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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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The serial changes of the probability of PAF using AI deep learning algorithm program according to acquired ECG dates. A 72-year-old man diagnosed with PAF is calculated to have AF with probabilities of 90% and 100% by the AI program during normal sinus rhythm, observed after his AF episode had terminated (red lightning bolt). Moreover, he was calculated to have AF with probabilities of 6.3% and 10% when there was no AF symptom. AF symptom is defined as palpitations, fatigue, dizziness, dyspnea, chest pain, and anxiety during AF. AI artificial intelligence; ECG electrocardiogram; NSR normal sinus rhythm; PAF paroxysmal atrial fibrillation.