Cargando…

Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire

Many wearables allow physiological data acquisition in sleep and enable clinicians to assess sleep outside of sleep labs. Belun Sleep Platform (BSP) is a novel neural network-based home sleep apnea testing system utilizing a wearable ring device to detect obstructive sleep apnea (OSA). The objective...

Descripción completa

Detalles Bibliográficos
Autores principales: Yeh, Eric, Wong, Eileen, Tsai, Chih-Wei, Gu, Wenbo, Chen, Pai-Lien, Leung, Lydia, Wu, I-Chen, Strohl, Kingman P., Folz, Rodney J., Yar, Wail, Chiang, Ambrose A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8504733/
https://www.ncbi.nlm.nih.gov/pubmed/34634070
http://dx.doi.org/10.1371/journal.pone.0258040
_version_ 1784581379139305472
author Yeh, Eric
Wong, Eileen
Tsai, Chih-Wei
Gu, Wenbo
Chen, Pai-Lien
Leung, Lydia
Wu, I-Chen
Strohl, Kingman P.
Folz, Rodney J.
Yar, Wail
Chiang, Ambrose A.
author_facet Yeh, Eric
Wong, Eileen
Tsai, Chih-Wei
Gu, Wenbo
Chen, Pai-Lien
Leung, Lydia
Wu, I-Chen
Strohl, Kingman P.
Folz, Rodney J.
Yar, Wail
Chiang, Ambrose A.
author_sort Yeh, Eric
collection PubMed
description Many wearables allow physiological data acquisition in sleep and enable clinicians to assess sleep outside of sleep labs. Belun Sleep Platform (BSP) is a novel neural network-based home sleep apnea testing system utilizing a wearable ring device to detect obstructive sleep apnea (OSA). The objective of the study is to assess the performance of BSP for the evaluation of OSA. Subjects who take heart rate-affecting medications and those with non-arrhythmic comorbidities were included in this cohort. Polysomnography (PSG) studies were performed simultaneously with the Belun Ring in individuals who were referred to the sleep lab for an overnight sleep study. The sleep studies were manually scored using the American Academy of Sleep Medicine Scoring Manual (version 2.4) with 4% desaturation hypopnea criteria. A total of 78 subjects were recruited. Of these, 45% had AHI < 5; 18% had AHI 5–15; 19% had AHI 15–30; 18% had AHI ≥ 30. The Belun apnea-hypopnea index (bAHI) correlated well with the PSG-AHI (r = 0.888, P < 0.001). The Belun total sleep time (bTST) and PSG-TST had a high correlation coefficient (r = 0.967, P < 0.001). The accuracy, sensitivity, specificity in categorizing AHI ≥ 15 were 0.808 [95% CI, 0.703–0.888], 0.931 [95% CI, 0.772–0.992], and 0.735 [95% CI, 0.589–0.850], respectively. The use of beta-blocker/calcium-receptor antagonist and the presence of comorbidities did not negatively affect the sensitivity and specificity of BSP in predicting OSA. A diagnostic algorithm combining STOP-Bang cutoff of 5 and bAHI cutoff of 15 events/h demonstrated an accuracy, sensitivity, specificity of 0.938 [95% CI, 0.828–0.987], 0.944 [95% CI, 0.727–0.999], and 0.933 [95% CI, 0.779–0.992], respectively, for the diagnosis of moderate to severe OSA. BSP is a promising testing tool for OSA assessment and can potentially be incorporated into clinical practices for the identification of OSA. Trial registration: ClinicalTrial.org NCT03997916 https://clinicaltrials.gov/ct2/show/NCT03997916?term=belun+ring&draw=2&rank=1
format Online
Article
Text
id pubmed-8504733
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-85047332021-10-12 Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire Yeh, Eric Wong, Eileen Tsai, Chih-Wei Gu, Wenbo Chen, Pai-Lien Leung, Lydia Wu, I-Chen Strohl, Kingman P. Folz, Rodney J. Yar, Wail Chiang, Ambrose A. PLoS One Research Article Many wearables allow physiological data acquisition in sleep and enable clinicians to assess sleep outside of sleep labs. Belun Sleep Platform (BSP) is a novel neural network-based home sleep apnea testing system utilizing a wearable ring device to detect obstructive sleep apnea (OSA). The objective of the study is to assess the performance of BSP for the evaluation of OSA. Subjects who take heart rate-affecting medications and those with non-arrhythmic comorbidities were included in this cohort. Polysomnography (PSG) studies were performed simultaneously with the Belun Ring in individuals who were referred to the sleep lab for an overnight sleep study. The