Bibliographic Details
| Title: |
Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning |
| Authors: |
Lijuan Liang, Yang Wang, Hui Ma, Ran Zhang, Rongxun Liu, Rongxin Zhu, Zhiguo Zheng, Xizhe Zhang, Fei Wang |
| Source: |
Frontiers in Psychiatry, Vol 15 (2024) |
| Publisher Information: |
Frontiers Media S.A., 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Psychiatry |
| Subject Terms: |
major depressive disorders, vocal acoustic features, classification, prediction, model, Psychiatry, RC435-571 |
| More Details: |
BackgroundPrevious 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.Methods120 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.ResultsThe 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 |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1664-0640 |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1422020/full; https://doaj.org/toc/1664-0640 |
| DOI: |
10.3389/fpsyt.2024.1422020 |
| Access URL: |
https://doaj.org/article/1a50f71b8185454c98aee70061508c95 |
| Accession Number: |
edsdoj.1a50f71b8185454c98aee70061508c95 |
| Database: |
Directory of Open Access Journals |
