    [103]
    C. h. Wuu et al., “Multiface: A dataset for neural face rendering,” arXiv preprint arXiv: 2207.11243, 2022.
    [104]
    M. Tlachac et al., “Emu: Early mental health uncovering framework and dataset,” in 2021 20th IEEE ICMLA, 2021, pp. 1311–1318.
    [105]
    S. Alghowinem, et al, “Multimodal depression detection: fusion analysis of paralinguistic, head pose and eye gaze behaviors,” IEEE TAC, vol. 9, no. 4, pp. 478–490, 2016.
    [106]
    Y. Yang, et al, “Detecting depression severity from vocal prosody,” IEEE TAC, vol. 4, no. 2, pp. 142–150, 2012.
    [107]
    D. Highland, et al, “A review of detection techniques for depression and bipolar disorder,” Smart Health, vol. 24, p. 100282, 2022. doi: 10.1016/j.smhl.2022.100282
    [108]
    Z. Yang, et al, “An effective sparsity evaluation criterion for power-line interference suppression of eeg signal,” Frontiers in Neuroscience, vol. 16, p. 984471, 2022. doi: 10.3389/fnins.2022.984471
    [109]
    Y. Sun, et al, “Capacitive biopotential measurement for electrophysiological signal acquisition: A review,” IEEE Sensors Journal, vol. 16, no. 9, pp. 2832–2853, 2016. doi: 10.1109/JSEN.2016.2519392
    [110]
    L. Xu, et al, “Use of power-line interference for adaptive motion artifact removal in biopotential measurements,” Physiological measurement, vol. 37, no. 1, p. 25, 2015.
    [111]
    S. B. Kotsiantis, et al, “Data preprocessing for supervised leaning,” Int. journal of computer science, vol. 1, no. 2, pp. 111–117, 2006.
    [112]
    S. García et al., Data preprocessing in data mining. Springer, 2015, vol. 72.
    [113]
    X. Gong et al., “Joint prediction of multiple vulnerability characteristics through multi-task learning,” in 2019 24th ICECCS, 2019, pp. 31–40.
    [114]
    M. M. Meskhi et al., “Learning abstract task representations,” in AAAI Workshop on Meta-Learning and MetaDL Challenge. PMLR, 2021, pp. 127–137.
    [115]
    I. Tavchioski, et al, “Early detection of depression with linear models using hand-crafted and contextual features,” Working Notes of CLEF, pp. 5–8, 2022.
    [116]
    J. R. Williamson et al., “Vocal biomarkers of depression based on motor incoordination,” in Proc. the 3rd ACM international workshop on AVEC, 2013, pp. 41–48.
    [117]
    C. J. Burges, “A tutorial on support vector machines for pattern recognition,” Data mining and knowledge discovery, vol. 2, no. 2, pp. 121–167, 1998. doi: 10.1023/A:1009715923555
    [118]
    A. J. Smola, et al, “A tutorial on support vector regression,” Statistics and computing, vol. 14, pp. 199–222, 2004. doi: 10.1023/B:STCO.0000035301.49549.88
    [119]
    L. Breiman, “Random forests,” Machine learning, vol. 45, pp. 5–32, 2001. doi: 10.1023/A:1010933404324
    [120]
    S. Menard, Applied logistic regression analysis. Sage, 2002, no. 106.
    [121]
    Y. Kang et al., “Deep transformation learning for depression diagnosis from facial images,” in Chinese conference on biometric recognition. Springer, 2017, pp. 13–22.
    [122]
    A. Salekin, et al, “A weakly supervised learning framework for detecting social anxiety and depression,” Proc. the ACM on interactive, mobile, wearable and ubiquitous technologies, vol. 2, no. 2, pp. 1–26, 2018.
    [123]
    T. Ran et al., “Identifying depressive disorder with sleep electroencephalogram data: A study based on deep learning,” Journal of Sichuan University (Medical Science Edition), vol. 54, no. 2, 2023.
    [124]
    Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998. doi: 10.1109/5.726791
    [125]
    J. L. Elman, “Finding structure in time,” Cognitive science, vol. 14, no. 2, pp. 179–211, 1990. doi: 10.1207/s15516709cog1402_1
    [126]
    S. Hochreiter, et al, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997. doi: 10.1162/neco.1997.9.8.1735
    [127]
    A. Vaswani et al., “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.
    [128]
    J. Jung et al., “Hique: Hierarchical question embedding network for multimodal depression detection,” in Proc. the 33rd ACM Int. Conf. on Information and Knowledge Management, 2024, pp. 1049–1059.
    [129]
    T. Baltrušaitis et al., “Openface: an open source facial behavior analysis toolkit,” in 2016 IEEE winter conference on applications of computer vision. IEEE, 2016, pp. 1–10.
    [130]
    W. C. De Melo et al., “Encoding temporal information for automatic depression recognition from facial analysis,” in ICASSP 2020-2020 IEEE ICASSP. IEEE, 2020, pp. 1080–1084.
    [131]
    X. Li et al., “Depression severity prediction from facial expression based on the drr_depressionnet network,” in 2020 IEEE Int. Conf. on BIBM. IEEE, 2020, pp. 2757–2764.
    [132]
    Y. Zhu, et al, “Automated depression diagnosis based on deep networks to encode facial appearance and dynamics,” IEEE TAC, vol. 9, no. 4, pp. 578–584, 2017.
    [133]
    Z. Li, et al, “Diversified regularization enhanced training for effective manipulator calibration,” IEEE TNNLS, vol. 34, no. 11, pp. 8778–8790, 2022.
    [134]
    Z. Li, S. Li, A. Francis, and X. Luo, “A novel calibration system for robot arm via an open dataset and a learning perspective,” IEEE Trans. Circuits and Systems II: Express Briefs, vol. 69, no. 12, pp. 5169–5173, 2022. doi: 10.1109/TCSII.2022.3199158
    [135]
    Z. Li, S. Li, and X. Luo, “An overview of calibration technology of industrial robots,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 1, pp. 23–36, 2021. doi: 10.1109/JAS.2020.1003381
    [136]
    E. Aghajari and A. A. AbdulRahim, “Prediction of short circuit current of wind turbines based on artificial neural network model,” EAI Endorsed Trans. AI and Robotics, vol. 3, 2024.
    [137]
    M. Davari, A. Harooni, A. Nasr, K. Savoji, and M. Soleimani, “Improving recognition accuracy for facial expressions using scattering wavelet,” EAI Endorsed Trans. AI and Robotics, vol. 3, 2024.
    [138]
    A. Gupta, “Improved hybrid preprocessing technique for effective segmentation of wheat canopies in chlorophyll fluorescence images,” EAI Endorsed Trans. AI and Robotics, vol. 3, 2024.
