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. 2021 Mar 4;11:5251. doi: 10.1038/s41598-021-84374-8

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© The Author(s) 2021

Open AccessThis 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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Visualization of transfer learning in this work. The process is divided into 3 steps: (1) deep convolutional neural network (CNN) is pretrained on the Icentia11K⁵ data set for a selected pretraining objective, e.g. classification of heart rate; (2) the pretrained weights are used as initial weights of a new CNN; (3) this CNN is finetuned on the PhysioNet/CinC Challenge 2017^{7, 8} data set to classify Atrial Fibrillation (AF).