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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 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 languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings
EditorsHayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor
PublisherSpringer Science and Business Media Deutschland GmbH
Pages184-194
Number of pages11
ISBN (Print)9783031439896
DOIs
StatePublished - 2023
Event26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 - Vancouver, Canada
Duration: 8 Oct 202312 Oct 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14226 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
Country/TerritoryCanada
CityVancouver
Period8/10/2312/10/23

Bibliographical note

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

Keywords

  • Biosignal synthesis
  • ECG reconstruction
  • Generative model

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