Classification of bipolar disorder, major depressive disorder, and healthy state using voice

Masakazu Higuchi, Shinichi Tokuno, Mitsuteru Nakamura, Shuji Shinohara, Shunji Mitsuyoshi, Yasuhiro Omiya, Naoki Hagiwara, Takeshi Takano, Hiroyuki Toda, Taku Saito, Hiroo Terashi, Hiroshi Mitoma

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Objective: In this study, we propose a voice index to identify healthy individuals, patients with bipolar disorder, and patients with major depressive disorder using polytomous logistic regression analysis. Methods: Voice features were extracted from voices of healthy individuals and patients with mental disease. Polytomous logistic regression analysis was performed for some voice features. Results: With the prediction model obtained using the analysis, we identified subject groups and were able to classify subjects into three groups with 90.79% accuracy. Conclusion: These results show that the proposed index may be used as a new evaluation index to identify depression.

Original languageEnglish (US)
Pages (from-to)89-93
Number of pages5
JournalAsian Journal of Pharmaceutical and Clinical Research
Volume11
Issue numberSpecial Issue 3
DOIs
StatePublished - 2018
Externally publishedYes

Keywords

  • Bipolar disorder
  • Major depressive disorder
  • Polytomous logistic regression analysis
  • Voice

ASJC Scopus subject areas

  • Pharmacology
  • Pharmaceutical Science
  • Pharmacology (medical)

Fingerprint Dive into the research topics of 'Classification of bipolar disorder, major depressive disorder, and healthy state using voice'. Together they form a unique fingerprint.

  • Cite this

    Higuchi, M., Tokuno, S., Nakamura, M., Shinohara, S., Mitsuyoshi, S., Omiya, Y., Hagiwara, N., Takano, T., Toda, H., Saito, T., Terashi, H., & Mitoma, H. (2018). Classification of bipolar disorder, major depressive disorder, and healthy state using voice. Asian Journal of Pharmaceutical and Clinical Research, 11(Special Issue 3), 89-93. https://doi.org/10.22159/ajpcr.2018.v11s3.30042