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Comprehensive review of depression detection techniques based on machine learning approach

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Abstract

Depression has become a serious disease that affects people's mental state and is an important part of the global disease burden. Research in this area began later in 1920 and has steadily increased due to the pandemic. Many studies on depression have been conducted worldwide. Still, obtaining comparable data for physiological and biological detection techniques, existing datasets, acquisition, and data classification methods in one platform is challenging. In addition, clinical methods using screening instruments, questionnaires, and episodic examinations to determine depression severity are time-consuming. Therefore, an alternative approach is to incorporate assessment into a person's daily activities in their environment or clinic, preferably via sensor technologies with smart systems. Recently, much research has been conducted on machine learning methods that can automatically decode mental and cognitive states to improve efficiency, accuracy, and precision. In this proposed review, depression detection methods based on electrical and acoustic signals and verbal and nonverbal communication are described in detail and then organized for practical/commercial applications. This paper also reviews the potential and challenges of various depression detection methods to serve as a suitable reference for upcoming researchers.

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References

  • Abdullah S, Choudhury T (2018) Sensing technologies for monitoring serious mental illnesses. IEEE Multimed 25:61–75

    Google Scholar 

  • Abel LA, Friedman L, Jesberger J, Malki A, Meltzer HY (1991) Quantitative assessment of smooth pursuit gain and catch-up saccades in schizophrenia and affective disorders. Biol Psychiat 29(11):1063–1072

    Google Scholar 

  • Acharya UR, Sudarshan VK, Adeli H, Santhosh J, Koh J (2015) Computer-aided diagnosis of depression using EEG signals. Eur Neurol 73:329–336

    Google Scholar 

  • Adigun OT (2017) Depression and individuals with hearing loss: a systematic review. J Psychol Psychother 7:5

    Google Scholar 

  • Ahmad Wani M, Elaffendi MA, Shakil KA, Shariq Imran A (2023) Depression screening in humans with AI and deep learning techniques. IEEE Trans Comput Soc Syst 10(4):2074–2089

    Google Scholar 

  • Ahmed A, Aziz S, Toro CT, Alzubaidi M, Irshaidat S, Serhan HA, Abd-alrazaq AA, Househ M (2022) Machine learning models to detect anxiety and depression through social media: a scoping review. Comput Methods Progr Biomed 2:2

    Google Scholar 

  • Aleem S, Huda N, Amin R, Khalid S, Alshamrani SS, Alshehri A (2022) Machine learning algorithms for depression: diagnosis, insights, and research directions. Electronics 11:2

    Google Scholar 

  • Alghowinem S, Goecke R, Wagner M, Parker G, Breakspear M (2013b) Eye movement analysis for depression detection. IEEE Int Conf Image Process 2:4220–4224

    Google Scholar 

  • Alghowinem S, Goecke R, Wagner M, Epps J, Hyett M, Parker G, Breakspear M (2018) Multimodal depression detection: fusion analysis of paralinguistic, head pose and eye gaze behaviours. IEEE Trans Affect Comput 9:4

    Google Scholar 

  • Alghowinem S, Goecke R, Wagner M, Parkerx G, Breakspear M (2013) Head pose and movement analysis as an indicator of depression. In: Affective computing and intelligent interaction (ACII), Humaine Association Conference, pp. 283–288

  • Allahyari E (2019) Predicting elderly depression: an artificial neural network model. Iran J Psychiatry Behav Sci 13:4

    MathSciNet  Google Scholar 

  • Al-Rahayfeh A, Faezipour M (2013) Eye tracking and head movement detection: a state-of-art survey. IEEE J Transl Eng Health Med 1:2100212

    Google Scholar 

  • Anticevic A, Murray JD (2017) Computational psychiatry: mathematical modelling of mental illness. Academic Press, New York

    Google Scholar 

  • Armstrong T, Olatunji BO (2012) Eye tracking of attention in the affective disorders: a metanalytic review and synthesis. Clin Psychol Rev 32(8):704–723

    Google Scholar 

  • Arndt JE, Newman KR, Sears CR (2014) An eye tracking study of the time course of attention to positive and negative images in dysphoric and nondysphoric individuals. J Exp Psychopathol 5(4):399–413

    Google Scholar 

  • Aslam SM, Jilani AK, Sultana J, Almutairi L (2021) Feature evaluation of emerging e-learning systems using machine learning: an extensive survey. IEEE Access 9:69573–69587