    [139]
    X. Luo, et al, “Efficient and high-quality recommendations via momentum-incorporated parallel stochastic gradient descent-based learning,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 2, pp. 402–411, 2020.
    [140]
    X. Wen and M. Zhou, “Evolution and role of optimizers in training deep learning models,” IEEE/CAA Journal of Automatica Sinica, vol. 11, no. 10, pp. 2039–2042, 2024. doi: 10.1109/JAS.2024.124806
    [141]
    W. He, et al, “Differentiable automatic data augmentation by proximal update for medical image segmentation,” IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 7, pp. 1315–1318, 2022.
    [142]
    X. Jiang and S. Li, “Bas: Beetle antennae search algorithm for optimization problems. arxiv preprint abs/1710.10724,” URL http://arxiv. org/abs/1710.10724, vol. 1710, 2017.
    [143]
    Y. Zhang, S. Li, and B. Xu, “Convergence analysis of beetle antennae search algorithm and its applications,” Soft Computing, vol. 25, no. 16, pp. 10595–10608, 2021.
    [144]
    Q. Wu, et al, “A new fallback beetle antennae search algorithm for path planning of mobile robots with collision-free capability,” Soft Computing, vol. 24, pp. 2369–2380, 2020.
    [145]
    A. H. Khan, X. Cao, S. Li, V. N. Katsikis, and L. Liao, “Bas-adam: An adam based approach to improve the performance of beetle antennae search optimizer,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 2, pp. 461–471, 2020.
    [146]
    W. H. Organization. (2022) Depression. [Online]. Available: https://www.who.int/health-topics/depression#tab=tab_1
    [147]
    J. R. Hughes, et al, “Conventional and quantitative electroencephalography in psychiatry,” The Journal of neuropsychiatry and clinical neurosciences, vol. 11, no. 2, pp. 190–208, 1999.
    [148]
    P. L. Faris, et al, “Evidence for a vagal pathophysiology for bulimia nervosa and the accompanying depressive symptoms,” Journal of affective disorders, vol. 92, no. 1, pp. 79–90, 2006.
    [149]
    M. R. Islam et al., “Emotion recognition from eeg signal focusing on deep learning and shallow learning techniques,” IEEE Access, vol. 9, 2021.
    [150]
    T. Song et al., “Variational instance-adaptive graph for eeg emotion recognition,” IEEE TAC, 2021.
    [151]
    G. Zhao et al., “Multi-target positive emotion recognition from eeg signals,” IEEE TAC, 2020.
    [152]
    B. Hosseinifard, et al, “Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from eeg signal,” Computer methods and programs in biomedicine, vol. 109, no. 3, pp. 339–345, 2013.
    [153]
    J. Shen, et al, “An improved empirical mode decomposition of electroencephalogram signals for depression detection,” IEEE TAC, vol. 13, no. 1, pp. 262–271, 2019.
    [154]
    M. Sharma, et al, “An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with eeg signals,” Cognitive Systems Research, vol. 52, pp. 508–520, 2018. doi: 10.1016/j.cogsys.2018.07.010
    [155]
    H. Cai, et al, “Study on feature selection methods for depression detection using three-electrode eeg data,” Interdisciplinary Sciences: Computational Life Sciences, vol. 10, pp. 558–565, 2018.
    [156]
    S. Mahato, et al, “Detection of major depressive disorder using linear and non-linear features from eeg signals,” Microsystem Technologies, vol. 25, pp. 1065–1076, 2019.
    [157]
    P. P. Thoduparambil, et al, “Eeg-based deep learning model for the automatic detection of clinical depression,” Physical and Engineering Sciences in Medicine, vol. 43, no. 4, pp. 1349–1360, 2020.
    [158]
    X. Li, et al, “Eeg-based mild depression recognition using convolutional neural network,” Medical & biological engineering & computing, vol. 57, no. 6, pp. 1341–1352, 2019.
    [159]
    M. Kang, et al, “Deep-asymmetry: Asymmetry matrix image for deep learning method in pre-screening depression,” Sensors, vol. 20, no. 22, p. 6526, 2020.
    [160]
    D. Wang et al., “Identification of depression with a semi-supervised gcn based on eeg data,” in 2021 IEEE BIBM. IEEE, 2021, pp. 2338–2345.
    [161]
    A. Saeedi, et al, “Major depressive disorder diagnosis based on effective connectivity in eeg signals: A convolutional neural network and long short-term memory approach,” Cognitive Neurodynamics, vol. 15, no. 2, pp. 239–252, 2021.
    [162]
    G. Rodriguez-Bermudez, et al, “Analysis of eeg signals using nonlinear dynamics and chaos: a review,” Applied mathematics & information sciences, vol. 9, no. 5, p. 2309, 2015.
    [163]
    S. A. Akar, et al, “Nonlinear analysis of eegs of patients with major depression during different emotional states,” Computers in biology and medicine, vol. 67, pp. 49–60, 2015.
    [164]
    A. Ortiz, et al, “The futility of long-term predictions in bipolar disorder: mood fluctuations are the result of deterministic chaotic processes,” Int. journal of bipolar disorders, vol. 9, no. 1, pp. 1–7, 2021.
    [165]
    A. Kustubayeva, et al, “Lyapunov exponent of theta rhythm as a marker of depression during attentional network test,” Biological Psychiatry, vol. 89, no. 9, p. S164, 2021.
    [166]
    G. Chopra, et al, “Using machine learning algorithms classified depressed patients and normal people,” Int. Journal of Machine Learning for Sustainable Development, vol. 4, no. 1, pp. 31–40, 2022.
    [167]
    L. Zhao, et al, “Cardiorespiratory coupling analysis based on entropy and cross-entropy in distinguishing different depression stages,” Frontiers in physiology, vol. 10, p. 359, 2019. doi: 10.3389/fphys.2019.00359
    [168]
    M. Cukic et al., “Eeg machine learning with higuchi fractal dimension and sample entropy as features for successful detection of depression,” arXiv preprint arXiv: 1803.05985, 2018.
    [169]
    L. Zhao, et al, “Frontal alpha eeg asymmetry variation of depression patients assessed by entropy measures and lemple-ziv complexity,” JMBE, vol. 41, no. 2, pp. 146–154, 2021.
    [170]
    Y. Li, et al, “Eeg-based mild depressive detection using differential evolution,” IEEE Access, vol. 7, pp. 7814–7822, 2018.
