Mental disorders: MedlinePlus. https://medlineplus.gov/mentaldisorders.html (accessed 07 Mar 2022).

Ritchie H, Roser M. Mental health. Our World Data. 2018; [Online]. Available: https://ourworldindata.org/mental-health.

James SL, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789–858. https://doi.org/10.1016/S0140-6736(18)32279-7.

Article  Google Scholar 

Chong SA, Vaingankar JA, Abdin E, Subramaniam M. Mental disorders: employment and work productivity in Singapore. Soc Psychiatry Psychiatr Epidemiol. 2013;48(1):117–23. https://doi.org/10.1007/s00127-012-0526-5.

Article  Google Scholar 

Too LS, Spittal MJ, Bugeja L, Reifels L, Butterworth P, Pirkis J. The association between mental disorders and suicide: a systematic review and meta-analysis of record linkage studies. J Affect Disord. 2019;259:302–13. https://doi.org/10.1016/j.jad.2019.08.054.

Article  Google Scholar 

Lund C, et al. Poverty and mental disorders: breaking the cycle in low-income and middle-income countries. Lancet. 2011;378(9801):1502–14. https://doi.org/10.1016/S0140-6736(11)60754-X.

Article  Google Scholar 

Liu ZH, et al. The prevalence of painful physical symptoms in major depressive disorder: a systematic review and meta-analysis of observational studies. Prog Neuro-Psychopharmacology Biol Psychiatry. 2021;111:110372. https://doi.org/10.1016/J.PNPBP.2021.110372.

Article  Google Scholar 

Wang Q, Xu R, Volkow ND. Increased risk of COVID-19 infection and mortality in people with mental disorders: analysis from electronic health records in the United States. World Psychiatry. 2021;20(1):124–30. https://doi.org/10.1002/WPS.20806.

Article  Google Scholar 

Scott KM, et al. Associations between DSM-IV mental disorders and subsequent heart disease onset: beyond depression. Int J Cardiol. 2013;168(6):5293–9. https://doi.org/10.1016/J.IJCARD.2013.08.012.

Article  Google Scholar 

Razzouk D. Mental health economics: the costs and benefits of psychiatric care. Ment Heal Econ Costs Benefits Psychiatr Care. 2017;1–459. https://doi.org/10.1007/978-3-319-55266-8.

World Health Organisation. SDG indicators — SDG indicators. https://unstats.un.org/sdgs/metadata/?Text=&Goal=3&Target=3.4 (accessed 27 Aug 2021).

Moustgaard H, Bello S, Miller FG, Hróbjartsson A. Subjective and objective outcomes in randomized clinical trials: definitions differed in methods publications and were often absent from trial reports. J Clin Epidemiol. 2014;67(12):1327–34. https://doi.org/10.1016/J.JCLINEPI.2014.06.020.

Article  Google Scholar 

Lader M. Rating scales in schizophrenia. CNS Drugs 2000. 2012;14(1):23–32. https://doi.org/10.2165/00023210-200014010-00003.

Article  Google Scholar 

Sajatovic M, Chen P, Young RC. Rating scales in bipolar disorder. Clin Trial Des Challenges Mood Disord. 2015;105–136. https://doi.org/10.1016/B978-0-12-405170-6.00009-9.

Sheehan B. Assessment scales in dementia. Ther Adv Neurol Disord. 2012;5(6):349. https://doi.org/10.1177/1756285612455733.

Article  Google Scholar 

Fried EI. The 52 symptoms of major depression: lack of content overlap among seven common depression scales. J Affect Disord. 2017;208:191–7. https://doi.org/10.1016/J.JAD.2016.10.019.

Article  Google Scholar 

Yasuhara A. Correlation between EEG abnormalities and symptoms of autism spectrum disorder (ASD). Brain Dev. 2010;32(10):791–8. https://doi.org/10.1016/J.BRAINDEV.2010.08.010.

Article  Google Scholar 

Shi T, et al. EEG characteristics and visual cognitive function of children with attention deficit hyperactivity disorder (ADHD). Brain Dev. 2012;34(10):806–11. https://doi.org/10.1016/J.BRAINDEV.2012.02.013.

