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ORIGINAL RESEARCH article

Front. Psychiatry
Sec. Mood Disorders
Volume 15 - 2024 | doi: 10.3389/fpsyt.2024.1422020

Research on Vocal Acoustic Features and Classification/Prediction Model of Major Depressive Disorders

Provisionally accepted
Lijuan Liang Lijuan Liang 1Yang Wang Yang Wang 2Hui Ma Hui Ma 3Ran Zhang Ran Zhang 2Rongxun Liu Rongxun Liu 2Rongxin Zhu Rongxin Zhu 2Zhiguo Zheng Zhiguo Zheng 4Xizhe Zhang Xizhe Zhang 5*Fei Wang Fei Wang 2*
  • 1 The First Affiliated Hospital, Hainan Medical University, Haikou, China
  • 2 Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu Province, China
  • 3 Hainan Anning Hospital, Haikou, Hainan Province, China
  • 4 School of Information and Communication Engineering, Hainan University, Haikou, China
  • 5 School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu Province, China

The final, formatted version of the article will be published soon.

    Background: Previous studies have classified major depression and healthy control groups based on vocal acoustic features, but the classification accuracy needs to be improved. Therefore, this study utilized deep learning methods to construct classification and prediction models for major depression and healthy control groups.Methods: 120 participants aged 16-25 participated in this study, included 64 MDD group and 56 HC group. We used the Covarep open-source algorithm to extract a total of 1200 high-level statistical functions for each sample. In addition, we used Python for correlation analysis, and neural network to establish the model to distinguish whether participants experienced depression, predict the total depression score, and evaluate the effectiveness of the classification and prediction model.The classification modelling of the major depression and the healthy control groups by relevant and significant vocal acoustic features was 0.90, and the Receiver Operating Characteristic (ROC) curves analysis results showed that the classification accuracy was 84.16%, the sensitivity was 95.38%, and the specificity was 70.9%. The depression prediction model of speech characteristics showed that the predicted score was closely related to the total score of 17 items of the Hamilton Depression Scale(HAMD-17) (r=0.687, P<0.01); and the Mean Absolute Error(MAE) between the model's predicted score and total HAMD-17 score was 4.51.

    Keywords: major depressive disorders, Vocal acoustic features, Classification, prediction, Model

    Received: 23 Apr 2024; Accepted: 12 Aug 2024.

    Copyright: © 2024 Liang, Wang, Ma, Zhang, Liu, Zhu, Zheng, Zhang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Xizhe Zhang, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, Jiangsu Province, China
    Fei Wang, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, 210029, Jiangsu Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.