Pharris A B, Munoz R T, Kratz J, et al. Hope as a buffer to suicide attempts among adolescents with depression[J]. J Sch Health, 2023, 93(6): 494-499.
[2]
Reddy M S. Depression - the global crisis[J]. Indian J Psychol Med, 2012, 34(3): 201-203.
[3]
Fedko I O, Hottenga J-J, Helmer Q, et al. Measurement and genetic architecture of lifetime depression in the netherlands as assessed by LIDAS (Lifetime Depression Assessment Self-report)[J]. Psychol Med, 2020, 51(8): 1-10.
[4]
Huang Y, Wang Y, Wang H, et al. Prevalence of mental disorders in China: a cross-sectional epidemiological study[J]. Lancet Psychiat, 2019, 6(3): 211-224.
Saunders E F H, Mukherjee D, Waschbusch D A, et al. Predictors of diagnostic delay: assessment of psychiatric disorders in the clinic[J]. Depress Anxiety, 2021, 38(5): 545-553.
[7]
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.
[8]
Koops S, Brederoo S G, Boer J N, et al. Speech as a biomarker for depression[J]. CNSNDDT, 2023, 22(2): 152-160.
[9]
Cummins N, Scherer S, Krajewski J, et al. A review of depression and suicide risk assessment using speech analysis[J]. Speech Commun, 2015, 71: 10-49.
[10]
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.
[11]
Sumali B, Mitsukura Y, Liang K C, et al. Speech quality feature analysis for classification of depression and dementia patients[J]. Sensors (Basel), 2020, 20(12): 3599.
[12]
Rejaibi E, Komaty A, Meriaudeau F, et al. MFCC-based recurrent neural network for automatic clinical depression recognition and assessment from speech[J]. Biomed Signal Proces, 2022, 71: 103107.
[13]
Zhang X, Shen J, Din Z U, et al. Multimodal depression detection: fusion of electroencephalography and paralinguistic behaviors using a novel strategy for classifier ensemble[J]. IEEE J Biomed Health Inform, 2019, 23(6): 2265-2275.
[14]
Di Y, Wang J, Li W, et al. Using i-vectors from voice features to identify major depressive disorder[J]. J Affect Disord, 2021, 288: 161-166.
[15]
Zhang D, Liu X, Xu L, et al. Effective differentiation between depressed patients and controls using discriminative eye movement features[J]. J Affect Disord, 2022, 307: 237-243.
[16]
Alghowinem S, Goecke R, Wagner M, et al. Eye movement analysis for depression detection[J]. 2013 IEEE International Conference on Image Processing, 2013: 4220-4224.
[17]
赵 菊. 基于眼动信息的抑郁症识别研究[D].济南: 齐鲁工业大学, 2024.
[18]
Wang B, Kang Y, Huo D, et al. Depression signal correlation identification from different EEG channels based on CNN feature extraction[J]. Psychiat Res Neuroim, 2023, 328: 111582.
[19]
Cai H, Qu Z, Li Z, et al. Feature-level fusion approaches based on multimodal EEG data for depression recognition[J]. Inform Fusion, 2020, 59: 127-138.
[20]
Lan Y T, Peng D, Liu W, et al. Investigating emotion EEG patterns for depression detection with attentive simple graph convolutional network[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2023, 2023: 1-4.
[21]
Pampouchidou A, Simantiraki O, Vazakopoulou C-M, et al. Facial geometry and speech analysis for depression detection[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2017, 2017: 1433-1436.
[22]
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.
[23]
Bhatia S, Hayat M, Breakspear M, et al. A video-based facial behaviour analysis approach to melancholia[C]. 2017 12th IEEE International Conference on Automatic Face Gesture Recognition (FG 2017), 2017: 754-761.
[24]
Zhou X, Jin K, Shang Y, et al. Visually interpretable representation learning for depression recognition from facial images[J]. IEEE Trans Affective Comput, 2020, 11(3): 542-552.