    Google Scholar 

  • Avots E, Jermakovs K, Bachmann M, Päeske L, Ozcinar C, Anbarjafari G (2022) Ensemble approach for detection of depression using EEG features. Entropy 24:211

    Google Scholar 

  • Ay B, Yildirim O, Talo M et al (2019) Automated depression detection using deep representation and sequence learning with EEG signals. J Med Syst 43:205

    Google Scholar 

  • Bachmann M, Lass J, Suhhova A, Hinrikus H (2013) Spectral asymmetry and Higuchi’s fractal dimension measures of depression electroencephalogram. Comput Math Methods Med 2:251638

    MathSciNet  Google Scholar 

  • Bassett D (2016) A literature review of heart rate variability in depressive and bipolar disorders. Aust N Z J Psychiatry 50(6):511–519

    MathSciNet  Google Scholar 

  • Bates M (2018) Gut feeling. IEEE Pulse 2:15–18

    Google Scholar 

  • Beak JW, Chung K (2020) Context deep neural network model for predicting depression risk using multiple regression special selection on machine learning designs, implementations and techniques. IEEE Acess 8:18171–18181

    Google Scholar 

  • Bernabei V, Morini V, Moretti F, Marchiori A, Ferrari B (2011) Vision and hearing impairments are associated with depressive–anxiety syndrome in Italian elderly. Aging Ment Health 15:467–474

    Google Scholar 

  • Bhatt JM, Bhattacharyya N, Lin HW (2017) Relationships between tinnitus and the prevalence of anxiety and depression. Laryngoscope 127(2):466–469

    Google Scholar 

  • Boettger S, Hoyer D, Falkenhahn K, Kaatz M, Yeragani VK, Bär KJ (2016) Altered diurnal autonomic variation and reduced vagal information flow in acute schizophrenia. Clin Neurophysiol 117:2715–2722

    Google Scholar 

  • Cai H, Han J, Chen Y, Sha X, Wang Z, Hu B, Yang J (2018) A pervasive approach to EEG-based depression detection. Complexity 523:8028

    Google Scholar 

  • Carvalho N, Noiret N, Vandel P, Monnin J, Chopard G, Laurent E (2014) Saccadic eye movements in depressed elderly patients. PLoS ONE 9(8):e105355

    Google Scholar 

  • Chattopadhyay S (2017) A neuro-fuzzy approach for the diagnosis of depression. Appl Comput Inform 13:10–18

    Google Scholar 

  • Chien-Te Wu, Dillon DG, Hsu H-C, Huang S, Barrick E, Liu Y-H (2018) Depression detection using relative EEG power induced by emotionally positive images and a conformal kernel support vector machine. Appl Sci 8(8):1244

    Google Scholar 

  • Chin-Teng L, Lun-De L, Yu-Hang L, Wang IJ, Bor-Shyh L, Jyh-Yeong C (2011) Novel dry polymer foam electrodes for long-term EEG measurement. IEEE Trans Biomed Eng 58(5):1200–1207

    Google Scholar 

  • Chopra S, Kaur H, Pandey RM, Nehra A (2018) Development of neuropsychological evaluation screening tool: an education-free cognitive screening instrument. 66:391–399

  • Cohen H, Kotler M, Matar M, Kaplan Z, Loewenthal U, Miodownik H (2006) Analysis of heart rate variability in posttraumatic stress disorder patients in response to a trauma-related reminder. Biol Psychiatry 44:1054–1059

    Google Scholar 

  • Cohn JF, Kruez TS, Matthews I, Yang Y, Nguyen MH, Padilla MT, Zhou F, De la Torre F (2009) Detecting depression from facial actions and vocal prosody. 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops

  • Crawford B (1936) The dependence of pupil size upon external light stimulus under static and variable conditions. Proc R Soc Lond B Biol Sci 121(823):376–395

    Google Scholar 

  • Cronly J, Duff AJ, Riekert KA, Perry IJ, Fitzgerald AP, Horgan A, Lehane E, Howe B, Ni Chroinin M, Savage E (2018) Online versus paper-based screening for depression and anxiety in adults with cystic fibrosis in Ireland: a cross-sectional exploratory study. BMJ Open 21:8

