{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T09:29:10Z","timestamp":1774344550782,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T00:00:00Z","timestamp":1648512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u201364 years; 55% women) slept one night on the smart bed with standard PSG. Smart bed data were compared to PSG by Bland\u2013Altman analysis and Pearson correlation for epoch-by-epoch HR and epoch-by-epoch BR. Agreement in sleep vs. wake classification was quantified using Cohen\u2019s 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 \u00b1 0.11, AUC = 0.86, sensitivity = 0.94 \u00b1 0.05, specificity = 0.48 \u00b1 0.18, accuracy = 0.86 \u00b1 0.11, and precision = 0.90 \u00b1 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.<\/jats:p>","DOI":"10.3390\/s22072605","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T21:45:51Z","timestamp":1648590351000},"page":"2605","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Performance Evaluation of a Smart Bed Technology against Polysomnography"],"prefix":"10.3390","volume":"22","author":[{"given":"Farzad","family":"Siyahjani","sequence":"first","affiliation":[{"name":"Sleep Number\u00ae Labs, San Jose, CA 95113, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4621-4667","authenticated-orcid":false,"given":"Gary","family":"Garcia Molina","sequence":"additional","affiliation":[{"name":"Sleep Number\u00ae Labs, San Jose, CA 95113, USA"}]},{"given":"Shawn","family":"Barr","sequence":"additional","affiliation":[{"name":"Sleep Number\u00ae Labs, San Jose, CA 95113, USA"}]},{"given":"Faisal","family":"Mushtaq","sequence":"additional","affiliation":[{"name":"Sleep Number\u00ae Labs, San Jose, CA 95113, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.5664\/jcsm.5288","article-title":"Consumer sleep technologies: A review of the landscape","volume":"11","author":"Ko","year":"2015","journal-title":"J. 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