Article  Google Scholar 

Pillai V, Kalmbach DA, Ciesla JA. A meta-analysis of electroencephalographic sleep in depression: evidence for genetic biomarkers. Biol Psychiatry. 2011;70(10):912–9. https://doi.org/10.1016/J.BIOPSYCH.2011.07.016.

Article  Google Scholar 

Narayanan B, et al. Resting state electroencephalogram oscillatory abnormalities in schizophrenia and psychotic bipolar patients and their relatives from the bipolar and schizophrenia network on intermediate phenotypes study. Biol Psychiatry. 2014;76(6):456–65. https://doi.org/10.1016/J.BIOPSYCH.2013.12.008.

Article  Google Scholar 

Kam JWY, Bolbecker AR, O’Donnell BF, Hetrick WP, Brenner CA. Resting state EEG power and coherence abnormalities in bipolar disorder and schizophrenia. J Psychiatr Res. 2013;47(12):1893–901. https://doi.org/10.1016/J.JPSYCHIRES.2013.09.009.

Article  Google Scholar 

Olbrich S, Arns M. EEG biomarkers in major depressive disorder: discriminative power and prediction of treatment response. 2013;25(5):604–18. https://doi.org/10.3109/09540261.2013.816269.

Blom EH, Olsson E, Serlachius E, Ericson M, Ingvar M. Heart rate variability (HRV) in adolescent females with anxiety disorders and major depressive disorder. Acta Pædiatrica. 2010;99(4):604–11. https://doi.org/10.1111/J.1651-2227.2009.01657.X.

Article  Google Scholar 

Liang CS, Lee JF, Chen CC, Chang YC. Reactive heart rate variability in male patients with first-episode major depressive disorder. Prog Neuro-Psychopharmacology Biol Psychiatry. 2015;56:52–7. https://doi.org/10.1016/J.PNPBP.2014.08.004.

Article  Google Scholar 

Akar SA, Kara S, Latifoğlu F, Bilgiç V. Analysis of heart rate variability during auditory stimulation periods in patients with schizophrenia. J Clin Monit Comput. 2014;29(1):153–62. https://doi.org/10.1007/S10877-014-9580-8.

Article  Google Scholar 

Kasanuki K, et al. Impaired heart rate variability in patients with dementia with Lewy bodies: efficacy of electrocardiogram as a supporting diagnostic marker. Parkinsonism Relat Disord. 2015;21(7):749–54. https://doi.org/10.1016/J.PARKRELDIS.2015.04.024.

Article  Google Scholar 

Faurholt-Jepsen M, Kessing LV, Munkholm K. Heart rate variability in bipolar disorder: a systematic review and meta-analysis. Neurosci Biobehav Rev. 2017;73:68–80. https://doi.org/10.1016/J.NEUBIOREV.2016.12.007.

Article  Google Scholar 

Valenza G, et al. Predicting mood changes in bipolar disorder through heartbeat nonlinear dynamics. IEEE J Biomed Heal Informatics. 2016;20(4):1034–43. https://doi.org/10.1109/JBHI.2016.2554546.

Article  Google Scholar 

Alam MAU, Roy N, Holmes S, Gangopadhyay A, Galik E. Automated functional and behavioral health assessment of older adults with dementia. Proc. - 2016 IEEE 1st Int. Conf. Connect. Heal. Appl. Syst. Eng. Technol. CHASE. 2016;140–149. https://doi.org/10.1109/CHASE.2016.16.

Greco A, Valenza G, Lanata A, Rota G, Scilingo EP. Electrodermal activity in bipolar patients during affective elicitation. IEEE J Biomed Heal Informatics. 2014;18(6):1865–73. https://doi.org/10.1109/JBHI.2014.2300940.

Article  Google Scholar 

Sarchiapone M, et al. The association between electrodermal activity (EDA), depression and suicidal behaviour: a systematic review and narrative synthesis. BMC Psychiatry. 2018;18(1):1–27. https://doi.org/10.1186/S12888-017-1551-4.