    Google Scholar 

  • Dash S, Clarke G, Berk M, Jacka FN (2015) The gut microbiome and diet in psychiatry: focus on depression. Curr Opin Psychiatry 28(1):1–6

    Google Scholar 

  • Dawes P, Emsley R, Cruickshanks KJ, Moore DR, Fortnum H, Edmondson-Jones M, McCormack A, Munro KJ (2015) Hearing loss and cognition: the role of hearing aids, social isolation and depression. PLoS ONE 10:2

    Google Scholar 

  • Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 9:21

    Google Scholar 

  • Dev A, Roy N, Islam MDK, Biswas C, Ahmed HU, Amin MDA, Sarkar F, Vaidyanathan R, Mamun KA (2022) Exploration of EEG-based depression biomarkers identification techniques and their applications: a systematic review. IEEE Access 2:2

    Google Scholar 

  • Dibeklioğlu H, Hammal Z, Cohn JF (2018) Dynamic multimodal measurement of depression severity using deep autoencoding. IEEE J Biomed Health Inform 22(2):525–536

    Google Scholar 

  • Dibeklioğlu H, Hammal Z, Yang Y, Cohn JF (2015) Multimodal detection of depression in clinical interviews. ACM International Conference Multimodal Interaction, pp. 307–310

  • Ding Y, Chen X, Fu Q, Zhong S (2020) A depression recognition method for college students using deep integrated support vector algorithm. IEEE Access 8:75616–75629

    Google Scholar 

  • Dwyer DB, Falkai P, Koutsouleris N (2018) Machine learning approaches for clinical psychology and psychiatry. Annu Rev Clin Psychol 14:91–118

    Google Scholar 

  • Ekman P, Friesen WV (1978) Facial action coding system. Consulting Psychol Press, Palo Alto

    Google Scholar 

  • Elena B, Joana J, Maite G (2019) Child and adolescent depression: a review of theories, evaluation instruments, prevention programs, and treatments. Front Psychol 10:543

    Google Scholar 

  • Evrensel A, Ceylan ME (2015) The gut-brain axis: the missing link in depression. Clin Psychopharmacol Neurosci 13(3):239–244

    Google Scholar 

  • Fei Y, Arne B, Egon S (2010) Wireless medical sensor measurements of fatigue in patients with multiple sclerosis. 32nd Annual International Conference of the IEEE EMBS Buenos Aires, pp. 3763–3767

  • Fountoulakis KN, Fotiou F, Iacovides A (2005) Is there a dysfunction in the visual system of depressed patients? Ann Gen Psychiatry 4:7

    Google Scholar 

  • France DJ, Shiavi RG, Stephen S, Marilyn S, Mitchell Wilkes D (2000) Acoustical properties of speech as indicators of depression and suicidal risk. IEEE Trans Biomed Eng 47(7):829–837

    Google Scholar 

  • Funke G, Eric G, Martha C, Allen D, Rebecca B, Lauren M (2016) Which eye tracker is right for your research? Performance evaluation of several cost variant eye trackers. Proc Hum Fact Ergon Soc Annu Meet 2:1239–1243

    Google Scholar 

  • Geng X-F, Jun-Hai X (2017) Application of autoencoder in depression diagnosis. 3rd International Conference on Computer Science and Mechanical Automation (CSMA 2017), PP.146–151

  • Giannakakis G, Manousos D, Chaniotakis V, Tsiknakis M (2018) Evaluation of head pose features for stress detection and classification. IEEE EMBS Int Conf Biomed Health Inform 2:2

    Google Scholar 

  • Giannakakis G, Grigoriadis D, Giannakaki K, Simantiraki O, Roniotis A, Tsiknakis M (2019) Review on psychological stress detection using bio signals. IEEE Trans Affect Comput 99:1–1

    Google Scholar 

  • Girard JM, Cohn JF, Mahoor MH, Mavadati S, Rosenwald DP (2013) Social risk and depression: evidence from manual and automatic facial expression analysis. In 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition

  • Gorman JM, Sloan RP (2000) Heart rate variability in depressive and anxiety disorders. Am Heart J 140:77–83

    Google Scholar 

  • Gotlib IH, Krasnoperova E, Yue DN, Joormann J (2004) Attentional biases for negative interpersonal stimuli in clinical depression. J Abnorm Psychol 113(1):127–135

