Discrimination of bipolar disorders using voice

Masakazu Higuchi; Mitsuteru Nakamura; Shuji Shinohara; Yasuhiro Omiya; Takeshi Takano; Hiroyuki Toda; Taku Saito; Aihide Yoshino; Shunji Mitsuyoshi; Shinichi Tokuno

doi:10.1007/978-3-030-25872-6_16

Discrimination of bipolar disorders using voice

Masakazu Higuchi, Mitsuteru Nakamura, Shuji Shinohara, Yasuhiro Omiya, Takeshi Takano, Hiroyuki Toda, Taku Saito, Aihide Yoshino, Shunji Mitsuyoshi, Shinichi Tokuno

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English (US)
Title of host publication	Pervasive Computing Paradigms for Mental Health - 9th International Conference, MindCare 2019, Proceedings
Editors	Pietro Cipresso, Silvia Serino, Daniela Villani
Publisher	Springer Verlag
Pages	199-207
Number of pages	9
ISBN (Print)	9783030258719
DOIs	https://doi.org/10.1007/978-3-030-25872-6_16
State	Published - 2019
Externally published	Yes
Event	9th International Conference on Pervasive Computing Paradigms for Mental Health, MindCare 2019 - Buenos Aires, Argentina Duration: Apr 23 2019 → Apr 24 2019

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	288
ISSN (Print)	1867-8211

Conference

Conference	9th International Conference on Pervasive Computing Paradigms for Mental Health, MindCare 2019
Country/Territory	Argentina
City	Buenos Aires
Period	4/23/19 → 4/24/19

Keywords

Bipolar disorders
Polytomous logistic regression analysis
Voice

ASJC Scopus subject areas

Computer Networks and Communications

Access to Document

10.1007/978-3-030-25872-6_16

Cite this

Higuchi, M., Nakamura, M., Shinohara, S., Omiya, Y., Takano, T., Toda, H., Saito, T., Yoshino, A., Mitsuyoshi, S., & Tokuno, S. (2019). Discrimination of bipolar disorders using voice. In P. Cipresso, S. Serino, & D. Villani (Eds.), Pervasive Computing Paradigms for Mental Health - 9th International Conference, MindCare 2019, Proceedings (pp. 199-207). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 288). Springer Verlag. https://doi.org/10.1007/978-3-030-25872-6_16

Discrimination of bipolar disorders using voice. / Higuchi, Masakazu; Nakamura, Mitsuteru; Shinohara, Shuji et al.

Pervasive Computing Paradigms for Mental Health - 9th International Conference, MindCare 2019, Proceedings. ed. / Pietro Cipresso; Silvia Serino; Daniela Villani. Springer Verlag, 2019. p. 199-207 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 288).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Higuchi, M, Nakamura, M, Shinohara, S, Omiya, Y, Takano, T, Toda, H, Saito, T, Yoshino, A, Mitsuyoshi, S & Tokuno, S 2019, Discrimination of bipolar disorders using voice. in P Cipresso, S Serino & D Villani (eds), Pervasive Computing Paradigms for Mental Health - 9th International Conference, MindCare 2019, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 288, Springer Verlag, pp. 199-207, 9th International Conference on Pervasive Computing Paradigms for Mental Health, MindCare 2019, Buenos Aires, Argentina, 4/23/19. https://doi.org/10.1007/978-3-030-25872-6_16

Higuchi M, Nakamura M, Shinohara S, Omiya Y, Takano T, Toda H et al. Discrimination of bipolar disorders using voice. In Cipresso P, Serino S, Villani D, editors, Pervasive Computing Paradigms for Mental Health - 9th International Conference, MindCare 2019, Proceedings. Springer Verlag. 2019. p. 199-207. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-030-25872-6_16

Higuchi, Masakazu ; Nakamura, Mitsuteru ; Shinohara, Shuji et al. / Discrimination of bipolar disorders using voice. Pervasive Computing Paradigms for Mental Health - 9th International Conference, MindCare 2019, Proceedings. editor / Pietro Cipresso ; Silvia Serino ; Daniela Villani. Springer Verlag, 2019. pp. 199-207 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

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AB - 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.

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Discrimination of bipolar disorders using voice

Abstract

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Conference

Keywords

ASJC Scopus subject areas

Access to Document

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