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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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Optimal section for AF detection during NSR. (a) The experiment for the optimal sample size to identify AF was performed at a certain range of the R–R interval, where we reweighted the input EEG signal f(t) using the window function g(t) (b). The reweighted signal h(t) is computed by the equation h(t) = f(t) × g(t) and illustrated by the red dotted curve (c). This process clarifies the value ranges that particularly affect the trained model along the time-axis. The bi-directional connection is added so that the time flow can be considered in forward and backward passes, and long short-term memory is used to maintain a series of information in the short and long terms (d). NSR normal sinus rhythm, AF paroxysmal atrial fibrillation; LSTM long short-term memory.