    [171]
    H. Cai, et al, “Feature-level fusion approaches based on multimodal eeg data for depression recognition,” Information Fusion, vol. 59, pp. 127–138, 2020. doi: 10.1016/j.inffus.2020.01.008
    [172]
    O. Faust et al., “Depression diagnosis support system based on eeg signal entropies,” Journal of mechanics in medicine and biology, vol. 14, no. 03, 2014.
    [173]
    W. Mumtaz, et al, “A machine learning framework involving eeg-based functional connectivity to diagnose major depressive disorder (mdd),” Medical & biological engineering & computing, vol. 56, pp. 233–246, 2018.
    [174]
    M. Bachmann, et al, “Methods for classifying depression in single channel eeg using linear and nonlinear signal analysis,” Computer methods and programs in biomedicine, vol. 155, pp. 11–17, 2018. doi: 10.1016/j.cmpb.2017.11.023
    [175]
    Z. Wan, et al, “Hybrideegnet: A convolutional neural network for eeg feature learning and depression discrimination,” IEEE Access, vol. 8, pp. 30332–30342, 2020. doi: 10.1109/ACCESS.2020.2971656
    [176]
    A. Saeedi et al., “Depression diagnosis and drug response prediction via recurrent neural networks and transformers utilizing eeg signals,” arXiv preprint arXiv: 2303.06033, 2023.
    [177]
    X. Sun, et al, “A novel complex network-based graph convolutional network in major depressive disorder detection,” IEEE Trans. Instrumentation and Measurement, vol. 71, pp. 1–8, 2022.
    [178]
    H. Wu and J. Liu, “A multi-stream deep learning model for eeg-based depression identification,” in 2022 IEEE BIBM. IEEE, 2022, pp. 2029–2034.
    [179]
    H. Wu et al., “Eeg-based depression identification using a deep learning model,” in 2022 IEEE 6th Conf. on Information and Communication Technology. IEEE, 2022, pp. 1–5.
    [180]
    B. M. Appelhans, et al, “Heart rate variability as an index of regulated emotional responding,” Review of general psychology, vol. 10, no. 3, pp. 229–240, 2006. doi: 10.1037/1089-2680.10.3.229
    [181]
    H. R. Variability, “Standards of measurement, physiological interpretation, and clinical use. task force of the european society of cardiology and the north american society of pacing and electrophysiology,” Eur Heart J, vol. 17, no. 3, pp. 354–381, 1996. doi: 10.1093/oxfordjournals.eurheartj.a014868
    [182]
    V. K. Jandackova, et al, “Heart rate variability and depressive symptoms: a cross-lagged analysis over a 10-year period in the whitehall ii study,” Psychological medicine, vol. 46, no. 10, pp. 2121–2131, 2016. doi: 10.1017/S003329171600060X
    [183]
    B. Ay, et al, “Automated depression detection using deep representation and sequence learning with eeg signals,” Journal of medical systems, vol. 43, no. 7, pp. 1–12, 2019.
    [184]
    Z. Zhang, et al, “A novel eeg-based graph convolution network for depression detection: incorporating secondary subject partitioning and attention mechanism,” Expert Systems with Applications, vol. 239, p. 122356, 2024. doi: 10.1016/j.eswa.2023.122356
    [185]
    Z. Wang, et al, “Eeg-based high-performance depression state recognition,” Frontiers in Neuroscience, vol. 17, p. 1301214, 2024. doi: 10.3389/fnins.2023.1301214
    [186]
    S. Byun, et al, “Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol,” Computers in biology and medicine, vol. 112, p. 103381, 2019. doi: 10.1016/j.compbiomed.2019.103381
    [187]
    F. Zhang et al., “Depression recognition based on electrocardiogram,” in 2023 8th Int. Conf. on Computer and Communication Systems. IEEE, 2023, pp. 1–5.
    [188]
    T. Roh et al., “Wearable depression monitoring system with heart-rate variability,” in 2014 36th EMBC. IEEE, 2014, pp. 562–565.
    [189]
    T. Matsui, et al, “Impaired parasympathetic augmentation under relaxation in patients with depression as assessed by a novel non-contact microwave radar system,” Journal of Medical Engineering & Technology, vol. 40, no. 1, pp. 15–19, 2016.
    [190]
    G. Sun, et al, “An objective screening method for major depressive disorder using logistic regression analysis of heart rate variability data obtained in a mental task paradigm,” Frontiers in Psychiatry, vol. 7, p. 180, 2016.
    [191]
    D. Kuang, et al, “Depression recognition according to heart rate variability using bayesian networks,” Journal of psychiatric research, vol. 95, pp. 282–287, 2017. doi: 10.1016/j.jpsychires.2017.09.012
    [192]
    S. T. Noor et al., “Predicting the risk of depression based on ecg using rnn,” Computational Intelligence and Neuroscience, vol. 2021, 2021.
    [193]
    X. Zang, et al, “End-to-end depression recognition based on a one-dimensional convolution neural network model using two-lead ecg signal,” JMBE, vol. 42, no. 2, pp. 225–233, 2022.
    [194]
    S. Mohanraj et al., “A deep convolution neural network framework for detecting depression,” in 2022 6th ICICCS. IEEE, 2022, pp. 1061–1068.
    [195]
    R. Hudson, et al, “Phytocannabinoids modulate emotional memory processing through interactions with the ventral hippocampus and mesolimbic dopamine system: implications for neuropsychiatric pathology,” Psychopharmacology, vol. 235, pp. 447–458, 2018. doi: 10.1007/s00213-017-4766-7
    [196]
    J. Jiang, et al, “Effect of multiple neonatal sevoflurane exposures on hippocampal apolipoprotein e levels and learning and memory abilities,” Pediatrics & Neonatology, vol. 59, no. 2, pp. 154–160, 2018.
    [197]
    M. A. O. Santos, et al, “Global hippocampal atrophy in major depressive disorder: a meta-analysis of magnetic resonance imaging studies,” Trends in psychiatry and psychotherapy, vol. 40, pp. 369–378, 2018. doi: 10.1590/2237-6089-2017-0130
    [198]
    P. Chagué, et al, “Radiological classification of dementia from anatomical mri assisted by machine learning-derived maps,” Journal of Neuroradiology, vol. 48, no. 6, pp. 412–418, 2021. doi: 10.1016/j.neurad.2020.04.004
    [199]
    M. Mousavian et al., “Depression detection using feature extraction and deep learning from smri images,” in 2019 18th IEEE Int. Conf. On Machine Learning And Applications. IEEE, 2019, pp. 1731–1736.
