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Fast and accurate assessment of depression based on voice acoustic features: a cross-sectional and longitudinal study
BACKGROUND: Depression is a widespread mental disorder that affects a significant portion of the population. However, the assessment of depression is often subjective, relying on standard questions or interviews. Acoustic features have been suggested as a reliable and objective alternative for depre...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320390/ https://www.ncbi.nlm.nih.gov/pubmed/37415683 http://dx.doi.org/10.3389/fpsyt.2023.1195276 |
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author | Wang, Yang Liang, Lijuan Zhang, Zhongguo Xu, Xiao Liu, Rongxun Fang, Hanzheng Zhang, Ran Wei, Yange Liu, Zhongchun Zhu, Rongxin Zhang, Xizhe Wang, Fei |
author_facet | Wang, Yang Liang, Lijuan Zhang, Zhongguo Xu, Xiao Liu, Rongxun Fang, Hanzheng Zhang, Ran Wei, Yange Liu, Zhongchun Zhu, Rongxin Zhang, Xizhe Wang, Fei |
author_sort | Wang, Yang |
collection | PubMed |
description | BACKGROUND: Depression is a widespread mental disorder that affects a significant portion of the population. However, the assessment of depression is often subjective, relying on standard questions or interviews. Acoustic features have been suggested as a reliable and objective alternative for depression assessment. Therefore, in this study, we aim to identify and explore voice acoustic features that can effectively and rapidly predict the severity of depression, as well as investigate the potential correlation between specific treatment options and voice acoustic features. METHODS: We utilized voice acoustic features correlated with depression scores to train a prediction model based on artificial neural network. Leave-one-out cross-validation was performed to evaluate the performance of the model. We also conducted a longitudinal study to analyze the correlation between the improvement of depression and changes in voice acoustic features after an Internet-based cognitive-behavioral therapy (ICBT) program consisting of 12 sessions. RESULTS: Our study showed that the neural network model trained based on the 30 voice acoustic features significantly correlated with HAMD scores can accurately predict the severity of depression with an absolute mean error of 3.137 and a correlation coefficient of 0.684. Furthermore, four out of the 30 features significantly decreased after ICBT, indicating their potential correlation with specific treatment options and significant improvement in depression (p < 0.05). CONCLUSION: Voice acoustic features can effectively and rapidly predict the severity of depression, providing a low-cost and efficient method for screening patients with depression on a large scale. Our study also identified potential acoustic features that may be significantly related to specific treatment options for depression. |
format | Online Article Text |
id | pubmed-10320390 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103203902023-07-06 Fast and accurate assessment of depression based on voice acoustic features: a cross-sectional and longitudinal study Wang, Yang Liang, Lijuan Zhang, Zhongguo Xu, Xiao Liu, Rongxun Fang, Hanzheng Zhang, Ran Wei, Yange Liu, Zhongchun Zhu, Rongxin Zhang, Xizhe Wang, Fei Front Psychiatry Psychiatry BACKGROUND: Depression is a widespread mental disorder that affects a significant portion of the population. However, the assessment of depression is often subjective, relying on standard questions or interviews. Acoustic features have been suggested as a reliable and objective alternative for depression assessment. Therefore, in this study, we aim to identify and explore voice acoustic features that can effectively and rapidly predict the severity of depression, as well as investigate the potential correlation between specific treatment options and voice acoustic features. METHODS: We utilized voice acoustic features correlated with depression scores to train a prediction model based on artificial neural network. Leave-one-out cross-validation was performed to evaluate the performance of the model. We also conducted a longitudinal study to analyze the correlation between the improvement of depression and changes in voice acoustic features after an Internet-based cognitive-behavioral therapy (ICBT) program consisting of 12 sessions. RESULTS: Our study showed that the neural network model trained based on the 30 voice acoustic features significantly correlated with HAMD scores can accurately predict the severity of depression with an absolute mean error of 3.137 and a correlation coefficient of 0.684. Furthermore, four out of the 30 features significantly decreased after ICBT, indicating their potential correlation with specific treatment options and significant improvement in depression (p < 0.05). CONCLUSION: Voice acoustic features can effectively and rapidly predict the severity of depression, providing a low-cost and efficient method for screening patients with depression on a large scale. Our study also identified potential acoustic features that may be significantly related to specific treatment options for depression. Frontiers Media S.A. 2023-06-21 /pmc/articles/PMC10320390/ /pubmed/37415683 http://dx.doi.org/10.3389/fpsyt.2023.1195276 Text en Copyright © 2023 Wang, Liang, Zhang, Xu, Liu, Fang, Zhang, Wei, Liu, Zhu, Zhang and Wang. https://creativecommons.org/licenses/by/4.0/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) and the copyright owner(s) 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. |
spellingShingle | Psychiatry Wang, Yang Liang, Lijuan Zhang, Zhongguo Xu, Xiao Liu, Rongxun Fang, Hanzheng Zhang, Ran Wei, Yange Liu, Zhongchun Zhu, Rongxin Zhang, Xizhe Wang, Fei Fast and accurate assessment of depression based on voice acoustic features: a cross-sectional and longitudinal study |
title | Fast and accurate assessment of depression based on voice acoustic features: a cross-sectional and longitudinal study |
title_full | Fast and accurate assessment of depression based on voice acoustic features: a cross-sectional and longitudinal study |
title_fullStr | Fast and accurate assessment of depression based on voice acoustic features: a cross-sectional and longitudinal study |
title_full_unstemmed | Fast and accurate assessment of depression based on voice acoustic features: a cross-sectional and longitudinal study |
title_short | Fast and accurate assessment of depression based on voice acoustic features: a cross-sectional and longitudinal study |
title_sort | fast and accurate assessment of depression based on voice acoustic features: a cross-sectional and longitudinal study |
topic | Psychiatry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320390/ https://www.ncbi.nlm.nih.gov/pubmed/37415683 http://dx.doi.org/10.3389/fpsyt.2023.1195276 |
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