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. 2018 Feb 26;8:3667. doi: 10.1038/s41598-018-22025-1

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© The Author(s) 2018

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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Selected features and classification results for UVA. (a) Classification accuracy by SVM method and LOOCV algorithm at different dimensions of selected features. Each training/testing sample used in LOOCV is the full 30 minute data from one surgery. (b) First and second principal components of features for UVA, classified using linear SVM method. (c) Average value and standard error of the mean (s.e.m) –standard deviation divided by the square root of number of data- of features for Trustworthy and Concerning procedures. Error bars represent s.e.m. for trustworthy (N = 63) and concerning (N = 24) samples. Selected features were significantly independent for trustworthy and concerning samples for selected features (two-sample t-test for 63 trustworthy and 24 concerning cases, resulted in P = 9.4 × 10⁻²⁵, 1.03 × 10⁻¹⁵, and 4.9 × 10⁻²⁶, respectively).