Article  Google Scholar 

Pourmohammadi S, Maleki A. Stress detection using ECG and EMG signals: a comprehensive study. Comput Methods Prog Biomed. 2020;193. https://doi.org/10.1016/j.cmpb.2020.105482.

Ghaderi A, Frounchi J, Farnam A. “Machine learning-based signal processing using physiological signals for stress detection”, 2015 22nd Iran. Conf Biomed Eng ICBME. 2016;2015:93–8. https://doi.org/10.1109/ICBME.2015.7404123.

Article  Google Scholar 

Wijsman J, Grundlehner B, Liu H, Hermens H, Penders J. Towards mental stress detection using wearable physiological sensors. Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS. 2011:1798–1801. https://doi.org/10.1109/IEMBS.2011.6090512.

Bubl E, Kern E, Ebert D, Bach M, Tebartz Van Elst L. Seeing gray when feeling blue? Depression can be measured in the eye of the diseased. Biol Psychiatry. 2010;68(2):205–8. https://doi.org/10.1016/J.BIOPSYCH.2010.02.009.

Article  Google Scholar 

Schwitzer T, Lavoie J, Giersch A, Schwan R, Laprevote V. The emerging field of retinal electrophysiological measurements in psychiatric research: a review of the findings and the perspectives in major depressive disorder. J Psychiatr Res. 2015;70:113–20. https://doi.org/10.1016/J.JPSYCHIRES.2015.09.003.

Article  Google Scholar 

Berman G, et al. Decreased retinal sensitivity in depressive disorder: a controlled study. Acta Psychiatr Scand. 2018;137(3):231–40. https://doi.org/10.1111/ACPS.12851.

Article  Google Scholar 

Ma G, et al. Electrocardiographic and electrooculographic responses to external emotions and their transitions in bipolar I and II disorders. Int J Environ Res Public Heal. 2018;15:884. https://doi.org/10.3390/IJERPH15050884.

Article  Google Scholar 

Hosseinifard B, Moradi MH, Rostami R. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal. Comput Methods Programs Biomed. 2013;109(3):339–45. https://doi.org/10.1016/j.cmpb.2012.10.008.

Article  Google Scholar 

Li X, Hu B, Sun S, Cai H. EEG-based mild depressive detection using feature selection methods and classifiers. Comput Methods Programs Biomed. 2016;136:151–61. https://doi.org/10.1016/j.cmpb.2016.08.010.

Article  Google Scholar 

Kim AY, et al. Automatic detection of major depressive disorder using electrodermal activity. Sci Rep. 2018;8(1):1–9. https://doi.org/10.1038/s41598-018-35147-3.

Article  Google Scholar 

Roh T, Hong S, Yoo HJ. Wearable depression monitoring system with heart-rate variability. 2014 36th Annu Int Conf IEEE Eng Med Biol Soc EMBC. 2014;562–565. https://doi.org/10.1109/EMBC.2014.6943653.

Williamson JR, Quatieri TF, Helfer BS, Ciccarelli G, Mehta DD. “ocal and facial biomarkers of depression based on motor incoordination and timing. AVEC 2014 - Proc 4th Int Work Audio/Visual Emot Challenge, Work MM 2014. 2014. p. 65–72. https://doi.org/10.1145/2661806.2661809.

Mashio Y, Kawaguchi H. Detecting early symptoms of mental health deterioration using handwriting duration parameters. Neuropsychopharmacol Reports. 2020;40(3):246–53. https://doi.org/10.1002/npr2.12123.

Article  Google Scholar 

Anusha AS, et al. Electrodermal activity based pre-surgery stress detection using a wrist wearable. IEEE J Biomed Heal Informatics. 2020;24(1):92–100. https://doi.org/10.1109/JBHI.2019.2893222.

MathSciNet  Article  Google Scholar 

Pluntke U, Gerke S, Sridhar A, Weiss J, Michel B. Evaluation and classification of physical and psychological stress in firefighters using heart rate variability. Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS. 2019;2207–2212. https://doi.org/10.1109/EMBC.2019.8856596.