    Google Scholar 

  • Gwak M, Sarrafzadeh M, Woo E (2018) Support for a clinical diagnosis of mild cognitive impairment using photoplethysmography and gait sensors. APSIPA Proceedings of Annual Summit and Conference

  • Hasib KM, Islam MR, Sakib S, Akbar MA, Razzak I, Alam MS (2023) Depression detection from social networks data based on machine learning and deep learning techniques: an interrogative survey. IEEE Trans Comput Soc Syst 10(4):1568–1586

    Google Scholar 

  • Heesterbeek TJ, Van der Aa HPA, Van Rens GHMB, Twisk JWR, Van Nispen RMA (2017) The incidence and predictors of depressive and anxiety symptoms in older adults with vision impairment: a longitudinal prospective cohort study. Ophthalm Physiol Opt. 2:2

    Google Scholar 

  • Hinrikus H, Suhhova A, Bachmann M, Aadamsoo K, Vohma U, Pehlak H, Lass J (2010) Spectral features of EEG in depression. Biomed Eng 55:155–161

    Google Scholar 

  • Hosseinifard B, Moradi MH, Rostami R (2013) Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal. Comput Methods Progr Biomed. 109:339–345

    Google Scholar 

  • Houston TK, Cooper LA, Vu HT et al (2001) Screening the public for depression through the internet. Psychiatr Serv 52:362–367

    Google Scholar 

  • https://www.who.int/news-room/fact-sheets/detail/mental-disorders.

  • Hwang B, Ryu JW, Park C, Zhang B (2017) A novel method to monitor human stress states using ultra-short-term ECG spectral feature. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), PP. 2381–2384

  • Jacobson E (1938) Progressive relaxation. University of Chicago Press, Chicago

    Google Scholar 

  • Jan A, Meng H, Falinie Y, Gaus BA, Zhang F (2018) Artificial intelligent system for automatic depression level analysis through visual and vocal expressions. IEEE Trans Cognit Dev Syst 10(3):668–680

    Google Scholar 

  • Janet SC, Betz J, Li L, Blake CR, Sung YK, Contrera KJ, Lin FR (2016) Association of using hearing aids or cochlear implants with change s in depressive symptoms in older adults. J Psychol Psychother 142:652–657

    Google Scholar 

  • Jeni LA, Cohn JF, Kanade T (2017) Dense 3D face alignment from 2D videos for real-time use. Image vis Comput 58:13–24

    Google Scholar 

  • Jiang H, Bin Hu, Liu Z, Wang G, Zhang L, Li X, Kang H (2018) Detecting depression using an ensemble logistic regression model based on multiple speech features. Comput Math Methods Med 2:2

    Google Scholar 

  • Joormann J, Gotlib IH (2007) Selective attention to emotional faces following recovery from depression. J Abnorm Psychol 116(1):80–85

    Google Scholar 

  • Kacem A, Hammal Z, Daoudi M, Cohn J (2018) Detecting depression severity by interpretable representations of motion dynamics. 13th IEEE International Conference on Automatic Face & Gesture Recognition

  • Khandoker AH, Luthra V, Abouallaban Y, Saha S, Ahmed KI, Mostafa R, Chowdhury N, Jelinek HF (2016) Identifying depressed patients with and without suicidal ideation by finger photo-plethysmography. 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1842–1845.

  • Knott V, Mahoney C, Kennedy S, Evans K (2001) EEG power, frequency, asymmetry and coherence in male depression. Psychiatry 106:123–140

    Google Scholar 

  • Kroenke K, Spitzer RL, Williams JB (2001) The PHQ-9: validity of a brief depression measure. J Gen Intern Med 16:603–613

    Google Scholar 

  • Kyung Ray M, Subin P, YouJi J, AhReum L, Jung Hyun L (2018) Effects of anxiety sensitivity and hearing loss on tinnitus symptom severity. Psychiatry Investig 15(1):34–40

    Google Scholar 

  • Le Y, Jiang D, He L, Pei E, Oveneke MC, Sahli H (2016) Decision tree based depression classification from audio video and language information. AVEC '16: Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge, pp 89–96

  • Lee J-S, Yang B-H, Lee J-H, Choi J-H, Choi I-G, Kim S-B (2007) Detrended fluctuation analysis of resting EEG in depressed outpatients and healthy controls. Clin Neurophysiol 118:2489–2496

