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. 2018 Sep 7;8:13474. doi: 10.1038/s41598-018-31748-0

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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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Whole night sleep sound analysis estimation. (A) Enhanced audio signal by adaptive noise reduction algorithm. (B) Real-time probability score of MSS. (C) Offline probability score of MSS. (D) MSS estimation based on PSG. MSS estimations are presented as likelihood probability score. MSS – macro sleep stage, SSA – sleep sound analysis, PSG – polysomnography (PSG), W – wake (blue), R – rapid eye movement sleep (orange), and N – non- rapid eye movement sleep (red). Data was collected from an 18-year-old female, BMI 32, apnea-hypopnea index 2. This subject exhibits epoch-by-epoch agreement with PSG of (88%, 0.66) and (92%, 0.80) for real-time and offline estimation, respectively (accuracy, kappa).