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. 2021 Jun 17;11:12818. doi: 10.1038/s41598-021-92172-5

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© The Author(s) 2021

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Description of the artificial intelligence algorithm for detecting PAF. All raw ECG data were stored as XML documents using the MUSE data management system in a relational database server. PAF probability is calculated through our developed AI algorithm using an RNN with two-dimensional convolution (red box). AI artificial intelligence; ECG electrocardiogram; LSTM long short-term memory; PAF paroxysmal atrial fibrillation; RNN recurrent neural network.