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. World J Radiol 2025; 17(9): 109116 [PMID: 41025059 DOI: 10.4329/wjr.v17.i9.109116]
Corresponding Author of This Article
Jian-Xin Peng, Professor, School of Physics and Optoelectronics, South China University of Technology, No. 381 Wushan Road, Tianhe District, Guangzhou 510640, Guangdong Province, China. phjxpeng@163.com
Research Domain of This Article
Engineering, Biomedical
Article-Type of This Article
Systematic Reviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
World J Radiol. Sep 28, 2025; 17(9): 109116 Published online Sep 28, 2025. doi: 10.4329/wjr.v17.i9.109116
Deep learning approaches for image-based snoring sound analysis in the diagnosis of obstructive sleep apnea-hypopnea syndrome: A systematic review
Li Ding, Jian-Xin Peng, Yu-Jun Song
Li Ding, School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, Anhui Province, China
Jian-Xin Peng, Yu-Jun Song, School of Physics and Optoelectronics, South China University of Technology, Guangzhou 510640, Guangdong Province, China
Author contributions: Peng JX, Ding L designed the research study; Song YJ, Ding L performed the literature review and conducted the data analysis; Ding L wrote the manuscript.
Supported by the National Natural Science Foundation of China, No. 11974121; and Talent Research Fund of Hefei University, No. 24RC08.
Conflict-of-interest statement: The authors declare no conflicts of interest related to this work.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
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/
Corresponding author: Jian-Xin Peng, Professor, School of Physics and Optoelectronics, South China University of Technology, No. 381 Wushan Road, Tianhe District, Guangzhou 510640, Guangdong Province, China. phjxpeng@163.com
Received: April 30, 2025 Revised: May 31, 2025 Accepted: August 13, 2025 Published online: September 28, 2025 Processing time: 150 Days and 4.4 Hours
Abstract
BACKGROUND
Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a highly prevalent sleep-related respiratory disorder associated with serious health risks. Although polysomnography is the clinical gold standard for diagnosis, it is expensive, inconvenient, and unsuitable for population-level screening due to the need for professional scoring and overnight monitoring.
AIM
To address these limitations, this review aims to systematically analyze recent advances in deep learning–based OSAHS detection methods using snoring sounds, particularly focusing on graphical signal representations and network architectures.
METHODS
A comprehensive literature search was conducted following the PRISMA 2009 guidelines, covering publications from 2010 to 2025. Studies were included based on predefined criteria involving the use of deep learning models on snoring sounds transformed into graphical representations such as spectrograms and scalograms. A total of 14 studies were selected for in-depth analysis.
RESULTS
This review summarizes the types of signal modalities, datasets, feature extraction methods, and classification frameworks used in the current literatures. The strengths and limitations of different deep network architectures are evaluated.
CONCLUSION
Challenges such as dataset variability, generalizability, model interpretability, and deployment feasibility are also discussed. Future directions highlight the importance of explainable artificial intelligence and domain-adaptive learning for clinically viable OSAHS diagnostic tools.
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.
