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. 2020 May 21;10:8445. doi: 10.1038/s41598-020-65105-x

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© The Author(s) 2020

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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ECG data and datasets preparation. (a) Target ECG regions for the present study are shown. The region A (744*368) was extracted for our deep learning processes. The region B (744*368) was used for over-sampling in order to resolve the data imbalance (see text in detail). (b) Dataset preparation is shown in this flowchart. As test sets and validation sets, 25 ECGs were randomly selected respectively. For data balancing, 10 or 17 ECGs were randomly selected in each group (over-sampling, see Fig. 2a), and added to construct the training sets. (c) ECGs with lead reduction are shown. 9-lead-ECG consisted of leads I, II, III, and V_1–6. 7-lead-ECG consisted of leads I, II, III, V_1–2, and V_5–6. (d) Based on the 9-lead-ECG, we compressed its image quality in 4 different ratios; full quality (744*368), half quality (398*260), quarter quality (282*184) and square-shape (224*224).