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Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography

PURPOSE: In this proof of principle study, we evaluated the diagnostic accuracy of the novel Nox BodySleep(TM) 1.0 algorithm (Nox Medical, Iceland) for the estimation of disease severity and sleep stages based on features extracted from actigraphy and respiratory inductance plethysmography (RIP) bel...

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Autores principales: Dietz-Terjung, Sarah, Martin, Amelie Ricarda, Finnsson, Eysteinn, Ágústsson, Jón Skínir, Helgason, Snorri, Helgadóttir, Halla, Welsner, Matthias, Taube, Christian, Weinreich, Gerhard, Schöbel, Christoph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590674/
https://www.ncbi.nlm.nih.gov/pubmed/33594617
http://dx.doi.org/10.1007/s11325-021-02316-0
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author Dietz-Terjung, Sarah
Martin, Amelie Ricarda
Finnsson, Eysteinn
Ágústsson, Jón Skínir
Helgason, Snorri
Helgadóttir, Halla
Welsner, Matthias
Taube, Christian
Weinreich, Gerhard
Schöbel, Christoph
author_facet Dietz-Terjung, Sarah
Martin, Amelie Ricarda
Finnsson, Eysteinn
Ágústsson, Jón Skínir
Helgason, Snorri
Helgadóttir, Halla
Welsner, Matthias
Taube, Christian
Weinreich, Gerhard
Schöbel, Christoph
author_sort Dietz-Terjung, Sarah
collection PubMed
description PURPOSE: In this proof of principle study, we evaluated the diagnostic accuracy of the novel Nox BodySleep(TM) 1.0 algorithm (Nox Medical, Iceland) for the estimation of disease severity and sleep stages based on features extracted from actigraphy and respiratory inductance plethysmography (RIP) belts. Validation was performed against in-lab polysomnography (PSG) in patients with sleep-disordered breathing (SDB). METHODS: Patients received PSG according to AASM. Sleep stages were manually scored using the AASM criteria and the recording was evaluated by the novel algorithm. The results were analyzed by descriptive statistics methods (IBM SPSS Statistics 25.0). RESULTS: We found a strong Pearson correlation (r=0.91) with a bias of 0.2/h for AHI estimation as well as a good correlation (r=0.81) and an overestimation of 14 min for total sleep time (TST). Sleep efficiency (SE) was also valued with a good Pearson correlation (r=0.73) and an overestimation of 2.1%. Wake epochs were estimated with a sensitivity of 0.65 and a specificity of 0.59 while REM and non-REM (NREM) phases were evaluated a sensitivity of 0.72 and 0.74, respectively. Specificity was 0.74 for NREM and 0.68 for REM. Additionally, a Cohen’s kappa of 0.62 was found for this 3-class classification problem. CONCLUSION: The algorithm shows a moderate diagnostic accuracy for the estimation of sleep. In addition, the algorithm determines the AHI with good agreement with the manual scoring and it shows good diagnostic accuracy in estimating wake-sleep transition. The presented algorithm seems to be an appropriate tool to increase the diagnostic accuracy of portable monitoring. The validated diagnostic algorithm promises a more appropriate and cost-effective method if integrated in out-of-center (OOC) testing of patients with suspicion for SDB. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11325-021-02316-0.
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spelling pubmed-85906742021-11-23 Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography Dietz-Terjung, Sarah Martin, Amelie Ricarda Finnsson, Eysteinn Ágústsson, Jón Skínir Helgason, Snorri Helgadóttir, Halla Welsner, Matthias Taube, Christian Weinreich, Gerhard Schöbel, Christoph Sleep Breath Sleep Breathing Physiology and Disorders • Original Article PURPOSE: In this proof of principle study, we evaluated the diagnostic accuracy of the novel Nox BodySleep(TM) 1.0 algorithm (Nox Medical, Iceland) for the estimation of disease severity and sleep stages based on features extracted from actigraphy and respiratory inductance plethysmography (RIP) belts. Validation was performed against in-lab polysomnography (PSG) in patients with sleep-disordered breathing (SDB). METHODS: Patients received PSG according to AASM. Sleep stages were manually scored using the AASM criteria and the recording was evaluated by the novel algorithm. The results were analyzed by descriptive statistics methods (IBM SPSS Statistics 25.0). RESULTS: We found a strong Pearson correlation (r=0.91) with a bias of 0.2/h for AHI estimation as well as a good correlation (r=0.81) and an overestimation of 14 min for total sleep time (TST). Sleep efficiency (SE) was also valued with a good Pearson correlation (r=0.73) and an overestimation of 2.1%. Wake epochs were estimated with a sensitivity of 0.65 and a specificity of 0.59 while REM and non-REM (NREM) phases were evaluated a sensitivity of 0.72 and 0.74, respectively. Specificity was 0.74 for NREM and 0.68 for REM. Additionally, a Cohen’s kappa of 0.62 was found for this 3-class classification problem. CONCLUSION: The algorithm shows a moderate diagnostic accuracy for the estimation of sleep. In addition, the algorithm determines the AHI with good agreement with the manual scoring and it shows good diagnostic accuracy in estimating wake-sleep transition. The presented algorithm seems to be an appropriate tool to increase the diagnostic accuracy of portable monitoring. The validated diagnostic algorithm promises a more appropriate and cost-effective method if integrated in out-of-center (OOC) testing of patients with suspicion for SDB. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11325-021-02316-0. Springer International Publishing 2021-02-16 2021 /pmc/articles/PMC8590674/ /pubmed/33594617 http://dx.doi.org/10.1007/s11325-021-02316-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Sleep Breathing Physiology and Disorders • Original Article
Dietz-Terjung, Sarah
Martin, Amelie Ricarda
Finnsson, Eysteinn
Ágústsson, Jón Skínir
Helgason, Snorri
Helgadóttir, Halla
Welsner, Matthias
Taube, Christian
Weinreich, Gerhard
Schöbel, Christoph
Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography
title Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography
title_full Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography
title_fullStr Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography
title_full_unstemmed Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography
title_short Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography
title_sort proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography
topic Sleep Breathing Physiology and Disorders • Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590674/
https://www.ncbi.nlm.nih.gov/pubmed/33594617
http://dx.doi.org/10.1007/s11325-021-02316-0
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