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Detection of Major Depressive Disorder Based on a Combination of Voice Features: An Exploratory Approach

Author

Listed:
  • Masakazu Higuchi

    (Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan)

  • Mitsuteru Nakamura

    (Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan)

  • Shuji Shinohara

    (School of Science and Engineering, Tokyo Denki University, Saitama 350-0394, Japan)

  • Yasuhiro Omiya

    (PST Inc., Yokohama 231-0023, Japan)

  • Takeshi Takano

    (PST Inc., Yokohama 231-0023, Japan)

  • Daisuke Mizuguchi

    (PST Inc., Yokohama 231-0023, Japan)

  • Noriaki Sonota

    (Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan)

  • Hiroyuki Toda

    (Department of Psychiatry, School of Medicine, National Defense Medical College, Saitama 359-8513, Japan)

  • Taku Saito

    (Department of Psychiatry, School of Medicine, National Defense Medical College, Saitama 359-8513, Japan)

  • Mirai So

    (Department of Neuropsychiatry, Tokyo Dental College, Tokyo 101-0061, Japan)

  • Eiji Takayama

    (Department of Oral Biochemistry, Asahi University School of Dentistry, Gifu 501-0296, Japan)

  • Hiroo Terashi

    (Department of Neurology, Tokyo Medical University, Tokyo 160-8402, Japan)

  • Shunji Mitsuyoshi

    (Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan)

  • Shinichi Tokuno

    (Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
    Graduate School of Health Innovation, Kanagawa University of Human Services, Yokosuka 210-0821, Japan)

Abstract

In general, it is common knowledge that people’s feelings are reflected in their voice and facial expressions. This research work focuses on developing techniques for diagnosing depression based on acoustic properties of the voice. In this study, we developed a composite index of vocal acoustic properties that can be used for depression detection. Voice recordings were collected from patients undergoing outpatient treatment for major depressive disorder at a hospital or clinic following a physician’s diagnosis. Numerous features were extracted from the collected audio data using openSMILE software. Furthermore, qualitatively similar features were combined using principal component analysis. The resulting components were incorporated as parameters in a logistic regression based classifier, which achieved a diagnostic accuracy of ~90% on the training set and ~80% on the test set. Lastly, the proposed metric could serve as a new measure for evaluation of major depressive disorder.

Suggested Citation

  • Masakazu Higuchi & Mitsuteru Nakamura & Shuji Shinohara & Yasuhiro Omiya & Takeshi Takano & Daisuke Mizuguchi & Noriaki Sonota & Hiroyuki Toda & Taku Saito & Mirai So & Eiji Takayama & Hiroo Terashi &, 2022. "Detection of Major Depressive Disorder Based on a Combination of Voice Features: An Exploratory Approach," IJERPH, MDPI, vol. 19(18), pages 1-13, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:18:p:11397-:d:911796
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    References listed on IDEAS

    as
    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. Shuji Shinohara & Mitsuteru Nakamura & Yasuhiro Omiya & Masakazu Higuchi & Naoki Hagiwara & Shunji Mitsuyoshi & Hiroyuki Toda & Taku Saito & Masaaki Tanichi & Aihide Yoshino & Shinichi Tokuno, 2021. "Depressive Mood Assessment Method Based on Emotion Level Derived from Voice: Comparison of Voice Features of Individuals with Major Depressive Disorders and Healthy Controls," IJERPH, MDPI, vol. 18(10), pages 1-12, May.
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