    Google Scholar 

  • Li X, Bin Hu, Shen Ji, Tingting Xu, Retcliffe M (2015) Mild depression detection of college students: an EEG-based solution with free viewing tasks. J Med Sci 39:187

    Google Scholar 

  • Li Yu, Yangyang Xu, Xia M, Zhang T, Junjie Wang Xu, Liu Y, Wang (2016) Eye movement indices in the study of depressive disorder. Shanghai Arch Psychiatry 28:6

    Google Scholar 

  • Li X, Cao T, Sun S, Hu B, Ratcliffe M (2016) Classification study on eye movement data: towards a new approach in depression detection. IEEE Congr Evol Comput 2:1227–1232

    Google Scholar 

  • Li M, Lu S, Feng L, Fu B, Wang G, Zhon N, Hu B (2016) Attentional bias in remitted depressed patients: evidence from an eyetracking study. J Psychiatry 19:5

    Google Scholar 

  • Li Y, Bin H, Zhengi X, Li X (2019) EEG-based mild depressive detection using differential evolution. IEEE Access 7:7814–7822

    Google Scholar 

  • Li Y, Fan F (2005) Classification of schizophrenia and depression by EEG with ANNs. Proceedings of IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai

  • Li Y, Li Y, Tong S, Tang Y, Zhu Y (2007) More normal EEGs of depression patients during mental arithmetic than rest. 165–168

  • Liao S-C, Chien-Te Wu, Huang H-C, Cheng W-T, Liu Y-H (2017) Major depression detection from EEG signals using kernel eigen-filter-bank common spatial patterns. Sensors 17:1385

    Google Scholar 

  • Lin CT, Liao LD, Liu YH, Wang IJ, Lin BS, Chang JY (2011) Novel dry polymer foam electrodes for long-term EEG measurement. IEEE Trans Biomed Eng 58(5):1200–1207

    Google Scholar 

  • Lin C-T, Chun-Hsiang C, Ehong C, Singh AK, Hung C-S, Yi-Hsin Yu, Nascimber M, Liu Y-T, King J-T, Tung-Ping Su, Wang S-J (2017) Forehead EEG in support of future feasible personal healthcare solutions: sleep management, headache prevention, and depression treatment. IEEE Access 2:10612–10621

    Google Scholar 

  • Lin L, Xuri C, Ying S, Lin Z (2020) Towards automatic depression detection: a BiLSTM/1D CNN-based model. Appl Sci 10:8701

    Google Scholar 

  • Lipton RB, Levin S, Holzman PS (1980) Horizontal and vertical pursuit eye movements, the oculocephalic reflex, and the functional psychoses. Psychiatry Res 3(2):193–203

    Google Scholar 

  • Liu Q, He H, Yang J, Feng X, Zhao F, Lyu J (2019) Changes in the global burden of depression from 1990 to 2017: findings from the Global burden of disease study. J Psychiatr Res 2:2

    Google Scholar 

  • Liu D, Feng XL, Ahmed F, Shahid M (2022a) Detecting and measuring depression on social media using a machine learning approach: systematic review. JMIR Ment Health 2:2

    Google Scholar 

  • Liu Y, Pu C, Xia S, Deng D, Wang X, Li M (2022b) Machine learning approaches for diagnosing depression using EEG: a review. Transl Neurosci 13(1):224–235

    Google Scholar 

  • Lopez-Otero P, Magariños C, Docio-Fernandez L, Rodriguez-Banga E, Erro D, Garcia-Mateo C (2017) Influence of speaker de-identification in depression detection. IET Signal Process 11(9):1023–1030

    Google Scholar 

  • Low LSA, Maddage NC, Lech M, Sheeber LB, Allen NB (2011) Detection of clinical depression in adolescents speech during family interactions. IEEE Trans Biomed Eng 58(3):574–586

    Google Scholar 

  • Millán JDR, Mouriño J, Franzé M (2002) A local neural classifier for the recognition of EEG patterns associated to mental tasks. IEEE Trans Neural Netw Learn Syst 13(3):678–686

    Google Scholar 

  • Moore E, Clements M, Peifer J, Weisser L (2004) Comparing objective feature statistics of speech for classifying clinical depression. In: Proc Annual International Conference IEEE Engineering in Medicine and Biology Society 1: 17–20

