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9/26/2025 10:07:28 AM | Browse: 281 | Download: 131
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Received |
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2025-04-30 07:53 |
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Peer-Review Started |
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2025-04-30 07:53 |
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To Make the First Decision |
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Return for Revision |
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2025-05-17 08:50 |
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Revised |
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2025-05-31 02:17 |
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Second Decision |
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2025-08-13 05:20 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-08-13 09:37 |
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Articles in Press |
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2025-08-13 09:37 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-09-05 06:36 |
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Publish the Manuscript Online |
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2025-09-26 10:05 |
| ISSN |
1949-8470 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Engineering, Biomedical |
| Manuscript Type |
Systematic Reviews |
| Article Title |
Deep learning approaches for image-based snoring sound analysis in the diagnosis of obstructive sleep apnea-hypopnea syndrome: A systematic review
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Li Ding, Jian-Xin Peng and Yu-Jun Song |
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Natural Science Foundation of China |
11974121 |
| Talent Research Fund of Hefei University |
24RC08 |
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| Corresponding Author |
Jian-Xin Peng, Professor, School of Physics and Optoelectronics, South China University of Technology, wuhan, Guangzhou 510640, Guangdong Province, China. phjxpeng@163.com |
| Key Words |
Obstructive sleep apnea hypopnea syndrome; Snoring sounds; Image; Neural network; Systematic review |
| Core Tip |
This systematic review summarizes recent advances in the use of image-based deep learning models for snoring sound analysis in the diagnosis of obstructive sleep apnea-hypopnea syndrome (OSAHS). The review highlights the role of time–frequency representations and deep learning architectures in classifying snoring types and estimating severity of OSAHS. The work also identifies current challenges in data standardization, model interpretability, and clinical integration, providing direction for future research. |
| Publish Date |
2025-09-26 10:05 |
| Citation |
<p>Ding L, Peng JX, Song YJ. Deep learning approaches for image-based snoring sound analysis in the diagnosis of obstructive sleep apnea-hypopnea syndrome: A systematic review. <i>World J Radiol</i> 2025; 17(9): 109116</p> |
| URL |
https://www.wjgnet.com/1949-8470/full/v17/i9/109116.htm |
| DOI |
https://dx.doi.org/10.4329/wjr.v17.i9.109116 |
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