ArticleClinical and experimental otorhinolaryngology2019
Prediction of Obstructive Sleep Apnea Based on Respiratory Sounds Recorded Between Sleep Onset and Sleep Offset.
Article in Clinical and experimental otorhinolaryngology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- A Classifying Model of Obstructive Sleep Apnea Based on Heart Rate Variability in a Large Korean Population.Journal of Korean medical science · 2023Trial
- Non-contact diagnosis of obstructive sleep apnea using impulse-radio ultra-wideband radar.Scientific reports · 2020Trial
- Short-Term Prognosis in Acute Asthma Exacerbations: A Comparative Evaluation of Machine Learning Models Using Spirometry and Tracheal Respiratory Sound Analysis.International journal of general medicine · 2026Article
- Diagnostic Modalities in Sleep Disordered Breathing: Current and Emerging Technology and Its Potential to Transform Diagnostics.Respirology (Carlton, Vic.) · 2025Review
- Enhanced machine learning approaches for OSA patient screening: model development and validation study.Scientific reports · 2024Article
- Evaluating Prediction Models of Sleep Apnea From Smartphone-Recorded Sleep Breathing Sounds.JAMA otolaryngology-- head & neck surgery · 2022Article
- Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2021Article
- The Endocannabinoid System May Modulate Sleep Disorders in Aging.Current neuropharmacology · 2020Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectivesTo develop a simple algorithm for prescreening of obstructive sleep apnea (OSA) on the basis of respiratory sounds recorded during polysomnography during all sleep stages between sleep onset and offset.
methodsPatients who underwent attended, in-laboratory, full-night polysomnography were included. For all patients, audio recordings were performed with an air-conduction microphone during polysomnography. Analyses included all sleep stages (i.e., N1, N2, N3, rapid eye movement, and waking). After noise reduction preprocessing, data were segmented into 5-s windows and sound features were extracted. Prediction models were established and validated with 10-fold cross-validation by using simple logistic regression. Binary classifications were separately conducted for three different threshold criteria at apnea hypopnea index (AHI) of 5, 15, or 30. Prediction model characteristics, including accuracy, sensitivity, specificity, positive predictive value (precision), negative predictive value, and area under the curve (AUC) of the receiver operating characteristic were computed.
resultsA total of 116 subjects were included; their mean age, body mass index, and AHI were 50.4 years, 25.5 kg/m2 , and 23.0/hr, respectively. A total of 508 sound features were extracted from respiratory sounds recorded throughout sleep. Accuracies of binary classifiers at AHIs of 5, 15, and 30 were 82.7%, 84.4%, and 85.3%, respectively. Prediction performances for the classifiers at AHIs of 5, 15, and 30 were AUC, 0.83, 0.901, and 0.91; sensitivity, 87.5%, 81.6%, and 60%; and specificity, 67.8%, 87.5%, and 94.1%. Respective precision values of the classifiers were 89.5%, 87.5%, and 78.2% for AHIs of 5, 15, and 30.
conclusionThis study showed that our binary classifier predicted patients with AHI of ≥15 with sensitivity and specificity of >80% by using respiratory sounds during sleep. Since our prediction model included all sleep stage data, algorithms based on respiratory sounds may have a high value for prescreening OSA with mobile devices.
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