ArticleJournal of medical Internet research2023
Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation.
Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
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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
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Pooled it
- Evaluation of a sleep sound analysis smartphone app for home obstructive sleep apnea screening among community-dwelling Chinese adults with hypertension.Sleep & breathing = Schlaf & Atmung · 2026Trial
- Associations between weather variables and sleep architecture: A large-scale analysis using consumer sleep data.Sleep & breathing = Schlaf & Atmung · 2026Article
- Non-contact on-device detection of obstructive sleep apnea from infrared video.Nature communications · 2026Article
- Continuous Monitoring of Positive Airway Pressure Therapy with a Smartphone-Based Home Sleep Apnea Test.Medicina (Kaunas, Lithuania) · 2026Article
- Cross-Modality Transfer Learning from PSG to FMCW Radar for Event-Level Apnea-Hypopnea Segmentation.Bioengineering (Basel, Switzerland) · 2026Article
- Fine-Grained and Lightweight OSA Detection: A CRNN-Based Model for Precise Temporal Localization of Respiratory Events in Sleep Audio.Diagnostics (Basel, Switzerland) · 2026Article
- Estimating Sleep-Stage Distribution from Respiratory Sounds via Deep Audio Segmentation.Sensors (Basel, Switzerland) · 2025Article
- Proposition of a new, minimally-invasive, software smartphone device to predict sleep apnea and its severity.Sleep & breathing = Schlaf & Atmung · 2025Observational
- Detection of sleep apnea using smartphone-embedded inertial measurement unit.Scientific reports · 2025Article
- Diagnostic Modalities in Sleep Disordered Breathing: Current and Emerging Technology and Its Potential to Transform Diagnostics.Respirology (Carlton, Vic.) · 2025Review
- Advancements in Obstructive Sleep Apnea Diagnosis and Screening Through Artificial Intelligence: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- ApneaWhisper: Transformer-Based Audio Segmentation for Fine-Grained Non-Invasive Sleep Apnea Detection.Nature and science of sleep · 2025Article
- Real-Time Snoring Detection Using Deep Learning: A Home-Based Smartphone Approach for Sleep Monitoring.Nature and science of sleep · 2025Article
- SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals.Sensors (Basel, Switzerland) · 2024Article
- Perspective: A resident's role in promoting safe machine-learning tools in sleep medicine.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMultinight monitoring can be helpful for the diagnosis and management of obstructive sleep apnea (OSA). For this purpose, it is necessary to be able to detect OSA in real time in a noisy home environment. Sound-based OSA assessment holds great potential since it can be integrated with smartphones to provide full noncontact monitoring of OSA at home.
objectiveThe purpose of this study is to develop a predictive model that can detect OSA in real time, even in a home environment where various noises exist.
methodsThis study included 1018 polysomnography (PSG) audio data sets, 297 smartphone audio data sets synced with PSG, and a home noise data set containing 22,500 noises to train the model to predict breathing events, such as apneas and hypopneas, based on breathing sounds that occur during sleep. The whole breathing sound of each night was divided into 30-second epochs and labeled as "apnea," "hypopnea," or "no-event," and the home noises were used to make the model robust to a noisy home environment. The performance of the prediction model was assessed using epoch-by-epoch prediction accuracy and OSA severity classification based on the apnea-hypopnea index (AHI).
resultsEpoch-by-epoch OSA event detection showed an accuracy of 86% and a macro F
conclusionsOur study presents a real-time epoch-by-epoch OSA detector that works in a variety of noisy home environments. Based on this, additional research is needed to verify the usefulness of various multinight monitoring and real-time diagnostic technologies in the home environment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.