    [200]
    J. X. Zheng, et al, “Disrupted spontaneous neural activity related to cognitive impairment in postpartum women,” Frontiers in psychology, vol. 9, p. 624, 2018. doi: 10.3389/fpsyg.2018.00624
    [201]
    J. Zhao, et al, “Functional network connectivity (fnc)-based generative adversarial network (gan) and its applications in classification of mental disorders,” Journal of Neuroscience Methods, vol. 341, p. 108756, 2020. doi: 10.1016/j.jneumeth.2020.108756
    [202]
    P. C. Mulders, et al, “Resting-state functional connectivity in major depressive disorder: a review,” Neuroscience & Biobehavioral Reviews, vol. 56, pp. 330–344, 2015.
    [203]
    S. Yu, et al, “Toward probabilistic diagnosis and understanding of depression based on functional mri data analysis with logistic group lasso,” PLOS ONE, vol. 10, no. 5, p. e0123524, 2015. doi: 10.1371/journal.pone.0123524
    [204]
    A. Yamashita, et al, “Generalizable brain network markers of major depressive disorder across multiple imaging sites,” PLoS biology, vol. 18, no. 12, p. e3000966, 2020. doi: 10.1371/journal.pbio.3000966
    [205]
    J. V. Haxby, et al, “Distributed and overlapping representations of faces and objects in ventral temporal cortex,” Science, vol. 293, no. 5539, pp. 2425–2430, 2001. doi: 10.1126/science.1063736
    [206]
    B. Yan et al., “Quantitative identification of major depression based on resting-state dynamic functional connectivity: A machine learning approach,” Frontiers in Neuroscience, vol. 14, 2020.
    [207]
    X. Wang et al., “Depression disorder classification of fmri data using sparse low-rank functional brain network and graph-based features,” Computational and mathematical methods in medicine, vol. 2017, 2017.
    [208]
    R. Bhaumik, et al, “Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity,” Neuroimage Clinical, p. 390, 2016.
    [209]
    A. Lord et al., “Changes in community structure of resting state functional connectivity in unipolar depression,” 2012.
    [210]
    G. E. Hinton et al., “Transforming auto-encoders,” Springer, Berlin, Heidelberg, 2012.
    [211]
    R. Gui, et al, “The impact of emotional music on active roi in patients with depression based on deep learning: A task-state fmri study,” Computational Intelligence and Neuroscience, vol. 2019, no. 6, pp. 1–14, 2019.
    [212]
    D. Yao et al., “Triplet graph convolutional network for multi-scale analysis of functional connectivity using functional mri,” in Int. Workshop on GLMI. Springer, 2019, pp. 70–78.
    [213]
    S. I. Ktena, et al, “Metric learning with spectral graph convolutions on brain connectivity networks,” NeuroImage, vol. 169, pp. 431–442, 2018. doi: 10.1016/j.neuroimage.2017.12.052
    [214]
    E. Jun et al., “Identifying resting-state effective connectivity abnormalities in drug-nave major depressive disorder diagnosis via graph convolutional networks,” Human Brain Mapping, vol. 41, no. 17, 2020.
    [215]
    Q. Wang et al., “Function mri representation learning via self-supervised transformer for automated brain disorder analysis,” in Int. Workshop on MLMI. Springer, 2022, pp. 1–10.
    [216]
    Y. Cheng et al., “Classification algorithms for brain magnetic resonance imaging images of patients with end-stage renal disease and depression,” Contrast Media & Molecular Imaging, vol. 2022, 2022.
    [217]
    T. Ma, et al, “Distinguishing bipolar depression from major depressive disorder using fnirs and deep neural network,” Progress In Electromagnetics Research, vol. 169, pp. 73–86, 2020. doi: 10.2528/PIER20102202
    [218]
    Y. Zhu, et al, “Classifying major depressive disorder using fnirs during motor rehabilitation,” IEEE Trans. Neural Systems and Rehabilitation Engineering, vol. 28, no. 4, pp. 961–969, 2020. doi: 10.1109/TNSRE.2020.2972270
    [219]
    L. Fu, et al, “Reduced prefrontal activation during the tower of london and verbal fluency task in patients with bipolar depression: A multi-channel nirs study,” Frontiers in Psychiatry, vol. 9, p. 214, 2018. doi: 10.3389/fpsyt.2018.00214
    [220]
    M. Kawano, et al, “Correlation between frontal lobe oxy-hemoglobin and severity of depression assessed using near-infrared spectroscopy,” Journal of Affective Disorders, vol. 205, pp. 154–158, 2016. doi: 10.1016/j.jad.2016.07.013
    [221]
    A. Manelis, et al, “The role of the right prefrontal cortex in recognition of facial emotional expressions in depressed individuals: fnirs study,” Journal of Affective Disorders, vol. 258, pp. 151–158, 2019. doi: 10.1016/j.jad.2019.08.006
    [222]
    L. Gu et al., “Classification of depressive disorder based on rs-fmri using multivariate pattern analysis with multiple features,” in 2017 4th IAPR ACPR, 2017, pp. 61–66.
    [223]
    L. L. Zeng et al., “Unsupervised classification of major depression using functional connectivity mri,” Human Brain Mapping, 2014.
    [224]
    M. Mousavian, et al, “Depression detection from smri and rs-fmri images using machine learning,” Journal of Intelligent Information Systems, vol. 57, no. 2, pp. 395–418, 2021. doi: 10.1007/s10844-021-00653-w
    [225]
    M. Patil, P. Mukherji, and V. Wadhai, “A novel hybrid optimization algorithm for depression detection using mri and speech signal,” Biomedical Signal Processing and Control, vol. 86, p. 105046, 2023. doi: 10.1016/j.bspc.2023.105046
    [226]
    Y. Fang, et al, “Unsupervised cross-domain functional mri adaptation for automated major depressive disorder identification,” Medical image analysis, vol. 84, p. 102707, 2023. doi: 10.1016/j.media.2022.102707
    [227]
    Y. Zhang et al., “Wavelet-enhanced cnn for depression classification based on mri images,” in ICIP. IEEE, 2024, pp. 3117–3123.
    [228]
    H. Song et al., “Automatic depression discrimination on fnirs by using general linear model and svm,” in 2014 7th Int. Conf. on Biomedical Engineering and Informatics. IEEE, 2014, pp. 278–282.
    [229]
    Y. Zhu and M. Mehta, “Machine learning approach on frontal lobe activity to assess depression in adults: Implications for rehabilitation outcomes,” 2017, pp. 1–2.
    [230]
    Q. Yu, et al, “Gnn-based depression recognition using spatio-temporal information: A fnirs study,” JBHI, vol. 26, no. 10, pp. 4925–4935, 2022.
