ArticleGerman medical science : GMS e-journal2019
Automated sleep stage classification based on tracheal body sound and actigraphy.
Article in German medical science : GMS e-journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 citations in OpenAlex.
- Home-testing devices for diagnosing obstructive sleep apnoea hypopnoea syndrome - a systematic review and economic evaluation.Health technology assessment (Winchester, England) · 2026Pooled it
- A Systematic Review of Sensing Technologies for Wearable Sleep Staging.Sensors (Basel, Switzerland) · 2021Pooled it
- Sleep prediction using data from oximeter, accelerometer and snoring for portable monitor obstructive sleep apnea diagnosis.Scientific reports · 2024Article
- 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
- End-to-End Sleep Staging Using Nocturnal Sounds from Microphone Chips for Mobile Devices.Nature and science of sleep · 2022Article
- Complexity of Body Movements during Sleep in Children with Autism Spectrum Disorder.Entropy (Basel, Switzerland) · 2021Article
- Sleep/Wakefulness Detection Using Tracheal Sounds and Movements.Nature and science of sleep · 2020Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The current gold standard for assessment of most sleep disorders is the in-laboratory polysomnography (PSG). This approach produces high costs and inconveniences for the patients. An accessible and simple preliminary screening method to diagnose the most common sleep disorders and to decide whether a PSG is necessary or not is therefore desirable. A minimalistic type-4 monitoring system which utilized tracheal body sound and actigraphy to accurately diagnose the obstructive sleep apnea syndrome was previously developed. To further improve the diagnostic ability of said system, this study aims to examine if it is possible to perform automated sleep staging utilizing body sound to extract cardiorespiratory features and actigraphy to extract movement features. A linear discriminant classifier based on those features was used for automated sleep staging using the type-4 sleep monitor. For validation 53 subjects underwent a full-night screening at Ulm University Hospital using the developed sleep monitor in addition to polysomnography. To assess sleep stages from PSG, a trained technician manually evaluated EEG, EOG, and EMG recordings. The classifier reached 86.9% accuracy and a Kappa of 0.69 for sleep/wake classification, 76.3% accuracy and a Kappa of 0.42 for Wake/REM/NREM classification, and 56.5% accuracy and a Kappa of 0.36 for Wake/REM/light sleep/deep sleep classification. For the calculation of sleep efficiency (SE), a coefficient of determination r
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