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. 2020 Aug 20;3:106. doi: 10.1038/s41746-020-0291-x

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© The Author(s) 2020

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 6 — The tables below show results of regression analysis with the covariates on each row as independent variables and our algorithm predicted sleep metrics on each column as the dependent variable. The metrics are adjusted for age and gender before regressing on apnea, and adjusted for age, gender and apnea before regressing on hypertension. Boxes where the covariate was a significant predictor are filled with values, the rest are filled with “NS”. Boxes colored in green are those where our results qualitatively agree with a previous study¹² done using on PSG reference stages. Boxes colored in red are those where the previous study¹² and our results disagree. N = 800. a Regression coefficients with 95% confidence intervals. b Percentage of variance of sleep measure explained by covariate calculated as 100 * R-squared along with p-values.