    [231]
    J. Chao, et al, “fnirs evidence for distinguishing patients with major depression and healthy controls,” IEEE Trans. Neural Systems and Rehabilitation Engineering, vol. 29, pp. 2211–2221, 2021. doi: 10.1109/TNSRE.2021.3115266
    [232]
    K. Shao et al., “fnirs-driven depression recognition based on cross-modal data augmentation,” IEEE Trans. Neural Systems and Rehabilitation Engineering, 2024.
    [233]
    X. Wang, et al, “Identifying psychological symptoms based on facial movements,” Frontiers in Psychiatry, vol. 11, p. 607890, 2020. doi: 10.3389/fpsyt.2020.607890
    [234]
    J. M. Girard et al., “Social risk and depression: Evidence from manual and automatic facial expression analysis,” in FG, 2013.
    [235]
    J. Li et al., “A novel study for mdd detection through task-elicited facial cues,” in 2018 IEEE Int. Conf. on BIBM, 2018.
    [236]
    A. Mulay et al., “Automatic depression level detection through visual input,” in 2020 Fourth WorldS4, 2020.
    [237]
    J. Rottenberg, et al, “Emotion context insensitivity in major depressive disorder,” Journal of Abnormal Psychology, vol. 114, no. 4, pp. 627–639, 2005. doi: 10.1037/0021-843X.114.4.627
    [238]
    L. M. Bylsma, et al, “A meta-analysis of emotional reactivity in major depressive disorder,” Clinical psychology review, vol. 28, no. 4, pp. 676–691, 2008. doi: 10.1016/j.cpr.2007.10.001
    [239]
    A. Pampouchidou et al., “Facial geometry and speech analysis for depression detection,” in 2017 39th EMBC. IEEE, 2017, pp. 1433–1436.
    [240]
    S. Alghowinem et al., “Head pose and movement analysis as an indicator of depression,” in 2013 Humaine Association Conf. on Affective Computing and Intelligent Interaction. IEEE, 2013, pp. 283–288.
    [241]
    H. Meng et al., “Depression recognition based on dynamic facial and vocal expression features using partial least square regression,” in Proc. the 3rd ACM international workshop on AVEC, 2013, pp. 21–30.
    [242]
    S. Alghowinem et al., “Eye movement analysis for depression detection,” in ICIP, 2013.
    [243]
    J. F. Cohn et al., “Detecting depression from facial actions and vocal prosody,” in 2009 3rd Int. Conf. on Affective Computing and Intelligent Interaction and Workshops, 2009.
    [244]
    A. Jan et al., “Automatic depression scale prediction using facial expression dynamics and regression,” in Int. Workshop on AVEC, 2014.
    [245]
    A. Pampouchidou et al., “Depression assessment by fusing high and low level features from audio, video, and text,” in Proc. the 6th Int. Workshop on AVEC, 2016, pp. 27–34.
    [246]
    W. Guo, et al, “Deep neural networks for depression recognition based on 2d and 3d facial expressions under emotional stimulus tasks,” Frontiers in neuroscience, vol. 15, p. 609760, 2021. doi: 10.3389/fnins.2021.609760
    [247]
    A. Pampouchidou et al., “Video-based depression detection using local curvelet binary patterns in pairwise orthogonal planes.” IEEE, 2016, pp. 3835–3838.
    [248]
    N. Cummins et al., “Diagnosis of depression by behavioural signals: a multimodal approach,” in Proc. the 3rd ACM international workshop on AVEC, 2013, pp. 11–20.
    [249]
    M. Nasir et al., “Multimodal and multiresolution depression detection from speech and facial landmark features,” in Proc. the 6th international workshop on AVEC, 2016, pp. 43–50.
    [250]
    F. Eyben et al., “Opensmile: the munich versatile and fast open-source audio feature extractor,” in Proc. the 18th ACM MM, 2010, pp. 1459–1462.
    [251]
    G. Littlewort et al., “The computer expression recognition toolbox (cert),” in 2011 IEEE Int. Conf. on Automatic Face & Gesture Recognition. IEEE, 2011, pp. 298–305.
    [252]
    M. Al Jazaery, et al, “Video-based depression level analysis by encoding deep spatiotemporal features,” IEEE TAC, vol. 12, no. 1, pp. 262–268, 2018.
    [253]
    W. C. De Melo et al., “Depression detection based on deep distribution learning,” in ICIP. IEEE, 2019, pp. 4544–4548.
    [254]
    X. Zhou, et al, “Learning content-adaptive feature pooling for facial depression recognition in videos,” Electronics Letters, vol. 55, no. 11, pp. 648–650, 2019. doi: 10.1049/el.2019.0443
    [255]
    L. Chao et al., “Multi task sequence learning for depression scale prediction from video,” in ACII. IEEE, 2015, pp. 526–531.
    [256]
    A. Jan, et al, “Artificial intelligent system for automatic depression level analysis through visual and vocal expressions,” IEEE Trans. Cognitive and Developmental Systems, vol. 10, no. 3, pp. 668–680, 2017.
    [257]
    W. C. De Melo, E. Granger, and A. Hadid, “A deep multiscale spatiotemporal network for assessing depression from facial dynamics,” IEEE transactions on affective computing, vol. 13, no. 3, pp. 1581–1592, 2020.
    [258]
    G. E. Hinton, et al, “A fast learning algorithm for deep belief nets,” Neural computation, vol. 18, no. 7, pp. 1527–1554, 2006. doi: 10.1162/neco.2006.18.7.1527
    [259]
    R. Flores et al., “Temporal facial features for depression screening,” in Adjunct Proc. the 2022 ACM Int. Joint Conf. on Pervasive and Ubiquitous Computing and the 2022 ACM Int. Symposium on Wearable Computers, 2022, pp. 488–493.
    [260]
    H. Kaya et al., “Eyes whisper depression: A cca based multimodal approach,” in Proc. the 22nd ACM MM, 2014, pp. 961–964.
    [261]
    L. Wen etal., “Automated depression diagnosis based on facial dynamic analysis and sparse coding,” IEEE Trans. Information Forensics and Security, vol. 10, no. 7, pp. 1432–1441, 2015. doi: 10.1109/TIFS.2015.2414392
    [262]
    A. Ray et al., “Multi-level attention network using text, audio and video for depression prediction,” in Proc. the 9th international on AVEC, 2019, pp. 81–88.
    [263]
    Y. Wang et al., “Automatic depression detection via facial expressions using multiple instance learning,” in 2020 IEEE 17th Int. Symposium on Biomedical Imaging (ISBI). IEEE, 2020, pp. 1933–1936.
