Deep learning approaches for image-based snoring sound analysis in the diagnosis of obstructive sleep apnea-hypopnea syndrome: A systematic review.

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.