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RESEARCH ARTICLE

Feasibility study on using house-tree-person drawings for automatic analysis of depression

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Pages 1129-1140 | Received 10 Mar 2023, Accepted 23 Jun 2023, Published online: 07 Jul 2023

References

  • Beck AT, Beamesderfer A. (1974). Assessment of depression: the depression inventory. S. Karger.
  • Belmaker RH, Agam G. 2008. Major depressive disorder. N Engl J Med. 358(1):55–68.
  • Bilello JA. 2016. Seeking an objective diagnosis of depression. Biomark Med. 10(8):861–875.
  • Blain GH, Bergner RM, Lewis ML, Goldstein MA. 1981. The use of objectively scorable house‐tree‐person indicators to establish child abuse. J Clin Psychol. 37(3):667–673.
  • Breiman L. 1996. Bagging predictors. Mach Learn. 24(2):123–140.
  • Buck JN. 1948. The HTP technique; a qualitative and quantitative scoring manual. J Clin Psychol. 4(4):317–317.
  • Cai H, Zhang X, Zhang Y, Wang Z, Hu B. 2020. A case-based reasoning model for depression based on three-electrode EEG data. IEEE Trans Affective Comput. 11(3):383–392.
  • Callaghan TC. 2000. Factors affecting children’s graphic symbol use in the third year: Llanguage, similarity, and iconicity. Cogn Dev. 15(2):185–214.
  • 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. 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops; September. IEEE; p. 1–7.
  • Cummins N, Scherer S, Krajewski J, Schnieder S, Epps J, Quatieri TF. 2015. A review of depression and suicide risk assessment using speech analysis. Speech cCommunication. 71:10–49.
  • Goodenough FL. 1926. Measurement of intelligence by drawings. New York, NY: World Book Company.
  • Gratch J, Artstein R, Lucas G, Stratou G, Scherer S, Nazarian A, Wood R, Boberg J, DeVault D, Marsella S, et al. 2014. The distress analysis interview corpus of human and computer interviews. Los Angeles (CA): University of Southern California.
  • Hamilton M. 1960. A rating scale for depression. J Neurol Neurosurg Psychiatry. 23(1):56–62.
  • Hammer EF. 1988. Kinetic-house-tree-person drawings (KHTP): Aan interpretative manual: RRobert C. Burns. New York (NY): BBrunner/Mazel; p. 1987. 213 pages, $27.50.
  • Han KM, De Berardis D, Fornaro M, Kim YK. 2019. Differentiating between bipolar and unipolar depression in functional and structural MRI studies. Prog Neuropsychopharmacol Biol Psychiatry. 91:20–27.
  • Harris DB. 1965. Children’s drawings as measures of intellectual maturity. J Aesthet Art Crit. 23(4):516.
  • Hastie T, Rosset S, Zhu J, Zou H. 2009. Multi-class AdaBoost. Stat Interface. 2(3):349–360.
  • Hawton KI, Comabella CC, Haw C, Saunders K. 2013. Risk factors for suicide in individuals with depression: a systematic review. J Affect Disord. 147(1–3):17–28.
  • He L, Niu M, Tiwari P, Marttinen P, Su R, Jiang J, Guo C, Wang H, Ding S, Wang Z, et al. 2022. Deep learning for depression recognition with audiovisual cues: A a review. Inf Fusion. 80:56–86.
  • Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B. 1998. Support vector machines. IEEE Intell Syst Their Appl. 13(4):18–28.
  • Huang Y, Wang Y, Wang H, Liu Z, Yu X, Yan J, Yu Y, Kou C, Xu X, Lu J, et al. 2019. Prevalence of mental disorders in China: a cross-sectional epidemiological study. Lancet Psychiatry. 6(3):211–224.
  • Kirchner JH, Marzolf SS. 1974. Personality of alcoholics as measured by Sixteen Personality Factor Questionnaire and house-tree-person color-choice characteristics. Psychol Rep. 35(1 Pt 2):627–642.