    [264]
    Y. Pan, et al, “Spatial-temporal attention network for depression recognition from facial videos,” Expert Systems with Applications, vol. 237, p. 121410, 2024. doi: 10.1016/j.eswa.2023.121410
    [265]
    Z. Liu, et al, “Pra-net: Part-and-relation attention network for depression recognition from facial expression,” Computers in Biology and Medicine, vol. 157, p. 106589, 2023. doi: 10.1016/j.compbiomed.2023.106589
    [266]
    S. Zhang et al., “Mtdan: A lightweight multi-scale temporal difference attention networks for automated video depression detection,” IEEE TAC, 2023.
    [267]
    S. A. Montgomery, et al, “A new depression scale designed to be sensitive to change,” The British journal of psychiatry, vol. 134, no. 4, pp. 382–389, 1979. doi: 10.1192/bjp.134.4.382
    [268]
    M. Asgari et al., “Inferring clinical depression from speech and spoken utterances,” in 2014 IEEE international workshop on Machine Learning for Signal Processing, 2014, pp. 1–5.
    [269]
    E. W. McGinnis, et al, “Giving voice to vulnerable children: machine learning analysis of speech detects anxiety and depression in early childhood,” IEEE journal of biomedical and health informatics, vol. 23, no. 6, pp. 2294–2301, 2019. doi: 10.1109/JBHI.2019.2913590
    [270]
    P. Wu et al., “Automatic depression recognition by intelligent speech signal processing: A systematic survey,” CAAI Trans. Intelligence Technology, 2022.
    [271]
    M. Tlachac et al., “Early mental health uncovering with short scripted and unscripted voice recordings,” in Deep Learning Applications, Volume 4. Springer, 2022, pp. 79–110.
    [272]
    M. Rodrigues Makiuchi et al., “Multimodal fusion of bert-cnn and gated cnn representations for depression detection,” in Proc. the 9th Int. on AVEC, 2019, pp. 55–63.
    [273]
    R. Flores et al., “Depression screening using deep learning on follow-up questions in clinical interviews,” in 2021 20th IEEE ICMLA, 2021, pp. 595–600.
    [274]
    K. Takakusaki, “Neurophysiology of gait: from the spinal cord to the frontal lobe,” Movement Disorders, vol. 28, no. 11, pp. 1483–1491, 2013. doi: 10.1002/mds.25669
    [275]
    P. L. Sheridan, et al, “The role of higher-level cognitive function in gait: executive dysfunction contributes to fall risk in alzheimer’s disease,” Dementia and geriatric cognitive disorders, vol. 24, no. 2, pp. 125–137, 2007. doi: 10.1159/000105126
    [276]
    L. Sloman, et al, “Mood, depressive illness and gait patterns,” The Canadian Journal of Psychiatry, vol. 32, no. 3, pp. 190–193, 1987. doi: 10.1177/070674378703200306
    [277]
    T. C. Brandler, et al, “Depressive symptoms and gait dysfunction in the elderly,” The American Journal of Geriatric Psychiatry, vol. 20, no. 5, pp. 425–432, 2012. doi: 10.1097/JGP.0b013e31821181c6
    [278]
    M. B. Van Iersel, et al, “Quantitative gait analysis to detect gait disorders in geriatric patients with depression,” J Am Geriatr Soc, vol. 53, no. 8, pp. 1441–2, 2005. doi: 10.1111/j.1532-5415.2005.53433_4.x
    [279]
    J. Michalak, et al, “The effects of mindfulness-based cognitive therapy on depressive gait patterns,” Journal of Cognitive and Behavioral Psychotherapies, vol. 11, no. 1, pp. 13–27, 2011.
    [280]
    S. Radovanović, et al, “Gait characteristics in patients with major depression performing cognitive and motor tasks while walking,” Psychiatry research, vol. 217, no. 1-2, pp. 39–46, 2014. doi: 10.1016/j.psychres.2014.02.001
    [281]
    A. S. Naidu, et al, “Does dual-task gait differ in those with late-life depression versus mild cognitive impairment?,” The American Journal of Geriatric Psychiatry, vol. 27, no. 1, pp. 62–72, 2019. doi: 10.1016/j.jagp.2018.10.011
    [282]
    J. Michalak, et al, “Embodiment of sadness and depression—gait patterns associated with dysphoric mood,” Psychosomatic medicine, vol. 71, no. 5, pp. 580–587, 2009. doi: 10.1097/PSY.0b013e3181a2515c
    [283]
    M. R. Lemke, et al, “Spatiotemporal gait patterns during over ground locomotion in major depression compared with healthy controls,” Journal of psychiatric research, vol. 34, no. 4-5, pp. 277–283, 2000. doi: 10.1016/S0022-3956(00)00017-0
    [284]
    S. Xu et al., “Emotion recognition from gait analyses: Current research and future directions,” arXiv preprint arXiv: 2003.11461, 2020.
    [285]
    F. Gholami, et al, “A microsoft kinect-based point-of-care gait assessment framework for multiple sclerosis patients,” JBHI, vol. 21, no. 5, pp. 1376–1385, 2016.
    [286]
    M. J. Nordin, et al, “A survey of gait recognition based on skeleton model for human identification,” Research Journal of Applied Sciences, Engineering and Technology, vol. 12, no. 7, pp. 756–763, 2016. doi: 10.19026/rjaset.12.2751
    [287]
    Y. Wang et al., “Gait recognition based on 3d skeleton joints captured by kinect,” in ICIP, 2016, pp. 3151–3155.
    [288]
    J. Han, et al, “Individual recognition using gait energy image,” PAMI, vol. 28, no. 2, pp. 316–322, 2005.
    [289]
    X. Yang, et al, “Gait recognition based on dynamic region analysis,” Signal Processing, vol. 88, no. 9, pp. 2350–2356, 2008. doi: 10.1016/j.sigpro.2008.03.006
    [290]
    S. Chancellor et al., “Quantifying and predicting mental illness severity in online pro-eating disorder communities,” in Proc. the 19th ACM CSCW, 2016, pp. 1171–1184.
    [291]
    T. Nguyen, et al, “Affective and content analysis of online depression communities,” IEEE TAC, vol. 5, no. 3, pp. 217–226, 2014.
    [292]
    J. Wolohan et al., “Detecting linguistic traces of depression in topic-restricted text: Attending to self-stigmatized depression with nlp,” in Proc. the first international workshop on language cognition and computational models, 2018, pp. 11–21.
    [293]
    M. Nadeem, “Identifying depression on twitter,” arXiv preprint arXiv: 1607.07384, 2016.
    [294]
    E. E. Newell, et al, “You sound so down: Capturing depressed affect through depressed language,” Journal of Language and Social Psychology, vol. 37, no. 4, pp. 451–474, 2018. doi: 10.1177/0261927X17731123
    [295]
    M. De Choudhury et al., “Predicting depression via social media,” in Seventh international AAAI conference on weblogs and social media, 2013.
