Discrimination of Bipolar Disorders Using Voice

  • Masakazu HiguchiEmail author
  • Mitsuteru Nakamura
  • Shuji Shinohara
  • Yasuhiro Omiya
  • Takeshi Takano
  • Hiroyuki Toda
  • Taku Saito
  • Aihide Yoshino
  • Shunji Mitsuyoshi
  • Shinichi Tokuno
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 288)


Several methods have been developed for screening mentally impaired patients using biomarkers, but these methods are invasive and costly. Self-administered tests are also used as screening methods. They are non-invasive and relatively simple, but they cannot eliminate the influence of reporting bias. On the other hand, the authors have conducted studies on technologies for inferring the mental state of persons from their voices. Analysis using voice has the advantage of being noninvasive and easy to perform. This study proposes a vocal index that will distinguish between a healthy person and a bipolar I or II patient using a polytomous logistic regression analysis with patients with bipolar disorder as subjects. When the subjects were classified using the prediction model obtained from the analysis, the subjects were categorized into three groups with an accuracy of approximately \(67\%\). This result suggested that the vocal index could be a new evaluation index for discriminating between subjects with and those without bipolar disorder.


Voice Bipolar disorders Polytomous logistic regression analysis 


  1. 1.
    Izawa, S., et al.: Salivary dehydroepiandrosterone secretion in response to acute psychosocial stress and its correlations with biological and psychological changes. Biol. Psychol. 79(3), 294–298 (2008)CrossRefGoogle Scholar
  2. 2.
    Suzuki, G., et al.: Decreased plasma brain-derived neurotrophic factor and vascular endothelial growth factor concentrations during military training. PloS One 9(2), e89455 (2014)CrossRefGoogle Scholar
  3. 3.
    Garcia, R.G., Valenza, G., Tomaz, C.A., Barbieri, R.: Instantaneous bispectral analysis of heartbeat dynamics for the assessment of major depression. In: The Proceedings of Computing in Cardiology 2015, pp. 781–784. Nice (2015)Google Scholar
  4. 4.
    Goldberg, D.P.: Manual of the General Health Questionnaire. NFER Publishing, Windsor (1978)Google Scholar
  5. 5.
    Beck, A.T., Ward, C.H., Mendelson, M., Mock, J., Erbaugh, J.: An inventory for measureing depression. Arch. Gen. Psychiatry 4(6), 561–571 (1961)CrossRefGoogle Scholar
  6. 6.
    Young, R.C., Biggs, J.T., Ziegler, V.E., Meyer, D.A.: A rating scale for mania: reliability, validity and sensitivity. Br. J. Psychiatry 133(5), 429–435 (1978)CrossRefGoogle Scholar
  7. 7.
    Delgado-Rodriguez, M., Llorca, J.: Bias. J. Epidemiol. Community Health 58(8), 635–641 (2004)CrossRefGoogle Scholar
  8. 8.
    Cummins, N., Epps, J., Breakspear, M., Goecke, R.: An investigation of depressed speech detection: features and normalization. In: The Proceedings of the 12th Annual Conference of the International Speech Communication Association, Florence, pp. 2997–3000 (2011)Google Scholar
  9. 9.
    Mundt, J.C., Vogel, A.P., Feltner, D.E., Lenderking, W.R.: Vocal acoustic biomarkers of depression severity and treatment response. Biol. Psychiatry 72(7), 580–587 (2012)CrossRefGoogle Scholar
  10. 10.
    Tokuno, S., Mitsuyoshi, S., Suzuki, G., Tsumatori, G.: Stress evaluation by voice: a novel stress evaluation technology. In: The Proceedings of the 9th International Conference on Early Psychosis, Tokyo, pp. 17–19 (2014)Google Scholar
  11. 11.
    Jiang, H., et al.: Investigation of different speech types and emotions for detecting depression using different classifiers. Speech Commun. 90, 39–46 (2017)CrossRefGoogle Scholar
  12. 12.
    Diagnostic and statistical manual of mental disorders V. American Psychiatric Association (2013)Google Scholar
  13. 13.
    Bowden, C.L.: Strategies to reduce misdiagnosis of bipolar depression. Psychiatr. Serv. 52(1), 51–55 (2001)CrossRefGoogle Scholar
  14. 14.
    Muzina, D.J., Kemp, D.E., McIntyre, R.S.: Differentiating bipolar disorders from major depressive disorders: treatment implications. Ann. Clin. Psychiatry 19(4), 305–312 (2007)CrossRefGoogle Scholar
  15. 15.
    Nakamura, M., et al.: Feasibility study of classifying major depressive disorder and bipolar disorders using voice features. In: The Proceedings of WPA XVII World Congress of Psychiatry, Berlin (2017)Google Scholar
  16. 16.
    Higuchi, M., et al.: Classification of bipolar disorder, major depressive disorder, and healthy state using voice. Asian J. Pharm. Clin. Res. 11(3), 89–93 (2018)CrossRefGoogle Scholar
  17. 17.
    Faurholt-Jepsen, M., et al.: Voice analysis as an objective state marker in bipolar disorder. Transl. Psychiatry 6, e856 (2016)CrossRefGoogle Scholar
  18. 18.
    Maxhuni, A., Muñoz-Meléndez, A., Osmani, V., Perez, H., Mayora, O., Morales, E.F.: Classification of bipolar disorder episodes based on analysis of voice and motor activity of patients. Pervasive Mob. Comput. 31, 50–66 (2016)CrossRefGoogle Scholar
  19. 19.
    Sheehan, D.V., et al.: The mini-international neuropsychiatric interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J. Clin. Psychiatry 59(Suppl. 20), 22–33 (1998)Google Scholar
  20. 20.
    Hamilton, M.: A rating scale for depression. J. Neurol. Neurosurg. Psychiatry 23, 56–62 (1960)CrossRefGoogle Scholar
  21. 21.
    Eyben, F., Wöllmer, M., Schuller, B.: openSMILE - the Munich versatile and fast open-source audio feature extractor. In: The Proceedings of the 18th ACM International Conference on Multimedia, Firenze, pp. 1459–1462 (2010)Google Scholar
  22. 22.
    Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(1), 1–22 (2010)CrossRefGoogle Scholar
  23. 23.
    R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. Accessed 2 Dec 2018

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Masakazu Higuchi
    • 1
    Email author
  • Mitsuteru Nakamura
    • 1
  • Shuji Shinohara
    • 2
  • Yasuhiro Omiya
    • 3
  • Takeshi Takano
    • 3
  • Hiroyuki Toda
    • 4
  • Taku Saito
    • 4
  • Aihide Yoshino
    • 4
  • Shunji Mitsuyoshi
    • 2
  • Shinichi Tokuno
    • 1
  1. 1.Graduate School of MedicineThe University of TokyoTokyoJapan
  2. 2.Graduate School of EngineeringThe University of TokyoTokyoJapan
  3. 3.PST Inc.YokohamaJapan
  4. 4.Department of PsychiatryNational Defense Medical CollegeTokorozawaJapan

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