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REVIEW article
Front. Physiol.
Sec. Cardiac Electrophysiology
Volume 16 - 2025 | doi: 10.3389/fphys.2025.1532284
RECONSTRUCTION OF 12-LEAD ECG: A REVIEW OF ALGORITHMS
Provisionally accepted- University of Leicester, Leicester, United Kingdom
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Purpose: This paper aims to review the literature on 12-lead ECG reconstruction, highlight various algorithmic approaches and evaluate their predictive strengths. In addition, it investigates the implications of performing reconstruction in particular ways.Methods: This narrative review analysed 39 works on the reconstruction of 12-lead ECGs, focusing on the algorithms used for reconstruction and the results gotten from using these algorithms.Results: The works analysed featured the use of as little as 1 lead and as much as 4 leads for reconstruction of the other leads. Linear and nonlinear (including artificial intelligence) algorithms showed promising performances.Their outputs had correlations of greater than 0.90 depending on how the reconstruction models were built.Conclusion: Three leads are optimal as input predictors for minimal reconstruction errors, but there is no universal algorithm that applies to every reconstruction task. Both linear and nonlinear algorithms can achieve high correlations, and minimal root means square errors. Hence, planned steps are needed when deciding how to manipulate the data and build the models to achieve high accuracies.
Keywords: ECG, reconstruction, neural networks, machine learning, review, regression
Received: 21 Nov 2024; Accepted: 08 Apr 2025.
Copyright: © 2025 Obianom, Ng and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ekenedirichukwu N Obianom, University of Leicester, Leicester, United Kingdom
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.