    [296]
    F. Magami et al., “Automatic detection of depression from text data: A systematic literacture review,” in XVI Brazilian Symposium on Information Systems, 2020, pp. 1–8.
    [297]
    A. Zirikly et al., “Explaining models of mental health via clinically grounded auxiliary tasks,” in Proc. the Eighth Workshop on Computational Linguistics and Clinical Psychology, 2022, pp. 30–39.
    [298]
    A. Mallol-Ragolta et al., “A hierarchical attention network-based approach for depression detection from transcribed clinical interviews,” 2019.
    [299]
    T. Shen et al., “Cross-domain depression detection via harvesting social media.” Int. Joint Conf.s on Artificial Intelligence, 2018.
    [300]
    S. G. Burdisso, et al, “A text classification framework for simple and effective early depression detection over social media streams,” Expert Systems with Applications, vol. 133, pp. 182–197, 2019. doi: 10.1016/j.eswa.2019.05.023
    [301]
    Z. Peng, et al, “Multi-kernel svm based depression recognition using social media data,” Int. Journal of Machine Learning and Cybernetics, vol. 10, no. 1, pp. 43–57, 2019. doi: 10.1007/s13042-017-0697-1
    [302]
    M. Tlachac, et al, “Screening for depression with retrospectively harvested private versus public text,” IEEE journal of biomedical and health informatics, vol. 24, no. 11, pp. 3326–3332, 2020. doi: 10.1109/JBHI.2020.2983035
    [303]
    T. Liu, et al, “The relationship between text message sentiment and self-reported depression,” Journal of affective disorders, vol. 302, pp. 7–14, 2022. doi: 10.1016/j.jad.2021.12.048
    [304]
    J. Meyerhoff, et al, “Analyzing text message linguistic features: Do people with depression communicate differently with their close and non-close contacts?,” Behaviour Research and Therapy, p. 104342, 2023.
    [305]
    G. Shen et al., “Depression detection via harvesting social media: A multimodal dictionary learning solution.” in IJCAI, 2017, pp. 3838–3844.
    [306]
    D. Li, et al, “Modeling spatiotemporal pattern of depressive symptoms caused by covid-19 using social media data mining,” Int. Journal of Environmental Research and Public Health, vol. 17, no. 14, p. 4988, 2020. doi: 10.3390/ijerph17144988
    [307]
    X. Hu et al., “Depression tendency detection model for weibo users based on bi-lstm,” in IEEE ICAICA, 2021, pp. 785–790.
    [308]
    H. Dinkel et al., “Text-based depression detection on sparse data,” arXiv preprint arXiv: 1904.05154, 2019.
    [309]
    H. A. Schwartz et al., “Towards assessing changes in degree of depression through facebook,” in Proc. the workshop on CLPsych: from linguistic signal to clinical reality, 2014, pp. 118–125.
    [310]
    T. Pedersen, “Screening twitter users for depression and ptsd with lexical decision lists,” in Proc. the 2nd workshop on CLPsych: from linguistic signal to clinical reality, 2015, pp. 46–53.
    [311]
    H. Almeida et al., “Detecting early risk of depression from social media user-generated content.” in CLEF (working notes), 2017.
    [312]
    C. Tung, et al, “Analyzing depression tendency of web posts using an event-driven depression tendency warning model,” Artificial Intelligence in Medicine, vol. 66, pp. 53–62, 2016. doi: 10.1016/j.artmed.2015.10.003
    [313]
    X. Wang et al., “A depression detection model based on sentiment analysis in micro-blog social network,” in Trends and Applications in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2013, pp. 201–213.
    [314]
    A. B. Shatte et al., “Social media markers to identify fathers at risk of postpartum depression,” 2019.
    [315]
    M. Islam, et al, “Depression detection from social network data using machine learning techniques,” Health information science and systems, vol. 6, no. 1, pp. 1–12, 2018. doi: 10.1007/s13755-017-0038-5
    [316]
    D. Mowery et al., “Feature studies to inform the classification of depressive symptoms from twitter data for population health,” arXiv preprint arXiv: 1701.08229, 2017.
    [317]
    E. Fast et al., “Empath: Understanding topic signals in large-scale text,” in Proc. the 2016 CHI Conf. on Human Factors in Computing Systems. Association for Computing Machinery, 2016, p. 4647–4657.
    [318]
    A. G. Reece, et al, “Instagram photos reveal predictive markers of depression,” EPJ Data Science, vol. 6, no. 1, p. 15, 2017. doi: 10.1140/epjds/s13688-017-0110-z
    [319]
    D. M. Blei et al., “Latent dirichlet allocation,” Journal of machine Learning research, vol. 3, no. Jan, pp. 993–1022, 2003.
    [320]
    P. Resnik et al., “Beyond lda: exploring supervised topic modeling for depression-related language in twitter,” in Proc. the 2nd workshop on CLPsych: from linguistic signal to clinical reality, 2015, pp. 99–107.
    [321]
    L. Mariñelarena-Dondena et al., “Predicting depression: a comparative study of machine learning approaches based on language usage,” 2017.
    [322]
    A. H. Orabi et al., “Deep learning for depression detection of twitter users,” in Proc. the Fifth Workshop on CLPsych: From Keyboard to Clinic, 2018, pp. 88–97.
    [323]
    F. Sadeque et al., “Uarizona at the clef erisk 2017 pilot task: linear and recurrent models for early depression detection,” in CEUR workshop proceedings, vol. 1866. NIH Public Access, 2017.
    [324]
    Z. Li, et al, “Mha: a multimodal hierarchical attention model for depression detection in social media,” Health information science and systems, vol. 11, no. 1, p. 6, 2023. doi: 10.1007/s13755-022-00197-5
    [325]
    H. S. Gavalan, M. N. Rastgoo, and B. Nakisa, “A bert-based summarization approach for depression detection,” arXiv preprint arXiv: 2409.08483, 2024.
    [326]
    J. Joshi, et al, “Multimodal assistive technologies for depression diagnosis and monitoring,” Journal on Multimodal User Interfaces, vol. 7, pp. 217–228, 2013. doi: 10.1007/s12193-013-0123-2
    [327]
    M. Bilalpur et al., “Multimodal feature selection for detecting mothers’ depression in dyadic interactions with their adolescent offspring,” in 2023 IEEE 17th Int. Conf. on Automatic Face and Gesture Recognition. IEEE, 2023, pp. 1–8.
    [328]
    Y. Gong et al., “Topic modeling based multi-modal depression detection,” in Proc. the 7th annual workshop on AVEC, 2017, pp. 69–76.
