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Effects of torso mesh density and electrode distribution on the accuracy of electrocardiographic imaging during atrial fibrillation

Introduction: Electrocardiographic Imaging (ECGI) allows computing the electrical activity in the heart non-invasively using geometrical information of the patient and multiple body surface signals. In the present study we investigate the influence of the number of nodes of geometrical meshes and re...

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Autores principales: Molero, Rubén, González-Ascaso, Ana, Hernández-Romero, Ismael, Lundback-Mompó, David, Climent, Andreu M., Guillem, María S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9465032/
https://www.ncbi.nlm.nih.gov/pubmed/36105286
http://dx.doi.org/10.3389/fphys.2022.908364
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author Molero, Rubén
González-Ascaso, Ana
Hernández-Romero, Ismael
Lundback-Mompó, David
Climent, Andreu M.
Guillem, María S.
author_facet Molero, Rubén
González-Ascaso, Ana
Hernández-Romero, Ismael
Lundback-Mompó, David
Climent, Andreu M.
Guillem, María S.
author_sort Molero, Rubén
collection PubMed
description Introduction: Electrocardiographic Imaging (ECGI) allows computing the electrical activity in the heart non-invasively using geometrical information of the patient and multiple body surface signals. In the present study we investigate the influence of the number of nodes of geometrical meshes and recording ECG electrodes distribution to compute ECGI during atrial fibrillation (AF). Methods: Torso meshes from 100 to 2000 nodes heterogeneously and homogeneously distributed were compared. Signals from nine AF realistic mathematical simulations were used for computing the ECGI. Results for each torso mesh were compared with the ECGI computed with a 4,000 nodes reference torso. In addition, real AF recordings from 25 AF patients were used to compute ECGI in torso meshes from 100 to 1,000 nodes. Results were compared with a reference torso of 2000 nodes. Torsos were remeshed either by reducing the number of nodes while maximizing the overall shape preservation and then assigning the location of the electrodes as the closest node in the new mesh or by forcing the remesher to place a node at each electrode location. Correlation coefficients, relative difference measurements and relative difference of dominant frequencies were computed to evaluate the impact on signal morphology of each torso mesh. Results: For remeshed torsos where electrodes match with a geometrical node in the mesh, all mesh densities presented similar results. On the other hand, in torsos with electrodes assigned to closest nodes in remeshed geometries performance metrics were dependent on mesh densities, with correlation coefficients ranging from 0.53 ± 0.06 to 0.92 ± 0.04 in simulations or from 0.42 ± 0.38 to 0.89 ± 0.2 in patients. Dominant frequency relative errors showed the same trend with values from 1.14 ± 0.26 to 0.55 ± 0.21 Hz in simulations and from 0.91 ± 0.56 to 0.45 ± 0.41 Hz in patients. Conclusion: The effect of mesh density in ECGI is minimal when the location of the electrode is preserved as a node in the mesh. Torso meshes constructed without imposing electrodes to constitute nodes in the torso geometry should contain at least 400 nodes homogeneously distributed so that a distance between nodes is below 4 cm.
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spelling pubmed-94650322022-09-13 Effects of torso mesh density and electrode distribution on the accuracy of electrocardiographic imaging during atrial fibrillation Molero, Rubén González-Ascaso, Ana Hernández-Romero, Ismael Lundback-Mompó, David Climent, Andreu M. Guillem, María S. Front Physiol Physiology Introduction: Electrocardiographic Imaging (ECGI) allows computing the electrical activity in the heart non-invasively using geometrical information of the patient and multiple body surface signals. In the present study we investigate the influence of the number of nodes of geometrical meshes and recording ECG electrodes distribution to compute ECGI during atrial fibrillation (AF). Methods: Torso meshes from 100 to 2000 nodes heterogeneously and homogeneously distributed were compared. Signals from nine AF realistic mathematical simulations were used for computing the ECGI. Results for each torso mesh were compared with the ECGI computed with a 4,000 nodes reference torso. In addition, real AF recordings from 25 AF patients were used to compute ECGI in torso meshes from 100 to 1,000 nodes. Results were compared with a reference torso of 2000 nodes. Torsos were remeshed either by reducing the number of nodes while maximizing the overall shape preservation and then assigning the location of the electrodes as the closest node in the new mesh or by forcing the remesher to place a node at each electrode location. Correlation coefficients, relative difference measurements and relative difference of dominant frequencies were computed to evaluate the impact on signal morphology of each torso mesh. Results: For remeshed torsos where electrodes match with a geometrical node in the mesh, all mesh densities presented similar results. On the other hand, in torsos with electrodes assigned to closest nodes in remeshed geometries performance metrics were dependent on mesh densities, with correlation coefficients ranging from 0.53 ± 0.06 to 0.92 ± 0.04 in simulations or from 0.42 ± 0.38 to 0.89 ± 0.2 in patients. Dominant frequency relative errors showed the same trend with values from 1.14 ± 0.26 to 0.55 ± 0.21 Hz in simulations and from 0.91 ± 0.56 to 0.45 ± 0.41 Hz in patients. Conclusion: The effect of mesh density in ECGI is minimal when the location of the electrode is preserved as a node in the mesh. Torso meshes constructed without imposing electrodes to constitute nodes in the torso geometry should contain at least 400 nodes homogeneously distributed so that a distance between nodes is below 4 cm. Frontiers Media S.A. 2022-08-29 /pmc/articles/PMC9465032/ /pubmed/36105286 http://dx.doi.org/10.3389/fphys.2022.908364 Text en Copyright © 2022 Molero, González-Ascaso, Hernández-Romero, Lundback-Mompó, Climent and Guillem. https://creativecommons.org/licenses/by/4.0/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) and the copyright owner(s) 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.
spellingShingle Physiology
Molero, Rubén
González-Ascaso, Ana
Hernández-Romero, Ismael
Lundback-Mompó, David
Climent, Andreu M.
Guillem, María S.
Effects of torso mesh density and electrode distribution on the accuracy of electrocardiographic imaging during atrial fibrillation
title Effects of torso mesh density and electrode distribution on the accuracy of electrocardiographic imaging during atrial fibrillation
title_full Effects of torso mesh density and electrode distribution on the accuracy of electrocardiographic imaging during atrial fibrillation
title_fullStr Effects of torso mesh density and electrode distribution on the accuracy of electrocardiographic imaging during atrial fibrillation
title_full_unstemmed Effects of torso mesh density and electrode distribution on the accuracy of electrocardiographic imaging during atrial fibrillation
title_short Effects of torso mesh density and electrode distribution on the accuracy of electrocardiographic imaging during atrial fibrillation
title_sort effects of torso mesh density and electrode distribution on the accuracy of electrocardiographic imaging during atrial fibrillation
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9465032/
https://www.ncbi.nlm.nih.gov/pubmed/36105286
http://dx.doi.org/10.3389/fphys.2022.908364
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