Published online Sep 28, 2025. doi: 10.4329/wjr.v17.i9.109116
Revised: May 31, 2025
Accepted: August 13, 2025
Published online: September 28, 2025
Processing time: 150 Days and 4.4 Hours
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, in
To address these limitations, this review aims to systematically analyze recent ad
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.
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.
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.