Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning
The vocal acoustic features can not only effectively classify the major depression and the healthy control groups, but also accurately predict the severity of depressive symptoms.
Authors
- Lijuan Liang (ORCID: https://orcid.org/0000-0002-1081-0520)
- Hui Ma (ORCID: https://orcid.org/0000-0003-1188-9303)
- Rongxun Liu (ORCID: https://orcid.org/0009-0005-3147-3593)
- Yang Wang (ORCID: https://orcid.org/0000-0003-2802-5392)
- Xizhe Zhang (ORCID: https://orcid.org/0000-0002-8684-4591)
- Ran Zhang (ORCID: https://orcid.org/0000-0001-5108-1160)
- Fei Wang (ORCID: https://orcid.org/0000-0002-7650-6345)
- Rongxin Zhu (ORCID: https://orcid.org/0000-0001-5133-0868)
- Zhiguo Zheng (ORCID: https://orcid.org/0000-0003-2913-8591)
Institutions
- Hainan University (CN)
- Nanjing Brain Hospital (CN)
- Hainan Provincial Hospital of Traditional Chinese Medicine (CN)
- Hainan Medical University (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Frontiers in Psychiatry
- Published
- 2024-09-17
- DOI
- https://doi.org/10.3389/fpsyt.2024.1422020
- Citations
- 7
- Primary Topic
- Emotion and Mood Recognition
- Type
- article
- Field-Weighted Citation Impact
- 3.55
Funders
- National Natural Science Foundation of China
- Hainan Medical University
- National Science Fund for Distinguished Young Scholars