Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026
Artificial Intelligence in Snoring Sound Analysis: OSA Detection and Obstruction Site Classification, a Systematic Review.
СтатьяСистематический обзорНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Tartaglia Francesco Carlo, Pace Gian Marco, Giombi Francesco, Nava Giorgia, Motisi Annagiulia, Maffoni Niccolo, Mancin Stefano, Berdin Nicolò, Sandri Giulio, Cerri Luca, Serra Egidio, Mercante Giuseppe, Spriano Giuseppe, Malvezzi Luca, Paderno Alberto
Аннотация
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.
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.