sleep studies were manually scored using the American Academy of Sleep Medicine Scoring Manual (version 2.4) with 4% desaturation hypopnea criteria. A total of 78 subjects were recruited. Of these, 45% had AHI < 5; 18% had AHI 5–15; 19% had AHI 15–30; 18% had AHI ≥ 30. The Belun apnea-hypopnea index (bAHI) correlated well with the PSG-AHI (r = 0.888, P < 0.001). The Belun total sleep time (bTST) and PSG-TST had a high correlation coefficient (r = 0.967, P < 0.001). The accuracy, sensitivity, specificity in categorizing AHI ≥ 15 were 0.808 [95% CI, 0.703–0.888], 0.931 [95% CI, 0.772–0.992], and 0.735 [95% CI, 0.589–0.850], respectively. The use of beta-blocker/calcium-receptor antagonist and the presence of comorbidities did not negatively affect the sensitivity and specificity of BSP in predicting OSA. A diagnostic algorithm combining STOP-Bang cutoff of 5 and bAHI cutoff of 15 events/h demonstrated an accuracy, sensitivity, specificity of 0.938 [95% CI, 0.828–0.987], 0.944 [95% CI, 0.727–0.999], and 0.933 [95% CI, 0.779–0.992], respectively, for the diagnosis of moderate to severe OSA. BSP is a promising testing tool for OSA assessment and can potentially be incorporated into clinical practices for the identification of OSA. Trial registration: ClinicalTrial.org NCT03997916 https://clinicaltrials.gov/ct2/show/NCT03997916?term=belun+ring&draw=2&rank=1 Public Library of Science 2021-10-11 /pmc/articles/PMC8504733/ /pubmed/34634070 http://dx.doi.org/10.1371/journal.pone.0258040 Text en https://creativecommons.org/publicdomain/zero/1.0/This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication.
spellingShingle Research Article
Yeh, Eric
Wong, Eileen
Tsai, Chih-Wei
Gu, Wenbo
Chen, Pai-Lien
Leung, Lydia
Wu, I-Chen
Strohl, Kingman P.
Folz, Rodney J.
Yar, Wail
Chiang, Ambrose A.
Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire
title Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire
title_full Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire
title_fullStr Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire
title_full_unstemmed Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire
title_short Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire
title_sort detection of obstructive sleep apnea using belun sleep platform wearable with neural network-based algorithm and its combined use with stop-bang questionnaire
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8504733/
https://www.ncbi.nlm.nih.gov/pubmed/34634070
http://dx.doi.org/10.1371/journal.pone.0258040
work_keys_str_mv AT yeheric detectionofobstructivesleepapneausingbelunsleepplatformwearablewithneuralnetworkbasedalgorithmanditscombinedusewithstopbangquestionnaire
AT wongeileen detectionofobstructivesleepapneausingbelunsleepplatformwearablewithneuralnetworkbasedalgorithmanditscombinedusewithstopbangquestionnaire
AT tsaichihwei detectionofobstructivesleepapneausingbelunsleepplatformwearablewithneuralnetworkbasedalgorithmanditscombinedusewithstopbangquestionnaire
AT guwenbo detectionofobstructivesleepapneausingbelunsleepplatformwearablewithneuralnetworkbasedalgorithmanditscombinedusewithstopbangquestionnaire
AT chenpailien detectionofobstructivesleepapneausingbelunsleepplatformwearablewithneuralnetworkbasedalgorithmanditscombinedusewithstopbangquestionnaire
AT leunglydia detectionofobstructivesleepapneausingbelunsleepplatformwearablewithneuralnetworkbasedalgorithmanditscombinedusewithstopbangquestionnaire
AT wuichen detectionofobstructivesleepapneausingbelunsleepplatformwearablewithneuralnetworkbasedalgorithmanditscombinedusewithstopbangquestionnaire
AT strohlkingmanp detectionofobstructivesleepapneausingbelunsleepplatformwearablewithneuralnetworkbasedalgorithmanditscombinedusewithstopbangquestionnaire
AT folzrodneyj detectionofobstructivesleepapneausingbelunsleepplatformwearablewithneuralnetworkbasedalgorithmanditscombinedusewithstopbangquestionnaire
AT yarwail detectionofobstructivesleepapneausingbelunsleepplatformwearablewithneuralnetworkbasedalgorithmanditscombinedusewithstopbangquestionnaire
AT chiangambrosea detectionofobstructivesleepapneausingbelunsleepplatformwearablewithneuralnetworkbasedalgorithmanditscombinedusewithstopbangquestionnaire