Bay Ayzeren Y, Erbilek M, Celebi E. Emotional state prediction from online handwriting and signature biometrics. IEEE Access. 2019;7:164759–74. https://doi.org/10.1109/ACCESS.2019.2952313.

Article  Google Scholar 

Khodayari-Rostamabad A, Reilly JP, Hasey G, Debruin H, MacCrimmon D. Diagnosis of psychiatric disorders using EEG data and employing a statistical decision model. 2010 Annu Int Conf IEEE Eng Med Biol Soc EMBC’10. 2010. p. 4006–4009. https://doi.org/10.1109/IEMBS.2010.5627998.

Liu Y, Du S. Psychological stress level detection based on electrodermal activity. Behav Brain Res. 2018;341:50–3. https://doi.org/10.1016/j.bbr.2017.12.021.

Article  Google Scholar 

Na KS, Cho SE, Cho SJ. Machine learning-based discrimination of panic disorder from other anxiety disorders. J Affect Disord. 2021;278:1–4. https://doi.org/10.1016/j.jad.2020.09.027.

Article  Google Scholar 

Cordasco G, Scibelli F, Faundez-Zanuy M, Likforman-Sulem L, Esposito A. “Handwriting and drawing features for detecting negative moods”, in Smart Innovation. Systems and Technologies. 2019;103:73–86.

Google Scholar 

Erguzel TT, Sayar GH, Tarhan N. Artificial intelligence approach to classify unipolar and bipolar depressive disorders. Neural Comput Appl. 2016;27(6):1607–16. https://doi.org/10.1007/s00521-015-1959-z.

Article  Google Scholar 

Greco A, Lanata A, Valenza G, Rota G, Vanello N, Scilingo EP. On the deconvolution analysis of electrodermal activity in bipolar patients. Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS. 2012;6691–6694. https://doi.org/10.1109/EMBC.2012.6347529.

Du Z, Huang D, Li W, Wang Y. Bipolar disorder recognition via multi-scale discriminative audio temporal representation. AVEC 2018 - Proc. 2018 Audio/Visual Emot. Chall. Work. co-located with MM 2018. 2018. p. 23–30. https://doi.org/10.1145/3266302.3268997.

PRISMA. http://www.prisma-statement.org/Default.aspx (accessed 08 Mar 2022).

Giannakakis G, Grigoriadis D, Giannakaki K, Simantiraki O, Roniotis A, Tsiknakis M. Review on psychological stress detection using biosignals. IEEE Trans Affect Comput. 2019. https://doi.org/10.1109/TAFFC.2019.2927337.

Article  Google Scholar 

Garcia-Ceja E, Riegler M, Nordgreen T, Jakobsen P, Oedegaard KJ, Tørresen J. Mental health monitoring with multimodal sensing and machine learning: a survey. Pervasive Mob Comput. 2018;51:1–26. https://doi.org/10.1016/j.pmcj.2018.09.003.

Article  Google Scholar 

de Aguiar Neto FS, Rosa JLG. Depression biomarkers using non-invasive EEG: a review. Neurosci Biobehav Rev. 2019;105:83–93. https://doi.org/10.1016/j.neubiorev.2019.07.021.

Article  Google Scholar 

Mahato S, Paul S. Electroencephalogram (EEG) signal analysis for diagnosis of major depressive disorder (MDD): a review. Lect Notes Electr Eng. 2019;511:323–35. https://doi.org/10.1007/978-981-13-0776-8_30.

Article  Google Scholar 

Vasu V, Indiramma M. A survey on bipolar disorder classification methodologies using machine learning. Proc - Int Conf Smart Electron Commun ICOSEC 2020, no. Icosec. 2020. p. 335–340. https://doi.org/10.1109/ICOSEC49089.2020.9215334.

Sabeti M, Katebi S, Boostani R. Entropy and complexity measures for EEG signal classification of schizophrenic and control participants. Artif Intell Med. 2009;47(3):263–74. https://doi.org/10.1016/j.artmed.2009.03.003.

Article