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Non-invasive Estimation of Atrial Fibrillation Driver Position With Convolutional Neural Networks and Body Surface Potentials
Atrial fibrillation (AF) is characterized by complex and irregular propagation patterns, and AF onset locations and drivers responsible for its perpetuation are the main targets for ablation procedures. ECG imaging (ECGI) has been demonstrated as a promising tool to identify AF drivers and guide abl...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8552066/ https://www.ncbi.nlm.nih.gov/pubmed/34721065 http://dx.doi.org/10.3389/fphys.2021.733449 |
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author | Cámara-Vázquez, Miguel Ángel Hernández-Romero, Ismael Morgado-Reyes, Eduardo Guillem, Maria S. Climent, Andreu M. Barquero-Pérez, Oscar |
author_facet | Cámara-Vázquez, Miguel Ángel Hernández-Romero, Ismael Morgado-Reyes, Eduardo Guillem, Maria S. Climent, Andreu M. Barquero-Pérez, Oscar |
author_sort | Cámara-Vázquez, Miguel Ángel |
collection | PubMed |
description | Atrial fibrillation (AF) is characterized by complex and irregular propagation patterns, and AF onset locations and drivers responsible for its perpetuation are the main targets for ablation procedures. ECG imaging (ECGI) has been demonstrated as a promising tool to identify AF drivers and guide ablation procedures, being able to reconstruct the electrophysiological activity on the heart surface by using a non-invasive recording of body surface potentials (BSP). However, the inverse problem of ECGI is ill-posed, and it requires accurate mathematical modeling of both atria and torso, mainly from CT or MR images. Several deep learning-based methods have been proposed to detect AF, but most of the AF-based studies do not include the estimation of ablation targets. In this study, we propose to model the location of AF drivers from BSP as a supervised classification problem using convolutional neural networks (CNN). Accuracy in the test set ranged between 0.75 (SNR = 5 dB) and 0.93 (SNR = 20 dB upward) when assuming time independence, but it worsened to 0.52 or lower when dividing AF models into blocks. Therefore, CNN could be a robust method that could help to non-invasively identify target regions for ablation in AF by using body surface potential mapping, avoiding the use of ECGI. |
format | Online Article Text |
id | pubmed-8552066 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85520662021-10-29 Non-invasive Estimation of Atrial Fibrillation Driver Position With Convolutional Neural Networks and Body Surface Potentials Cámara-Vázquez, Miguel Ángel Hernández-Romero, Ismael Morgado-Reyes, Eduardo Guillem, Maria S. Climent, Andreu M. Barquero-Pérez, Oscar Front Physiol Physiology Atrial fibrillation (AF) is characterized by complex and irregular propagation patterns, and AF onset locations and drivers responsible for its perpetuation are the main targets for ablation procedures. ECG imaging (ECGI) has been demonstrated as a promising tool to identify AF drivers and guide ablation procedures, being able to reconstruct the electrophysiological activity on the heart surface by using a non-invasive recording of body surface potentials (BSP). However, the inverse problem of ECGI is ill-posed, and it requires accurate mathematical modeling of both atria and torso, mainly from CT or MR images. Several deep learning-based methods have been proposed to detect AF, but most of the AF-based studies do not include the estimation of ablation targets. In this study, we propose to model the location of AF drivers from BSP as a supervised classification problem using convolutional neural networks (CNN). Accuracy in the test set ranged between 0.75 (SNR = 5 dB) and 0.93 (SNR = 20 dB upward) when assuming time independence, but it worsened to 0.52 or lower when dividing AF models into blocks. Therefore, CNN could be a robust method that could help to non-invasively identify target regions for ablation in AF by using body surface potential mapping, avoiding the use of ECGI. Frontiers Media S.A. 2021-10-14 /pmc/articles/PMC8552066/ /pubmed/34721065 http://dx.doi.org/10.3389/fphys.2021.733449 Text en Copyright © 2021 Cámara-Vázquez, Hernández-Romero, Morgado-Reyes, Guillem, Climent and Barquero-Pérez. 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 Cámara-Vázquez, Miguel Ángel Hernández-Romero, Ismael Morgado-Reyes, Eduardo Guillem, Maria S. Climent, Andreu M. Barquero-Pérez, Oscar Non-invasive Estimation of Atrial Fibrillation Driver Position With Convolutional Neural Networks and Body Surface Potentials |
title | Non-invasive Estimation of Atrial Fibrillation Driver Position With Convolutional Neural Networks and Body Surface Potentials |
title_full | Non-invasive Estimation of Atrial Fibrillation Driver Position With Convolutional Neural Networks and Body Surface Potentials |
title_fullStr | Non-invasive Estimation of Atrial Fibrillation Driver Position With Convolutional Neural Networks and Body Surface Potentials |
title_full_unstemmed | Non-invasive Estimation of Atrial Fibrillation Driver Position With Convolutional Neural Networks and Body Surface Potentials |
title_short | Non-invasive Estimation of Atrial Fibrillation Driver Position With Convolutional Neural Networks and Body Surface Potentials |
title_sort | non-invasive estimation of atrial fibrillation driver position with convolutional neural networks and body surface potentials |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8552066/ https://www.ncbi.nlm.nih.gov/pubmed/34721065 http://dx.doi.org/10.3389/fphys.2021.733449 |
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