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Performance Evaluation of a Smart Bed Technology against Polysomnography
The Sleep Number smart bed uses embedded ballistocardiography, together with network connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing rate (BR), and sleep vs. wake states. This study evaluated the performance of the smart bed relative to polysomnography (PS...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002520/ https://www.ncbi.nlm.nih.gov/pubmed/35408220 http://dx.doi.org/10.3390/s22072605 |
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author | Siyahjani, Farzad Garcia Molina, Gary Barr, Shawn Mushtaq, Faisal |
author_facet | Siyahjani, Farzad Garcia Molina, Gary Barr, Shawn Mushtaq, Faisal |
author_sort | Siyahjani, Farzad |
collection | PubMed |
description | The Sleep Number smart bed uses embedded ballistocardiography, together with network connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing rate (BR), and sleep vs. wake states. This study evaluated the performance of the smart bed relative to polysomnography (PSG) in estimating epoch-by-epoch HR, BR, sleep vs. wake, mean overnight HR and BR, and summary sleep variables. Forty-five participants (aged 22–64 years; 55% women) slept one night on the smart bed with standard PSG. Smart bed data were compared to PSG by Bland–Altman analysis and Pearson correlation for epoch-by-epoch HR and epoch-by-epoch BR. Agreement in sleep vs. wake classification was quantified using Cohen’s kappa, ROC analysis, sensitivity, specificity, accuracy, and precision. Epoch-by-epoch HR and BR were highly correlated with PSG (HR: r = 0.81, |bias| = 0.23 beats/min; BR: r = 0.71, |bias| = 0.08 breaths/min), as were estimations of mean overnight HR and BR (HR: r = 0.94, |bias| = 0.15 beats/min; BR: r = 0.96, |bias| = 0.09 breaths/min). Calculated agreement for sleep vs. wake detection included kappa (prevalence and bias-adjusted) = 0.74 ± 0.11, AUC = 0.86, sensitivity = 0.94 ± 0.05, specificity = 0.48 ± 0.18, accuracy = 0.86 ± 0.11, and precision = 0.90 ± 0.06. For all-night summary variables, agreement was moderate to strong. Overall, the findings suggest that the Sleep Number smart bed may provide reliable metrics to unobtrusively characterize human sleep under real life-conditions. |
format | Online Article Text |
id | pubmed-9002520 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90025202022-04-13 Performance Evaluation of a Smart Bed Technology against Polysomnography Siyahjani, Farzad Garcia Molina, Gary Barr, Shawn Mushtaq, Faisal Sensors (Basel) Article The Sleep Number smart bed uses embedded ballistocardiography, together with network connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing rate (BR), and sleep vs. wake states. This study evaluated the performance of the smart bed relative to polysomnography (PSG) in estimating epoch-by-epoch HR, BR, sleep vs. wake, mean overnight HR and BR, and summary sleep variables. Forty-five participants (aged 22–64 years; 55% women) slept one night on the smart bed with standard PSG. Smart bed data were compared to PSG by Bland–Altman analysis and Pearson correlation for epoch-by-epoch HR and epoch-by-epoch BR. Agreement in sleep vs. wake classification was quantified using Cohen’s kappa, ROC analysis, sensitivity, specificity, accuracy, and precision. Epoch-by-epoch HR and BR were highly correlated with PSG (HR: r = 0.81, |bias| = 0.23 beats/min; BR: r = 0.71, |bias| = 0.08 breaths/min), as were estimations of mean overnight HR and BR (HR: r = 0.94, |bias| = 0.15 beats/min; BR: r = 0.96, |bias| = 0.09 breaths/min). Calculated agreement for sleep vs. wake detection included kappa (prevalence and bias-adjusted) = 0.74 ± 0.11, AUC = 0.86, sensitivity = 0.94 ± 0.05, specificity = 0.48 ± 0.18, accuracy = 0.86 ± 0.11, and precision = 0.90 ± 0.06. For all-night summary variables, agreement was moderate to strong. Overall, the findings suggest that the Sleep Number smart bed may provide reliable metrics to unobtrusively characterize human sleep under real life-conditions. MDPI 2022-03-29 /pmc/articles/PMC9002520/ /pubmed/35408220 http://dx.doi.org/10.3390/s22072605 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Siyahjani, Farzad Garcia Molina, Gary Barr, Shawn Mushtaq, Faisal Performance Evaluation of a Smart Bed Technology against Polysomnography |
title | Performance Evaluation of a Smart Bed Technology against Polysomnography |
title_full | Performance Evaluation of a Smart Bed Technology against Polysomnography |
title_fullStr | Performance Evaluation of a Smart Bed Technology against Polysomnography |
title_full_unstemmed | Performance Evaluation of a Smart Bed Technology against Polysomnography |
title_short | Performance Evaluation of a Smart Bed Technology against Polysomnography |
title_sort | performance evaluation of a smart bed technology against polysomnography |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002520/ https://www.ncbi.nlm.nih.gov/pubmed/35408220 http://dx.doi.org/10.3390/s22072605 |
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