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. 2019 Nov 14;9:16824. doi: 10.1038/s41598-019-53115-3

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© The Author(s) 2019

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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Classifier performance as a function of training data: On the left is shown the distribution of Cohens kappa values for leave-one-subject-out (LOSO) classification versus number of subjects in the training data (N). On the right is shown distributions for ‘Individual’ (using only recordings from the same subject) and ‘Leave-one-record-out’ (LORO (combining recordings from all other subjects with 1, 2 or 3 from the same subject). For LOSO, each value of N was repeated 100 times, to obtain unique samplings of training subjects. All subjects not used for training were used for testing, meaning that the number of kappa values for a given N was (20-N) times the number of repetitions. For N = 1 and 19, only 20 repetitions were done (being the number of unique combinations). For Individual and LORO, all possible combinations were used (resp. 80, 120, 80 combinations for 1, 2 or 3 nights).