He L, Chan J CW, Wang Z.Automatic depression recognition using CNN with attention mechanism from videos[J]. Neurocomputing, 2021, 422: 165-175.
[2]
McGrath J J, Al-Hamzawi A, Alonso J, et al. Age of onset and cumulative risk of mental disorders: a cross-national analysis of population surveys from 29 countries[J]. Lancet Psychiatry, 2023, 10(9): 668-681.
[3]
Huang Y, Wang Y, Wang H, et al. Prevalence of mental disorders in China: a cross-sectional epidemiological study[J]. Lancet Psychiatry, 2019, 6(3): 211-224.
[4]
Kong X, Yao Y, Wang C, et al. Automatic identification of depression using facial images with deep convolutional neural network[J]. Med Sci Monit, 2022, 28: e936409.
[5]
Hepdurgun C.The present and future of artificial intelligence applications in psychiatry[J]. Noro Psikiyatr Ars, 2024, 61(1): 1-2.
[6]
Gao S, Calhoun V D, Sui J.Machine learning in major depression: from classification to treatment outcome prediction[J]. CNS Neurosci Ther, 2018, 24(11): 1037-1052.
[7]
Uddin M A, Joolee J B, Sohn K-A.Deep multi-modal network based automated depression severity estimation[J]. IEEE T Affect Comput,2023, 14(3): 2153-2167.
[8]
Mao K, Wu Y, Chen J.A systematic review on automated clinical depression diagnosis[J]. Npj Ment Health Res, 2023, 2(1): 20.
[9]
Pampouchidou A, Pediaditis M, Kazantzaki E, et al. Automated facial video-based recognition of depression and anxiety symptom severity: cross-corpus validation[J]. Mach Vision Appl, 2020, 31.
[10]
Kupfer D J, Frank E, Phillips M L.Major depressive disorder: new clinical, neurobiological, and treatment perspectives[J]. Focus (Am Psychiatr Publ), 2016, 14(2): 266-276.
[11]
Schwartz G E, Fair P L, Salt P, et al. Facial muscle patterning to affective imagery in depressed and nondepressed subjects[J]. Science, 1976, 192(4238): 489-491.
[12]
魏巍. 基于面部特征的抑郁症识别研究[D]. 兰州: 兰州大学, 2021.
[13]
Zhou X, Jin K, Shang Y, et al. Visually interpretable representation learning for depression recognition from facial images[J]. IEEE T Affect Comput , 2020, 11(3): 542-552.
[14]
Li X, Yi X, Ye J, et al. SFTNet: a microexpression-based method for depression detection[J]. Comput Methods Programs Biomed, 2024, 243: 107923.
[15]
Mahayossanunt Y, Nupairoj N, Hemrungrojn S, et al. Explainable depression detection based on facial expression using LSTM on attentional intermediate feature fusion with label smoothing[J]. Sensors (Basel), 2023, 23(23): 9402.
[16]
郭威彤. 利用深度学习从面部表情和语音识别抑郁症方法的研究[D]. 兰州: 兰州大学, 2023.
[17]
Lee T-Y, Li C-C, Chou K-R, et al. Machine learning-based speech recognition system for nursing documentation - a pilot study[J]. Int J Med Inform, 2023, 178: 105213.
[18]
Choi R Y, Coyner A S, Kalpathy-Cramer J, et al. Introduction to machine learning, neural networks, and deep learning[J]. Transl Vis Sci Technol, 2020, 9(2): 14.
[19]
Tadalagi M.AutoDep: automatic depression detection using facial expressions based on linear binary pattern descriptor[J]. Med Biol Eng Comput, 2021,59(6):1339-1354.
[20]
Ma Y, Shen J, Zhao Z, et al. What can facial movements reveal? Depression recognition and analysis based on optical flow using bayesian networks[J]. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 3459-3468.
[21]
Li J, Liu Z, Ding Z, et al. A novel study for MDD detection through task-elicited facial cues[C]//2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
[22]
Jiang Z, Harati S, Crowell A, et al. Classifying major depressive disorder and response to deep brain stimulation over time by analyzing facial expressions[J]. IEEE Trans Biomed Eng, 2021, 68(2): 664-672.
[23]
Lee Y-S, Park W-H.Diagnosis of depressive disorder model on facial expression based on fast R-CNN[J]. Diagnostics (Basel), 2022, 12(2): 317.
