What type of study is this?

This is a Systematic Review study.

What question did this study set out to answer?

Evaluate AI and machine learning applications in snoring sound analysis for detecting obstructive sleep apnea and classifying obstruction sites.

January 14, 2026Otolaryngology0 citations

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Artificial Intelligence in Snoring Sound Analysis: OSA Detection and Obstruction Site Classification, a Systematic Review

FTFrancesco TartagliaUniversity of Parma GPGian Marco PaceCasa di Cura Columbus FGFrancesco GiombiHumanitas University

Key Points

Evaluate AI and machine learning applications in snoring sound analysis for detecting obstructive sleep apnea and classifying obstruction sites.
Conducted systematic review of literature from multiple databases including PubMed and Scopus.
Screened 591 records, including 42 studies based on the PICO framework and PRISMA guidelines.
Assessed methodological quality using PROBAST and focused on ML models, feature extraction methods, and performance metrics.
ML models achieved up to 98.6% accuracy in snore classification and OSA detection.
Key features included mel-frequency cepstral coefficients and wavelet transforms.
VOTE classification showed UARs ranged from 49% to 75%, with accuracy improvements up to 95.5% when targeted features were used.

Abstract

Abstract Objective The aim of the systematic review is to evaluate the application of machine learning (ML) and artificial intelligence (AI) models in the analysis of snoring sounds for the detection of obstructive sleep apnea (OSA) and the classification of obstruction sites based on the Velum, Oropharynx, Tongue, and Epiglottis (VOTE) system. Data Sources We conducted a comprehensive literature search in PubMed, Scopus, Ovid, Cochrane, and Web of Science databases from 2011 to November 30, 2024. The search included terms related to snoring, machine learning, artificial intelligence, and acoustic analysis. Review Methods The review was structured using the PICO framework and adhered to Preferred Reporting Items for Systematic Reviews and Meta‐analyses (PRISMA) 2020 guidelines. A total of 591 records were identified, and 42 studies were included after screening and full‐text review. Methodological quality was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool, and data extraction focused on ML models, data sets, feature extraction methods, and performance metrics. Results ML models such as support vector machine (SVMs), convolutional neural networks (CNNs), and hybrid deep learning architectures achieved high accuracy (up to 98.6%) in snore classification and OSA detection. Key features included mel‐frequency cepstral coefficients (MFCCs), wavelet transforms, and deep spectral representations. Several studies attempted VOTE classification, reporting unweighted average recalls (UARs) of 49% to 75% with baseline pipelines, and up to 87.5% to 94.7% UAR or 92.2 to 95.5% accuracy when using targeted features with SVM or k‐nearest neighbor (kNN). Challenges included limited external validation, data set imbalance, and noise interference. Conclusion AI‐driven snore analysis holds potential for noninvasive OSA screening and anatomical site classification. Future work should focus on multimodal data integration, real‐world validation, and model generalizability to support clinical translation.

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Artificial Intelligence in Snoring Sound Analysis: OSA Detection and Obstruction Site Classification, a Systematic Review

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