Prediction of the obstruction sites in the upper airway in sleep-disordered breathing based on snoring sound parameters: a systematic review

• Published in: Sleep medicine Vol. 88; pp. 116 - 133
• Main Authors: Huang, Zhengfei, Aarab, Ghizlane, Ravesloot, Madeline J.L., Zhou, Ning, Bosschieter, Pien F.N., van Selms, Maurits K.A., den Haan, Chantal, de Vries, Nico, Lobbezoo, Frank, Hilgevoord, Antonius A.J.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-12-2021
• Subjects: Airway Obstruction; Frequency; Humans; Machine learning; Nose; Prediction model; Sleep Apnea Syndromes - diagnosis; Sleep Apnea, Obstructive; Snoring; Snoring sound; Sound; Upper airway; Frequency; Snoring sound; Prediction model; Machine learning; Upper airway
• ISSN: 1389-9457; 1878-5506
• DOI: 10.1016/j.sleep.2021.10.015

Identification of the obstruction site in the upper airway may help in treatment selection for patients with sleep-disordered breathing. Because of limitations of existing techniques, there is a continuous search for more feasible methods. Snoring sound parameters were hypothesized to be potential predictors of the obstruction site. Therefore, this review aims to i) investigate the association between snoring sound parameters and the obstruction sites; and ii) analyze the methodology of reported prediction models of the obstruction sites.
The literature search was conducted in PubMed, Embase.com, CENTRAL, Web of Science, and Scopus in collaboration with a medical librarian. Studies were eligible if they investigated the associations between snoring sound parameters and the obstruction sites, and/or reported prediction models of the obstruction sites based on snoring sound.
Of the 1016 retrieved references, 28 eligible studies were included. It was found that the characteristic frequency components generated from lower-level obstructions of the upper airway were higher than those generated from upper-level obstructions. Prediction models were built mainly based on snoring sound parameters in frequency domain. The reported accuracies ranged from 60.4% to 92.2%.
Available evidence points toward associations between the snoring sound parameters in the frequency domain and the obstruction sites in the upper airway. It is promising to build a prediction model of the obstruction sites based on snoring sound parameters and participant characteristics, but so far snoring sound analysis does not seem to be a viable diagnostic modality for treatment selection.
•The snoring sound parameters in the frequency domain are associated with the obstruction sites in the upper airway.•Associations between the obstruction sites and snoring sound parameters in the temporal and intensity domains are unclear.•It is promising to build a prediction model of the obstruction sites based on snoring sound parameters.•Snoring sound analysis so far does not seem to be a viable diagnostic modality for treatment selection.