  • Munkler P, Rothkirch M, Dalati Y, Schmack K, Sterzer P (2015) Biased recognition of facial affect in patients with major depressive disorder reflects clinical state. PLoS ONE 10:6

    Google Scholar 

  • Ngampramuan S, Tungtong P, Mukda S, Jariyavilas A, Sakulisariyaporn C (2018) Evaluation of autonomic nervous system, saliva cortisol levels, and cognitive function in major depressive disorder patients. Depress Res Treat 2:2

    Google Scholar 

  • Nouman M, Khoo SY, Mahmud MAP, Kouzani AZ (2022) Recent advances in contactless sensing technologies for mental health monitoring. IEEE Internet Things J 9(1):274–297

    Google Scholar 

  • Ogles BM, France CR, Lunnen KM (1998) Computerized depression screening and awareness. Commun Ment Health 34:27–38

    Google Scholar 

  • Ooi KEB, Lech M, Allen NB (2013) Multichannel weighted speech classification system for prediction of major depression in adolescents. IEEE Trans Biomed Eng 60(2):497–506

    Google Scholar 

  • Ozdas A, Shiavi RG, Silverman SE, Silverman MK, Mitchell Wilkes D (2004) Investigation of vocal jitter and glottal flow spectrum as possible cues for depression and near-term suicidal risk. IEEE Trans Biomed Eng 51(9):1530–1540

    Google Scholar 

  • Pampouchidou A et al (2019) Automatic assessment of depression based on visual cues: a systematic review. IEEE Trans Affect Comput 10(4):445–470

    Google Scholar 

  • Passik SD, Lundberg JC, Rosenfeld B, Kirsh KL, Donaghy K, Theobald D, Lundberg E, Dugan W (2000) Factor analysis of the Zung self-rating depression scale in a large ambulatory oncology sample. Psychosomatics 41:121–127

    Google Scholar 

  • Pourkeyvan A, Safa R, Sorourkhah A (2024) Harnessing the power of hugging face transformers for predicting mental health disorders in social networks. IEEE Access 12:28025–28035

    Google Scholar 

  • Pronk M, Deeg DJH, Smits C, van Tilburg TG, Kuik DJ, Festen JM, Kramer SE (2011) Prospective effects of hearing status on loneliness and depression in older persons: identification of subgroups. Int J Audiol 50:887–896

    Google Scholar 

  • Radloff LS, Locke BZ (1985) The community mental health assessment survey and the CES-D scale. Commun Surv Psychiatr Disord 2:177–189

    Google Scholar 

  • Rahman RA, Omar K, Mohd Noah SA, Danuri MSNM, Al-Garadi MA (2020) Application of machine learning methods in mental health detection: a systematic review. IEEE Access 8:183952–183964

    Google Scholar 

  • Rejaibi E, Komaty A, Meriaudeau F, Agrebi S, Othmani A (2020) MFCC-based recurrent neural network for automatic clinical depression recognition and assessment from speech. Human Computer Interaction. Cornell University

    Google Scholar 

  • Robert M, CarneyBarry A, Kapila HK (1981) A comparison of EMG and SCL in normal and depressed subjects. Pavlovian J Biol Sci 16:212–216

    Google Scholar 

  • Rostami M, Bahmani B, Bakhtyari V, Movallali G (2014) Depression and deaf adolescents: a review. Iran Rehabil J 12:43–53

    Google Scholar 

  • Rush AJ, Carmody T, Reimitz PE (2000) The inventory of depressive symptomatology (IDS): clinician (IDS-C) and self-report (IDS-SR) ratings of depressive symptoms. Int J Methods Psychiatr Res 9:45–59

    Google Scholar 

  • Saeedi M, Saeedi A, Maghsoudi A (2020) Major depressive disorder assessment via enhanced K-nearest neighbor method and EEG signals. Phys Eng Sci Med 43(3):1007–1018

    Google Scholar 

  • Saha S, Scott J, Varghese D, McGrath J (2012) Anxiety and depressive disorders are associated with delusional-like experiences: a replication study based on a National Survey of Mental Health and Wellbeing. BMJ Open 2:2

    Google Scholar 

  • Sarkar A, Singh A, Chakraborty R (2022) A deep learning-based comparative study to track mental depression from EEG data. Neurosci Inf 2:4

    Google Scholar 

  • Sau A, Bhakta I (2017) Artificial neural network (ANN) model to predict depression among geriatric population at a Slum in Kolkata, India. J Clin Diagn Res 11:5