  • Krebber AMH, Buffart LM, Kleijn G, Riepma IC, de Bree R, Leemans CR, Becker A, Brug J, van Straten A, Cuijpers P, et al. 2014. Prevalence of depression in cancer patients: a meta‐analysis of diagnostic interviews and self‐report instruments. Psychooncology. 23(2):121–130.
  • Ma X, Yang H, Chen Q, Huang D, Wang Y. 2016. Depaudionet: Aan efficient deep model for audio based depression classification. Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge; October; p. 35–42.
  • Maj M, Stein DJ, Parker G, Zimmerman M, Fava GA, De Hert M, Demyttenaere K, McIntyre RS, Widiger T, Wittchen H-U. 2020. The clinical characterization of the adult patient with depression aimed at personalization of management. World Psychiatry. 19(3):269–293.
  • Mogge NL, LePage JP. 2004. The Assessment of Depression Inventory (ADI): A a new instrument used to measure depression and to detect honesty of response. Depress Anxiety. 20(3):107–113.
  • Mohan Y, Chee SS, Xin DKP, Foong LP. 2016. Artificial neural network for classification of depressive and normal in EEG. 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, Malaysia; p. 286–290.
  • Mundt JC, Snyder PJ, Cannizzaro MS, Chappie K, Geralts DS. 2007. Voice acoustic measures of depression severity and treatment response collected via interactive voice response (IVR) technology. J Neurolinguistics. 20(1):50–64.
  • Nasir M, Jati A, Shivakumar PG, Nallan Chakravarthula S, Georgiou P. 2016. Multimodal and multiresolution depression detection from speech and facial landmark features. Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge; October; p. 43–50.
  • Nocedal J, Wright SJ. (Eds.) (1999). Numerical optimization. New York, NY: Springer New York.
  • Pampouchidou A, Simos PG, Marias K, Meriaudeau F, Yang F, Pediaditis M, Tsiknakis M. 2019. Automatic assessment of depression based on visual cues: a systematic review. IEEE Trans Affective Comput. 10(4):445–470.
  • Panesi S, Morra S. 2022. The relation between drawing and language in preschoolers: Tthe role of working Memory and executive functions. Cogn Dev. 61:101142.
  • Quinlan JR. 1986. Induction of decision trees. Mach Learn. 1(1):81–106. doi: 10.1007/BF00116251.
  • Rosenblatt F. 1958. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev. 65(6):386–408.
  • Rosten E, Drummond T. 2006. Machine learning for high-speed corner detection. Computer Vision–ECCV 2006: 9th European Conference on Computer Vision; Graz, Austria; May 7–13 Proceedings, Part I 9. Berlin; Heidelberg: Springer Berlin Heidelberg; p. 430–443.
  • Shoumy NJ, Ang LM, Seng KP, Rahaman DM, Zia T. 2020. Multimodal big data affective analytics: A comprehensive survey using text, audio, visual and physiological signals. J Netw Comput Appl. 149:102447.
  • Valstar M, Schuller B, Smith K, Almaev T, Eyben F, Krajewski J, Cowie R, Pantic M. 2014. Avec 2014: 3D dimensional affect and depression recognition challenge. Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge; November; p. 3–10.
  • Valstar M, Schuller B, Smith K, Eyben F, Jiang B, Bilakhia S, Schnieder S, Cowie R, Pantic M. 2013. Avec 2013: Tthe continuous audio/visual emotion and depression recognition challenge. Proceedings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge; October; p. 3–10.
  • Zimmerman M, Coryell W. 1987. The Inventory to Diagnose Depression (IDD): a self-report scale to diagnose major depressive disorder. J Consult Clin Psychol. 55(1):55–59.
  • Zung WW. 1965. A self-rating depression scale. Arch Gen Psychiatry. 12(1):63–70.

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