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A novel neural-network model for deriving standard 12-lead ECGs from serial three-lead ECGs: application to self-care

Authors:

Paul RubelAuthors Info & Claims

IEEE Transactions on Information Technology in Biomedicine, Volume 14, Issue 3

Pages 883 - 890

https://doi.org/10.1109/TITB.2010.2047754

Published: 01 May 2010 Publication History

Abstract

Synthesis of the 12-lead ECG has been investigated in the past decade as a method to improve patient monitoring in situations where the acquisition of the 12-lead ECG is cumbersome and time consuming. This paper presents and assesses a novel approach for deriving 12-lead ECGs from a pseudoorthogonal three-lead subset via generic and patient-specific nonlinear reconstruction methods based on the use of artificial neural-networks (ANNs) committees. We train and test the ANN on a set of serial ECGs from 120 cardiac inpatients from the intensive care unit of the Cardiology Hospital of Lyon. We then assess the similarity between the synthesized ECGs and the original ECGs at the quantitative level in comparison with generic and patient-specific multiple-regression-based methods. The ANN achieved accurate reconstruction of the 12-lead ECGs of the study population using both generic and patient-specific ANN transforms, showing significant improvements over generic (p-value ≤ 0.05) and patient-specific (p-value ≤ 0.01) multiple-linear-regression-based models. Consequently, our neural-network-based approach has proven to be sufficiently accurate to be deployed in home care as well as in ambulatory situations to synthesize a standard 12-lead ECG from a reduced lead-set ECG recording.

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Cited By

Grande-Fidalgo ACalpe JSoria-Olivas E(2023)Challenges in ECG Lead Reconstruction in Patients with Pacemakers and Implantable DefibrillatorsProceedings of the 2023 10th International Conference on Bioinformatics Research and Applications10.1145/3632047.3632063(101-106)Online publication date: 22-Sep-2023
https://dl.acm.org/doi/10.1145/3632047.3632063
Joo JJoo GKim YJin MPark JIm H(2023)Twelve-Lead ECG Reconstruction from Single-Lead Signals Using Generative Adversarial NetworksMedical Image Computing and Computer Assisted Intervention – MICCAI 202310.1007/978-3-031-43990-2_18(184-194)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-43990-2_18
Banerjee AMaji DDatta RBarman SSamanta DChattopadhyay S(2022)SHUBHCHINTAKMultimedia Tools and Applications10.1007/s11042-022-13539-y81:26(37137-37163)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s11042-022-13539-y
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Published In

cover image IEEE Transactions on Information Technology in Biomedicine

IEEE Transactions on Information Technology in Biomedicine Volume 14, Issue 3

Special section on new and emerging technologies in bioinformatics and bioengineering

May 2010

348 pages

ISSN:1089-7771

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Copyright © 2010.

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IEEE Press

Publication History

Published: 01 May 2010

Revised: 15 March 2007

Received: 03 November 2006

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Cited By

Grande-Fidalgo ACalpe JSoria-Olivas E(2023)Challenges in ECG Lead Reconstruction in Patients with Pacemakers and Implantable DefibrillatorsProceedings of the 2023 10th International Conference on Bioinformatics Research and Applications10.1145/3632047.3632063(101-106)Online publication date: 22-Sep-2023
https://dl.acm.org/doi/10.1145/3632047.3632063
Joo JJoo GKim YJin MPark JIm H(2023)Twelve-Lead ECG Reconstruction from Single-Lead Signals Using Generative Adversarial NetworksMedical Image Computing and Computer Assisted Intervention – MICCAI 202310.1007/978-3-031-43990-2_18(184-194)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-43990-2_18
Banerjee AMaji DDatta RBarman SSamanta DChattopadhyay S(2022)SHUBHCHINTAKMultimedia Tools and Applications10.1007/s11042-022-13539-y81:26(37137-37163)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s11042-022-13539-y
Chuang YHuang YChiu CChang WChien J(2021)Synthesis of Chest-Lead ECG Using Temporal Convolutional NetworksProceedings of the 5th International Conference on Graphics and Signal Processing10.1145/3474906.3474908(54-59)Online publication date: 25-Jun-2021
https://dl.acm.org/doi/10.1145/3474906.3474908
lin DVasilakos ATang YYao Y(2016)Neural networks for computer-aided diagnosis in medicineNeurocomputing10.1016/j.neucom.2016.08.039216:C(700-708)Online publication date: 5-Dec-2016
https://dl.acm.org/doi/10.1016/j.neucom.2016.08.039
Chen HLiu H(2016)A remote electrocardiogram monitoring system with good swiftness and high reliablilityComputers and Electrical Engineering10.1016/j.compeleceng.2016.02.00453:C(191-202)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.compeleceng.2016.02.004
Seera MLim CLiew WLim ELoo C(2015)Classification of electrocardiogram and auscultatory blood pressure signals using machine learning modelsExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.12.02342:7(3643-3652)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1016/j.eswa.2014.12.023
Lee SMotai YWeiss ESun S(2013)Customized prediction of respiratory motion with clustering from multiple patient interactionACM Transactions on Intelligent Systems and Technology10.1145/2508037.25080504:4(1-17)Online publication date: 8-Oct-2013
https://dl.acm.org/doi/10.1145/2508037.2508050
Fayn JRubel P(2010)Toward a personal health society in cardiologyIEEE Transactions on Information Technology in Biomedicine10.1109/TITB.2009.203761614:2(401-409)Online publication date: 1-Mar-2010
https://dl.acm.org/doi/10.1109/TITB.2009.2037616

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