Twelve-Lead ECG Reconstruction from Single-Lead Signals Using Generative Adversarial Networks

Jinho Joo; Gihun Joo; Yeji Kim; Moo Nyun Jin; Junbeom Park; Hyeonseung Im

doi:10.1007/978-3-031-43990-2_18

Twelve-Lead ECG Reconstruction from Single-Lead Signals Using Generative Adversarial Networks

Jinho Joo, Gihun Joo, Yeji Kim, Moo Nyun Jin, Junbeom Park, Hyeonseung Im

Department of Internal Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Recent advances in wearable healthcare devices such as smartwatches allow us to monitor and manage our health condition more actively, for example, by measuring our electrocardiogram (ECG) and predicting cardiovascular diseases (CVDs) such as atrial fibrillation in real-time. Nevertheless, most smart devices can only measure single-lead signals, such as Lead I, while multichannel ECGs, such as twelve-lead signals, are necessary to identify more intricate CVDs such as left and right bundle branch blocks. In this paper, to address this problem, we propose a novel generative adversarial network (GAN) that can faithfully reconstruct 12-lead ECG signals from single-lead signals, which consists of two generators and one 1D U-Net discriminator. Experimental results show that it outperforms other representative generative models. Moreover, we also validate our method’s ability to effectively reconstruct CVD-related characteristics by evaluating reconstructed ECGs with a highly accurate 12-lead ECG-based prediction model and three cardiologists.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings
Editors	Hayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	184-194
Number of pages	11
ISBN (Print)	9783031439896
DOIs	https://doi.org/10.1007/978-3-031-43990-2_18
State	Published - 2023
Event	26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 - Vancouver, Canada Duration: 8 Oct 2023 → 12 Oct 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14226 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
Country/Territory	Canada
City	Vancouver
Period	8/10/23 → 12/10/23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Keywords

Biosignal synthesis
ECG reconstruction
Generative model

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-43990-2_18

Cite this

Joo, J., Joo, G., Kim, Y., Jin, M. N., Park, J., & Im, H. (2023). Twelve-Lead ECG Reconstruction from Single-Lead Signals Using Generative Adversarial Networks. In H. Greenspan, H. Greenspan, A. Madabhushi, P. Mousavi, S. Salcudean, J. Duncan, T. Syeda-Mahmood, & R. Taylor (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings (pp. 184-194). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14226 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-43990-2_18

Joo, Jinho ; Joo, Gihun ; Kim, Yeji et al. / Twelve-Lead ECG Reconstruction from Single-Lead Signals Using Generative Adversarial Networks. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. editor / Hayit Greenspan ; Hayit Greenspan ; Anant Madabhushi ; Parvin Mousavi ; Septimiu Salcudean ; James Duncan ; Tanveer Syeda-Mahmood ; Russell Taylor. Springer Science and Business Media Deutschland GmbH, 2023. pp. 184-194 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Twelve-Lead ECG Reconstruction from Single-Lead Signals Using Generative Adversarial Networks",

abstract = "Recent advances in wearable healthcare devices such as smartwatches allow us to monitor and manage our health condition more actively, for example, by measuring our electrocardiogram (ECG) and predicting cardiovascular diseases (CVDs) such as atrial fibrillation in real-time. Nevertheless, most smart devices can only measure single-lead signals, such as Lead I, while multichannel ECGs, such as twelve-lead signals, are necessary to identify more intricate CVDs such as left and right bundle branch blocks. In this paper, to address this problem, we propose a novel generative adversarial network (GAN) that can faithfully reconstruct 12-lead ECG signals from single-lead signals, which consists of two generators and one 1D U-Net discriminator. Experimental results show that it outperforms other representative generative models. Moreover, we also validate our method{\textquoteright}s ability to effectively reconstruct CVD-related characteristics by evaluating reconstructed ECGs with a highly accurate 12-lead ECG-based prediction model and three cardiologists.",

keywords = "Biosignal synthesis, ECG reconstruction, Generative model",

author = "Jinho Joo and Gihun Joo and Yeji Kim and Jin, {Moo Nyun} and Junbeom Park and Hyeonseung Im",

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Joo, J, Joo, G, Kim, Y, Jin, MN, Park, J & Im, H 2023, Twelve-Lead ECG Reconstruction from Single-Lead Signals Using Generative Adversarial Networks. in H Greenspan, H Greenspan, A Madabhushi, P Mousavi, S Salcudean, J Duncan, T Syeda-Mahmood & R Taylor (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14226 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 184-194, 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, Vancouver, Canada, 8/10/23. https://doi.org/10.1007/978-3-031-43990-2_18

Twelve-Lead ECG Reconstruction from Single-Lead Signals Using Generative Adversarial Networks. / Joo, Jinho; Joo, Gihun; Kim, Yeji et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. ed. / Hayit Greenspan; Hayit Greenspan; Anant Madabhushi; Parvin Mousavi; Septimiu Salcudean; James Duncan; Tanveer Syeda-Mahmood; Russell Taylor. Springer Science and Business Media Deutschland GmbH, 2023. p. 184-194 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14226 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Recent advances in wearable healthcare devices such as smartwatches allow us to monitor and manage our health condition more actively, for example, by measuring our electrocardiogram (ECG) and predicting cardiovascular diseases (CVDs) such as atrial fibrillation in real-time. Nevertheless, most smart devices can only measure single-lead signals, such as Lead I, while multichannel ECGs, such as twelve-lead signals, are necessary to identify more intricate CVDs such as left and right bundle branch blocks. In this paper, to address this problem, we propose a novel generative adversarial network (GAN) that can faithfully reconstruct 12-lead ECG signals from single-lead signals, which consists of two generators and one 1D U-Net discriminator. Experimental results show that it outperforms other representative generative models. Moreover, we also validate our method’s ability to effectively reconstruct CVD-related characteristics by evaluating reconstructed ECGs with a highly accurate 12-lead ECG-based prediction model and three cardiologists.

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Joo J, Joo G, Kim Y, Jin MN, Park J, Im H. Twelve-Lead ECG Reconstruction from Single-Lead Signals Using Generative Adversarial Networks. In Greenspan H, Greenspan H, Madabhushi A, Mousavi P, Salcudean S, Duncan J, Syeda-Mahmood T, Taylor R, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 184-194. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-43990-2_18