    [329]
    M. Niu et al., “Hcag: A hierarchical context-aware graph attention model for depression detection,” in ICASSP. IEEE, 2021, pp. 4235–4239.
    [330]
    M. Kächele, et al, “Fusion of audio-visual features using hierarchical classifier systems for the recognition of affective states and the state of depression,” depression, vol. 1, no. 1, pp. 671–678, 2014.
    [331]
    M. Niu et al., “Multimodal spatiotemporal representation for automatic depression level detection,” IEEE TAC, 2020.
    [332]
    H. Pérez Espinosa et al., “Fusing affective dimensions and audio-visual features from segmented video for depression recognition: Inaoe-buap’s participation at avec’14 challenge,” in Proc. the 4th Int. Workshop on AVEC, 2014, pp. 49–55.
    [333]
    L. Yang et al, “Dcnn and dnn based multi-modal depression recognition,” in ACII. IEEE, 2017, pp. 484–489.
    [334]
    L. Yang, et al, “Integrating deep and shallow models for multi-modal depression analysis—hybrid architectures,” IEEE TAC, vol. 12, no. 1, pp. 239–253, 2018.
    [335]
    S. Alghowinem, et al, “Interpretation of depression detection models via feature selection methods,” IEEE TAC, vol. 14, no. 1, pp. 133–152, 2020.
    [336]
    M. R. Morales et al., “Speech vs. text: A comparative analysis of features for depression detection systems,” in 2016 IEEE spoken language technology workshop. IEEE, 2016, pp. 136–143.
    [337]
    K. C. Fraser et al., “Detecting late-life depression in alzheimer’s disease through analysis of speech and language,” in Proc. the Third Workshop on CLPsych, 2016, pp. 1–11.
    [338]
    T. Al Hanai et al., “Detecting depression with audio/text sequence modeling of interviews.” in Interspeech, 2018, pp. 1716–1720.
    [339]
    C. Lau et al., “Improving depression assessment with multi-task learning from speech and text information,” in 2021 55th Asilomar Conf. on Signals, Systems, and Computers. IEEE, 2021, pp. 449–453.
    [340]
    G. Lam et al., “Context-aware deep learning for multi-modal depression detection,” in ICASSP. IEEE, 2019, pp. 3946–3950.
    [341]
    E. Toto et al., “Audibert: A deep transfer learning multimodal classification framework for depression screening,” 2021, pp. 4145–4154.
    [342]
    R. Flores et al., “Audiface: Multimodal deep learning for depression screening,” in Machine Learning for Healthcare Conf.. PMLR, 2022, pp. 609–630.
    [343]
    J. R. Williamson et al., “Detecting depression using vocal, facial and semantic communication cues,” in Proc. the 6th Int. Workshop on AVEC, 2016, pp. 11–18.
    [344]
    L. Yang et al., “Multimodal measurement of depression using deep learning models,” in Proc. the 7th annual workshop on AVEC, 2017, pp. 53–59.
    [345]
    S. Yin et al, “A multi-modal hierarchical recurrent neural network for depression detection,” in Proc. the 9th Int. on AVEC, 2019, pp. 65–71.
    [346]
    M. Morales et al., “A linguistically-informed fusion approach for multimodal depression detection,” in proceedings of the fifth workshop on computational linguistics and clinical psychology: from keyboard to clinic, 2018, pp. 13–24.
    [347]
    M. Rohanian et al., “Detecting depression with word-level multimodal fusion.” in INTERSPEECH, 2019, pp. 1443–1447.
    [348]
    S. A. Qureshi et al., “The verbal and non verbal signals of depression-combining acoustics, text and visuals for estimating depression level,” arXiv preprint arXiv: 1904.07656, 2019.
    [349]
    J. Gong, et al, “Common and distinct patterns of intrinsic brain activity alterations in major depression and bipolar disorder: voxel-based meta-analysis,” Translational psychiatry, vol. 10, no. 1, p. 353, 2020. doi: 10.1038/s41398-020-01036-5
    [350]
    N. Patil et al., “Depression and associated alzheimer’s disease,” 2021.
    [351]
    M. L. Radell, et al, “Depression in post-traumatic stress disorder,” Reviews in the Neurosciences, vol. 31, no. 7, pp. 703–722, 2020. doi: 10.1515/revneuro-2020-0006
    [352]
    N. J. Wiles, et al, “Physical activity and depression in adolescents: cross-sectional findings from the alspac cohort,” Social psychiatry and psychiatric epidemiology, vol. 47, pp. 1023–1033, 2012. doi: 10.1007/s00127-011-0422-4
    [353]
    T. Khumalo et al., “The relationship between locus of control and depression: A cross-sectional survey with university students in botswana,” South African Journal of Psychiatry, vol. 25, 2019.
    [354]
    K. Gbyl, et al, “Cortical thickness following electroconvulsive therapy in patients with depression: a longitudinal mri study,” Acta Psychiatrica Scandinavica, vol. 140, no. 3, pp. 205–216, 2019. doi: 10.1111/acps.13068
    [355]
    J. Joshi et al., “Relative body parts movement for automatic depression analysis,” in 2013 Humaine association conference on affective computing and intelligent interaction. IEEE, 2013, pp. 492–497.
    [356]
    S. Bhatia et al., “Automated measurement of head movement synchrony during dyadic depression severity interviews,” in 2019 14th IEEE Int. Conf. on Automatic Face & Gesture Recognition. IEEE, 2019, pp. 1–8.
    [357]
    J. Binnewies, et al, “Associations between depression, lifestyle and brain structure: A longitudinal mri study,” NeuroImage, vol. 231, p. 117834, 2021. doi: 10.1016/j.neuroimage.2021.117834
    [358]
    N. Cummins, et al, “A review of depression and suicide risk assessment using speech analysis,” Speech communication, vol. 71, pp. 10–49, 2015. doi: 10.1016/j.specom.2015.03.004
    [359]
    H. Kaya et al., “Predicting depression and emotions in the cross-roads of cultures, para-linguistics, and non-linguistics,” in Proc. the 9th Int. on AVEC, 2019, pp. 27–35.
    [360]
    B. Stasak, et al, “Breaking age barriers with automatic voice-based depression detection,” IEEE Pervasive Computing, vol. 21, no. 2, pp. 10–19, 2022. doi: 10.1109/MPRV.2022.3163656
    [361]
    M. Bilalpur et al., “Shap-based prediction of mother’s history of depression to understand the influence on child behavior,” in Proc. the 25th Int. Conf. on Multimodal Interaction, 2023, pp. 537–544.
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(4)  / Tables(7)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (6) PDF downloads(3) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return