    Google Scholar 

  • Sau A, Bhakta I (2019) Screening of anxiety and depression among seafarers using machine learning technology. Inf Med Unlocked 16:19

    Google Scholar 

  • Sayar K, Güleç H, Gökçe M, Smail A (2002) Heart rate variability in depressed patients. Bull Clin Psychopharmacol 12(3):130–132

    Google Scholar 

  • Scherer S, Lucas GM, Gratch J (2016) Self-repor ted symptoms of depression and PTSD are associated with reduced vowel space in screening inter views. IEEE Trans Affect Comput 7(1):59–63

    Google Scholar 

  • Schwab K (2016) The global competitiveness report, 2016–2017. World Economic Forum, Singapore

    Google Scholar 

  • Seggie Jo, MacMillan H, Griffith L, Shannon HS, Martin J, Simpson J, Steiner M (1990) Retinal pigment epithelium response and the use of the EOG and arden ratio in depression. Psychiatry Res 36:175–185

    Google Scholar 

  • Shengfu Lu, Jiying Xu, Li Mi, Xue J, Xiaofeng Lu, Feng L, Bingbing Fu, Wang G, Zhong N, Bin Hu (2017) Attentional bias scores in patients with depression and effects of age: a controlled, eye-tracking study. J Int Med Res 45(5):1518–1527

    Google Scholar 

  • Sivertsen H, Bjørkløf GH, Engedal K, Selbæk G, Helvik AS (2015) Depression and quality of life in older persons: a review. Dement Geriatr Cogn Disorder 40:311–339

    Google Scholar 

  • Spelta A, Flori A, Pierri F et al (2020) After the lockdown: simulating mobility, public health and economic recovery scenarios. Sci Rep 10:16950

    Google Scholar 

  • Spitzer RL, Williams JB, Kroenke K (1994) Utility of a new procedure for diagnosing mental disorders in primary care: the PRIME-MD 1000 study. JAMA 272(22):1749–1756

    Google Scholar 

  • Stead WW (2018) Clinical implications and challenges of artificial iintelligence and deep learning. JAMA 320(11):1107–1108

    Google Scholar 

  • Steer RA, Beck AT, Garrison B (1986) Applications of the beck depression inventory. In: Sartorius N, Ban TA (eds) Assessment of depression. World Health Organization, Geneva, pp 121–142

    Google Scholar 

  • Stiles PG, Mcgarrahan JF (1998) The geriatric depression scale: a comprehensive review. J Clin Geropsychol 4:89–110

    Google Scholar 

  • Stolar MN, Lech M, Stolar SJ, Allen NB (2018) Detection of adolescent depression from speech using optimised spectral roll-off parameters. Biomed J Sci Tech Res 5:1

    Google Scholar 

  • Stuhrmann A, Suslow T, Dannlowski U (2011) Facial emotion processing in major depression: a systematic review of neuroimaging findings. Biol Mood Anxiety Disord 1:10

    Google Scholar 

  • Sumiyakhand D, Guanghao S, Toshikazu S, Mai K, Nobutoshi K, Loravsal C, Suvdaa B, Seokjin K, Satoshi S, Takemi M (2018) Development and clinical application of a novel autonomic transient response-based screening system for major depressive disorder using a fingertip photoplethysmographic sensor. Front Bioeng Biotechnol 6:64

    Google Scholar 

  • Sun G, Shinba T, Kirimoto T, Mutsi T (2016) An objective screening method for major depressive disorder using logistic regression analysis of heart rate variability data obtained in a mental task paradigm. Front Psych 7:180

    Google Scholar 

  • Thieme A, Belgrave D, Doherty G (2020) Machine learning in mental health: a systematic review of the HCI literature to support the development of effective and implementable ML systems. ACM Trans Comput Hum Interact 27:1–53

    Google Scholar 

  • Ueafyeam K, Boonnag C, Sudhawiyangkul T, Leelaarporn P, Gulistan A, Wei Chen SC, Mukhopadhya TW, Piyayotai S (2021) Potential applications of mobile and wearable devices for physchological support during the covid-19 pandemic: a review. IEEE Sens J 21:6

    Google Scholar 

  • Valenza G, Nardelli M, Lanatà A, Gentili C, Bertschy G, Paradiso R (2014) Wearable monitoring for mood recognition in bipolar disorder based on history-dependent long-term heart rate variability analysis. IEEE J Biomed Heal Inf 18:1625–1635