[24]
De Melo W C, Granger E, López M B. MDN: a deep maximization-differentiation network for spatio-temporal depression detection[J]. IEEE T Affect Comput , 2023, 14(1): 578-590.
[25]
De Melo W C, Granger E, Hadid A. Depression detection based on deep distribution learning[C]//2019 IEEE International Conference on Image Processing (ICIP).
Al Jazaery M, Guo G.Video-based depression level analysis by encoding deep spatiotemporal features[J]. IEEE T Affect Comput , 2021, 12(1): 262-268.
[28]
Wang Y, Ma J, Hao B, et al. Automatic depression detection via facial expressions using multiple instance learning[C]//2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).
[29]
Guo W, Yang H, Liu Z, et al. Deep neural networks for depression recognition based on 2D and 3D facial expressions under emotional stimulus tasks[J]. Front Neurosci, 2021, 15: 609760.
[30]
He L, Jiang D, Sahli H.Automatic depression analysis using dynamic facial appearance descriptor and dirichlet process fisher encoding[J]. IEEE Trans Mul, 2019, 21(6): 1476-1486.
[31]
Xu N, Huo H, Xu J, et al. Automatic diagnosis of depression based on attention mechanism and feature pyramid model[J]. PLoS One, 2024, 19(3): e0295051.
[32]
Ekman P, Friesen W V.Facial action coding system (FACS): a technique for the measurement of facial actions[J]. Rivista Di Psichiatria, 1978, 47(2): 126-38.
[33]
Girard J M, Cohn J F, Mahoor M H, et al. Nonverbal social withdrawal in depression: evidence from manual and automatic analysis[J]. Image Vis Comput, 2014, 32(10): 641-647.
[34]
Hu B, Tao Y, Yang M.Detecting depression based on facial cues elicited by emotional stimuli in video[J]. Comput Biol Med, 2023, 165: 107457.
[35]
Liu X, Wang X.Automatic identification of a depressive state in primary care[J]. Healthcare (Basel), 2022, 10(12): 2347.
[36]
Wang Q.Facial expression video analysis for depression detection in chinese patients[J]. J Vis Commun Image R, 2018.
[37]
Sumali B, Mitsukura Y, Tazawa Y, et al. Facial landmark activity features for depression screening[C]//2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE).
[38]
Chen X, Luo T.Catching elusive depression via facial micro-expression recognition[J]. IEEE Commun Mag , 2023, 61(10): 30-36.
[39]
Bengio Y, Courville A, Vincent P.Representation learning: a review and new perspectives[J]. IEEE Trans Pattern Anal Mach Intell, 2013, 35(8): 1798-1828.
[40]
Li Y, Liu Z, Zhou L, et al. A facial depression recognition method based on hybrid multi-head cross attention network[J]. Front Neurosci, 2023, 17: 1188434.
[41]
Li X, Yi X, Lu L, et al. TSFFM: depression detection based on latent association of facial and body expressions[J]. Comput Biol Med, 2024, 168: 107805.
[42]
Zhu D, Wang Y, Wang H, et al. Assessment of eye tracking and facial expression as a model for depression identification: a preliminary study[J]. Gen Hosp Psychiatry, 2024, 87: 144-145.
[43]
Niu M, Tao J, Liu B, ,et al. Multimodal spatiotemporal representation for automatic depression level detection[J]. IEEE T Affect Comput. Multimodal spatiotemporal representation for automatic depression level detection[J]. IEEE T Affect Comput, 2020, PP(99): 1-1.
[44]
Liu Z, Yuan X, Li Y, et al. PRA-Net: part-and-relation attention network for depression recognition from facial expression[J]. Comput Biol Med, 2023, 157: 106589.
[45]
Shangguan Z, Liu Z, Li G, et al. Dual-stream multiple instance learning for depression detection with facial expression videos[J]. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 554-563.
[46]
Kwon N, Kim S.Depression severity detection using read speech with a divide-and-conquer approach[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2021, 2021: 633-637.
[47]
Khan S, Umar Saeed S M, Frnda J, et al. A machine learning based depression screening framework using temporal domain features of the electroencephalography signals[J]. PLoS One, 2024, 19(3): e0299127.
[48]
Byun S, Kim A Y, Jang E H, et al. Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol[J]. Comput Biol Med, 2019, 112: 103381.