[25]
Dai L, Zhou H, Xu X, et al. Brain structural and functional changes in patients with major depressive disorder: a literature review[J]. Peer J, 2019, 7: e8170.
[26]
Ramasubbu R, Brown E C, Marcil L D, et al. Automatic classification of major depression disorder using arterial spin labeling MRI perfusion measurements[J]. Psychiat Clin Neurosci, 2019, 73(8): 486-493.
[27]
Shimizu Y, Yoshimoto J, Toki S, et al. Toward probabilistic diagnosis and understanding of depression based on functional MRI data analysis with logistic group LASSO[J]. PLoS One, 2015, 10(5): e0123524.
[28]
Yamashita A, Sakai Y, Yamada T, et al. Generalizable brain network markers of major depressive disorder across multiple imaging sites[J]. PLoS Biol, 2020, 18(12): e3000966.
[29]
Guo H, Qin M, Chen J, et al. Machine-learning classifier for patients with major depressive disorder: multifeature approach based on a high-order minimum spanning tree functional brain network[J]. Comput Math Methods Med, 2017, 2017: 4820935.
[30]
Schnyer D M, Clasen P C, Gonzalez C, et al. Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder[J]. Psychiat Res Neuroim, 2017, 264: 1-9.
[31]
Kambeitz J, Cabral C, Sacchet M D, et al. Detecting neuroimaging biomarkers for depression: a meta-analysis of multivariate pattern recognition studies[J]. Biol Psychiat, 2017, 82(5): 330-338.
[32]
Alghowinem S, Goecke R, Wagner M, et al. Multimodal depression detection: fusion analysis of paralinguistic, head pose and eye gaze behaviors[J]. IEEE Trans Affective Comput, 2018, 9(4): 478-490.
Kim S S, Gil M, Min E J. Machine learning models for predicting depression in Korean young employees[J]. Front Public Health, 2023, 11: 1201054.
[35]
Chiong R, Budhi G S, Dhakal S, et al. A textual-based featuring approach for depression detection using machine learning classifiers and social media texts[J]. Comput Biol Med, 2021, 135: 104499.
[36]
Abdulla H, Maalouf M, Jelinek H F. Machine learning for the prediction of depression progression from inflammation markers[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2023, 2023: 1-4.
Shao W, You Z, Liang L, et al. A multi-modal gait analysis-based detection system of the risk of depression[J]. IEEE J Biomed Health Inform, 2022, 26(10): 4859-4868.
[41]
沈 骥. 基于眼动、脑电特征的抑郁识别研究[D]. 兰州: 兰州大学, 2016.
[42]
Acharya U R, Oh S L, Hagiwara Y, et al. Automated EEG-based screening of depression using deep convolutional neural network[J]. Comput Methods Programs Biomed, 2018, 161: 103-113.
[43]
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.
[44]
Lee Y, Ragguett R-M, Mansur R B, et al. Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review[J]. J Affect Disord, 2018, 241: 519-532.
[45]
李建秀. 基于抑郁症患者ERP数据的源定位及分类算法研究[D]. 兰州: 兰州大学, 2018.
[46]
Astafeva D, Gayduk A, Tavormina G, et al. Neuronetwork approach in the early diagnosis of depression[J]. Psychiatr Danub, 2023, 35(Suppl 2): 77-85.
[47]
Li X, La R, Wang Y, et al. EEG-based mild depression recognition using convolutional neural network[J]. Med Biol Eng Comput, 2019, 57(6): 1341-1352.
[48]
Lundervold A S, Lundervold A. An overview of deep learning in medical imaging focusing on MRI[J]. Z Med Phys, 2019, 29(2): 102-127.
[49]
Kolossváry M, Cecco C N, Feuchtner G, et al. Advanced atherosclerosis imaging by CT: radiomics, machine learning and deep learning[J]. J Cardiovasc Comput Tomogr, 2019, 13(5): 274-280.
2024, Vol. 35 
