ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2021
Automatic classification of excitation location of snoring sounds.
Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Fine-Grained and Lightweight OSA Detection: A CRNN-Based Model for Precise Temporal Localization of Respiratory Events in Sleep Audio.Diagnostics (Basel, Switzerland) · 2026Article
- Sleep stage classification using instantaneous heart and breathing rates with a TCN-BiGRU-SE model.Frontiers in bioengineering and biotechnology · 2026Article
- Multimodal physiological signal learning for clinical sleep respiratory event recognition.Frontiers in neurology · 2026Article
- A flexible catheter-based sensor array for upper airway soft tissues pressure monitoring.Nature communications · 2025Article
- MLS-Net: An Automatic Sleep Stage Classifier Utilizing Multimodal Physiological Signals in Mice.Biosensors · 2024Article
- Predicting upper airway collapse sites found in drug-induced sleep endoscopy from clinical data and snoring sounds in patients with obstructive sleep apnea: a prospective clinical study.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2022Article
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5 authors.
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Abstract
STUDY
objectivesFor surgical treatment of patients with obstructive sleep apnea-hypopnea syndrome, it is crucial to locate accurately the obstructive sites in the upper airway; however, noninvasive methods for locating the obstructive sites have not been well explored. Snoring, as the cardinal symptom of obstructive sleep apnea-hypopnea syndrome, should contain information that reflects the state of the upper airway. Through the classification of snores produced at four different locations, this study aimed to test the hypothesis that snores generated by various obstructive sites differ.
methodsWe trained and tested our model on a public data set that comprised 219 participants. For each snore episode, an acoustic and a physiological feature were extracted and concatenated, forming a 59-dimensional fusion feature. A principal component analysis and a support machine vector were used for dimensional reduction and snore classification. The performance of the proposed model was evaluated using several metrics: sensitivity, precision, specificity, area under the receiver operating characteristic curve, and F1 score.
resultsThe unweighted average values of sensitivity, precision, specificity, area under the curve, and F1 were 86.36%, 89.09%, 96.4%, 87.9%, and 87.63%, respectively. The model achieved 98.04%, 80.56%, 72.73%, and 94.12% sensitivity for types V (velum), O (oropharyngeal), T (tongue), and E (epiglottis) snores.
conclusionsThe characteristics of snores are related to the state of the upper airway. The machine-learning-based model can be used to locate the vibration sites in the upper airway.
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