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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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Automatic CP-VI detection. Respiratory signals (Flow and Paw) are decimated at a sampling rate of 40 Hz. Sample entropy (SE) is calculated for different values of the embedding dimension (m) and tolerance (r). An 8-period-long exponential moving average is used to reduce noise and to increase the consistency of the SE results. Two SE features are determined for each 15-min period: the mean value and the maximum value. The percentage of change (PC) from the patient's own baseline value is calculated for each SE setting. When PC exceeds a determined threshold (Th), the period is considered to contain a CP-VI event. An optimization procedure is required to select the values of m, r, Th, respiratory signal, and SE feature that yield the most robust estimations of CP-VI.