    Google Scholar 

  • Valenza G, Citi L, Gentili C, Lanata A, Pasquale Scilingo E, Riccardo B (2015) Characterization of depressive states in bipolar patients using wearable textile technology and instantaneous heart rate variability assessment. IEEE J Biomed Health Inf 19:1

    Google Scholar 

  • Venkataraman D, Parameswaran NS (2018) Extraction of facial features for depression detection among students. Int J Pure Appl Math 118(7):455–463

    Google Scholar 

  • Vicsi K, Sztahó D, Kiss G (2012) Examination of the sensitivity of acoustic-phonetic parameters of speech to depression. 3rd IEEE International Conference on Cognitive Info-Communications

  • Wade BS, Joshi SH, Pirnia T, Leaver AM, Woods RP, Thompson PM, Espinoza R, Narr KL (2015) Random forest classification of depression status based on subcortical brain morphometry following electroconvulsive therapy. Proceedings IEEE International Symposium on Biomedical Imaging 25:92–96

  • Wang R, Hao Y, Yu Q, Chen M, Humar I, Fortino G (2021a) Depression analysis and recognition based on functional near infrared spectroscopy. IEEE J Biomed Health Inf 25:12

    Google Scholar 

  • Wang H, Liu Y, Zhen X, Tu X (2021b) Depression speech recognition with a three-dimensional convolutional network. J Front Hum Neurosci 15:713823

    Google Scholar 

  • Xiaowei Li, Rong La, Wang Ying Hu, Bin ZX (2020) A deep learning approach for mild depression recognition based on functional connectivity using electroencephalography. Front Neurosci 14:192

    Google Scholar 

  • Yamada Y, Kobayashi M (2018a) Detecting mental fatigue from eye-tracking data gathered while watching video: evaluation in younger and older adults. Artif Intell Med 91:39–48

    Google Scholar 

  • Yamada Y, Kobayashi M (2018b) Detecting mental fatigue from eye-tracking data gathered while watching video: evaluation in younger and older adults. Artif Intell Med 2:2

    Google Scholar 

  • Yang Y, Fairbairn C, Cohn JF (2013) Detecting depression severity from vocal prosody. IEEE Trans Affect Comput 4(2):142–150

    Google Scholar 

  • Yasin S, Othmani A, Raza I, Hussain SA (2023) Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: a comprehensive review. Comput Biol Med 159:106741

    Google Scholar 

  • Yu J, Xue AY, Redei EE, Bagheri N (2016) A support vector machine model provides an accurate transcript-level-based diagnostic for major depressive disorder. Transl Psychiatry 6:2

    Google Scholar 

  • Zang X, Li B, Zhao L et al (2022) End-to-end depression recognition based on a one-dimensional convolution neural network model using two-lead ECG signal. J Med Biol Eng 42:225–233

    Google Scholar 

  • Zangróniz R, Martínez-Rodrigo A, López MT, Ator JM, Fernández-Caballero A (2018) Estimation of mental distress from photoplethysmography. Appl Sci 8:69

    Google Scholar 

  • Zhang ZX, Tian XW, Lim JS (2011) New algorithm for the depression diagnosis using HRV: a neuro-fuzzy approach. International Symposium on Bioelectronics and Bioinformation’s, pp. 283–286

  • Ziai K, Moshtaghi O, Mahboubi H (2017) Tinnitus patients suffering from anxiety and depression: a review. In Tinnitus J 21:1

    Google Scholar 

  • Zigmond AS, Snaith RP (1983) The hospital anxiety and depression scale. Acta Psychiatr Scand 67:361–370

    Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication, Maharaja Institute of Technology Mysore, Mandya, Karnatka, India

    Smitha Joyce Pinto

  2. Dipartimento Di Scienze Aziendali—Management & Innovation Systems, Università Degli Studi Di Salerno, Salerno, Italy

    Mimmo Parente

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  1. Smitha Joyce Pinto
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SJP has drafted, reviewed, and formatted the paper. MP has supervised the research work.

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Pinto, S.J., Parente, M. Comprehensive review of depression detection techniques based on machine learning approach. Soft Comput 28, 10701–10725 (2024). https://doi.org/10.1007/s00500-024-09862-1

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