ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2019
Tracheal Sound Analysis Using a Deep Neural Network to Detect Sleep Apnea.
Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 66 citations in OpenAlex.
- Isfahan Artificial Intelligence Event 2024, Challenge I: Respiratory Depression Detection.Journal of medical signals and sensors · 2026Article
- Developing an Empirical Acoustic Transfer Function Model for the Upper Airway.Annals of biomedical engineering · 2026Article
- Real-time airway monitoring system using binary classification model based on respiratory sounds of rabbits with a tracheostomy tube.Scientific reports · 2025Article
- Snoring, obstructive sleep apnea, and upper respiratory tract infection in elementary school children in Japan.Sleep & breathing = Schlaf & Atmung · 2024Article
- Breathing sounds analysis system for early detection of airway problems in patients with a tracheostomy tube.Scientific reports · 2023Article
- Systematic review of automated sleep apnea detection based on physiological signal data using deep learning algorithm: a meta-analysis approach.Biomedical engineering letters · 2023Review
- Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation.Journal of medical Internet research · 2023Article
- A novel system that continuously visualizes and analyzes respiratory sounds to promptly evaluate upper airway abnormalities: a pilot study.Journal of clinical monitoring and computing · 2022Article
- End-to-End Sleep Staging Using Nocturnal Sounds from Microphone Chips for Mobile Devices.Nature and science of sleep · 2022Article
- At-home wireless monitoring of acute hemodynamic disturbances to detect sleep apnea and sleep stages via a soft sternal patch.Science advances · 2021Article
- PSG-Audio, a scored polysomnography dataset with simultaneous audio recordings for sleep apnea studies.Scientific data · 2021Article
- The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise.Sensors (Basel, Switzerland) · 2020Review
- Acoustic characterization of upper airway variations from wakefulness to sleep with respect to obstructive sleep apnea.Medical & biological engineering & computing · 2020Article
- Sleep/Wakefulness Detection Using Tracheal Sounds and Movements.Nature and science of sleep · 2020Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesPortable devices for home sleep apnea testing are often limited by their inability to discriminate sleep/wake status, possibly resulting in underestimations. Tracheal sound (TS), which can be visualized as a spectrogram, carries information about apnea/hypopnea and sleep/wake status. We hypothesized that image analysis of all-night TS recordings by a deep neural network (DNN) would be capable of detecting breathing events and classifying sleep/wake status. The aim of this study is to develop a DNN-based system for sleep apnea testing and validate it using a large sampling of polysomnography (PSG) data.
methodsPSG examinations for the evaluation of sleep-disordered breathing (SDB) were performed for 1,852 patients: 1,548 PSG records were used to develop the system, and the remaining 304 records were used for validation. TS spectrogram images were obtained every 60 seconds and labeled with the PSG scoring results (breathing event and sleep/wake status), then introduced to DNN learning. Two different DNNs were trained for breathing status and sleep/wake status, respectively.
resultsA DNN with convolutional layers showed the best performance for discriminating breathing status. The same DNN structure was trained for sleep/wake discrimination. In the validation study, the DNN analysis was capable of discriminating the sleep/wake status with reasonable accuracy. The diagnostic sensitivity, specificity, and area under the receiver operating characteristic curves for diagnosis of SDB with apnea-hypopnea index of > 5, 15, and 30 were 0.98, 0.76, and 0.99; 0.97, 0.90, and 0.99; and 0.92, 0.94, and 0.98, respectively.
conclusionsThe developed system using the TS DNN analysis has a good performance for SDB testing. CITATION: Nakano H, Furukawa T, Tanigawa T. Tracheal sound analysis using a deep neural network to detect sleep apnea. J Clin Sleep Med. 2019;15(8): 1125-1133.
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