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. 2020 Aug 17;10:13911. doi: 10.1038/s41598-020-70814-4

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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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Flowchart for the optimization procedure and validation. Procedure to select the optimal sample entropy (SE) settings (m and r) and the threshold of change (Th) for each SE airway flow (Flow) and airway pressure (Paw) features (SE-Flow_mean, SE-Flow_max, SE-Paw_mean, and SE-Paw_max). The dataset visually validated by the experts was randomly divided into two subsets: optimization (Opt.) and validation (Val.). The optimization procedure was repeated a total of 15 times using different subsets (randomly selected each time). The global maximum mean value of the Matthews correlation coefficient (MCC) determined the optimal values of m*, r*, and Th* among all possible combinations and all SE-derived features. Finally, the mean values of the measures of accuracy were computed for the optimal combination of parameters in both the optimization and the validation subsets.