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

doi:10.22159/ajpcr.2018.v11s3.30042

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 journal › Article › peer-review

3 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 language	English (US)
Pages (from-to)	89-93
Number of pages	5
Journal	Asian Journal of Pharmaceutical and Clinical Research
Volume	11
Issue number	Special Issue 3
DOIs	https://doi.org/10.22159/ajpcr.2018.v11s3.30042
State	Published - 2018
Externally published	Yes

Keywords

Bipolar disorder
Major depressive disorder
Polytomous logistic regression analysis
Voice

ASJC Scopus subject areas

Pharmacology
Pharmaceutical Science
Pharmacology (medical)

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10.22159/ajpcr.2018.v11s3.30042

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

Classification of bipolar disorder, major depressive disorder, and healthy state using voice. / Higuchi, Masakazu; Tokuno, Shinichi; Nakamura, Mitsuteru; Shinohara, Shuji; Mitsuyoshi, Shunji; Omiya, Yasuhiro; Hagiwara, Naoki; Takano, Takeshi; Toda, Hiroyuki; Saito, Taku; Terashi, Hiroo; Mitoma, Hiroshi.

In: Asian Journal of Pharmaceutical and Clinical Research, Vol. 11, No. Special Issue 3, 2018, p. 89-93.

Research output: Contribution to journal › Article › peer-review

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, vol. 11, no. Special Issue 3, pp. 89-93. https://doi.org/10.22159/ajpcr.2018.v11s3.30042

Higuchi, Masakazu ; Tokuno, Shinichi ; Nakamura, Mitsuteru ; Shinohara, Shuji ; Mitsuyoshi, Shunji ; Omiya, Yasuhiro ; Hagiwara, Naoki ; Takano, Takeshi ; Toda, Hiroyuki ; Saito, Taku ; Terashi, Hiroo ; Mitoma, Hiroshi. / Classification of bipolar disorder, major depressive disorder, and healthy state using voice. In: Asian Journal of Pharmaceutical and Clinical Research. 2018 ; Vol. 11, No. Special Issue 3. pp. 89-93.

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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.",

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AU - Higuchi, Masakazu

AU - Tokuno, Shinichi

AU - Nakamura, Mitsuteru

AU - Shinohara, Shuji

AU - Mitsuyoshi, Shunji

AU - Omiya, Yasuhiro

AU - Hagiwara, Naoki

AU - Takano, Takeshi

AU - Toda, Hiroyuki

AU - Saito, Taku

AU - Terashi, Hiroo

AU - Mitoma, Hiroshi

PY - 2018

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N2 - 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.

AB - 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.

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

Abstract

Keywords

ASJC Scopus subject areas

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