ArticleNature and science of sleep2023
Sleep Apnea Detection by Tracheal Motion and Sound, and Oximetry via Application of Deep Neural Networks.
Article in Nature and science of sleep, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- From classic to cutting-edge: technological approaches to respiratory physiological signals in assessing sleep-disordered breathing.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2026Review
- Anatomical Determinants of Tracheal Breathing Sounds: A Computational Study of Airway Narrowing and Obstructive Sleep Apnea.Diagnostics (Basel, Switzerland) · 2025Article
- Diagnostic Modalities in Sleep Disordered Breathing: Current and Emerging Technology and Its Potential to Transform Diagnostics.Respirology (Carlton, Vic.) · 2025Review
- Association of estimated pulse wave velocity with all-cause mortality and cardiovascular mortality in obstructive sleep apnea patients: results from NHANES.Frontiers in cardiovascular medicine · 2025Article
- OSA diagnosis goes wearable: are the latest devices ready to shine?Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Sleep apnea (SA) is highly prevalent, but under diagnosed due to inaccessibility of sleep testing. To address this issue, portable devices for home sleep testing have been developed to provide convenience with reasonable accuracy in diagnosing SA. The objective of this study was to test the validity a novel portable sleep apnea testing device, BresoDX1, in SA diagnosis, via recording of trachea-sternal motion, tracheal sound and oximetry. Patients and Methods: Adults with a suspected sleep disorder were recruited to undergo in-laboratory polysomnography (PSG) and a simultaneous BresoDX1 recording. Data from BresoDX1 were collected and features related to breathing sounds, body motions and oximetry were extracted. A deep neural network (DNN) model was trained with 61-second epochs of the extracted features to detect apneas and hypopneas from which an apnea-hypopnea index (AHI) was calculated. The AHI estimated by BresoDX1 (AHI Results: Two-hundred thirty-three participants (mean ± SD) 50 ± 16 years of age, with BMI of 29.8 ± 6.6 and AHI of 19.5 ± 22.7, were included. There was a strong relationship between AHI Conclusion: We conclude that the DNN model we developed via recording and analyses of trachea-sternal motion and sound along with oximetry provides an accurate estimate of the AHI
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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.