ArticleScientific reports2018
Sleep staging using nocturnal sound analysis.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 65 citations in OpenAlex.
- Radar Multiple Bin Selection for Breathing and Heart Rate Monitoring in Acute Stroke Patients in a Clinical Setting.Sensors (Basel, Switzerland) · 2025Article
- Estimating Sleep-Stage Distribution from Respiratory Sounds via Deep Audio Segmentation.Sensors (Basel, Switzerland) · 2025Article
- High-Precision Contactless Stereo Acoustic Monitoring in Polysomnographic Studies of Children.Sensors (Basel, Switzerland) · 2025Article
- Feasibility of Radar Vital Sign Monitoring Using Multiple Range Bin Selection.Sensors (Basel, Switzerland) · 2025Article
- A perspective on automated rapid eye movement sleep assessment.Journal of sleep research · 2025Review
- Development of generalizable automatic sleep staging using heart rate and movement based on large databases.Biomedical engineering letters · 2023Article
- Toward Personalized Medicine Approaches for Parkinson Disease Using Digital Technologies.JMIR formative research · 2023Review
- Alternatives to Polysomnography for the Diagnosis of Pediatric Obstructive Sleep Apnea.Diagnostics (Basel, Switzerland) · 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
- Spotlight on Sleep Stage Classification Based on EEG.Nature and science of sleep · 2023Article
- Evaluating Prediction Models of Sleep Apnea From Smartphone-Recorded Sleep Breathing Sounds.JAMA otolaryngology-- head & neck surgery · 2022Article
- Smart Speakers: The Next Frontier in mHealth.JMIR mHealth and uHealth · 2022Article
- End-to-End Sleep Staging Using Nocturnal Sounds from Microphone Chips for Mobile Devices.Nature and science of sleep · 2022Article
- Article
- Evaluating consumer and clinical sleep technologies: an American Academy of Sleep Medicine update.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2021Article
- Photoplethysmography in Normal and Pathological Sleep.Sensors (Basel, Switzerland) · 2021Article
- Sleep/Wakefulness Detection Using Tracheal Sounds and Movements.Nature and science of sleep · 2020Article
- Sleep stage classification from heart-rate variability using long short-term memory neural networks.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sleep staging is essential for evaluating sleep and its disorders. Most sleep studies today incorporate contact sensors that may interfere with natural sleep and may bias results. Moreover, the availability of sleep studies is limited, and many people with sleep disorders remain undiagnosed. Here, we present a pioneering approach for rapid eye movement (REM), non-REM, and wake staging (macro-sleep stages, MSS) estimation based on sleep sounds analysis. Our working hypothesis is that the properties of sleep sounds, such as breathing and movement, within each MSS are different. We recorded audio signals, using non-contact microphones, of 250 patients referred to a polysomnography (PSG) study in a sleep laboratory. We trained an ensemble of one-layer, feedforward neural network classifiers fed by time-series of sleep sounds to produce real-time and offline analyses. The audio-based system was validated and produced an epoch-by-epoch (standard 30-sec segments) agreement with PSG of 87% with Cohen's kappa of 0.7. This study shows the potential of audio signal analysis as a simple, convenient, and reliable MSS estimation without contact sensors.
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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.