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Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective

PURPOSE: Effective identification of electrical drivers within remodeled tissue is a key for improving ablation treatment for atrial fibrillation. We have developed a mutual information, graph-based approach to identify and propose fault tolerance metric of local efficiency as a distinguishing featu...

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Autores principales: Sha, Qun, Elliott, Luizetta, Zhang, Xiangming, Levy, Tzachi, Sharma, Tushar, Abdelaal, Ahmed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9470649/
https://www.ncbi.nlm.nih.gov/pubmed/34981289
http://dx.doi.org/10.1007/s10840-021-01101-z
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author Sha, Qun
Elliott, Luizetta
Zhang, Xiangming
Levy, Tzachi
Sharma, Tushar
Abdelaal, Ahmed
author_facet Sha, Qun
Elliott, Luizetta
Zhang, Xiangming
Levy, Tzachi
Sharma, Tushar
Abdelaal, Ahmed
author_sort Sha, Qun
collection PubMed
description PURPOSE: Effective identification of electrical drivers within remodeled tissue is a key for improving ablation treatment for atrial fibrillation. We have developed a mutual information, graph-based approach to identify and propose fault tolerance metric of local efficiency as a distinguishing feature of rotational activation and remodeled atrial tissue. METHODS: Voltage data were extracted from atrial tissue simulations (2D Karma, 3D physiological, and the Multiscale Cardiac Simulation Framework (MSCSF)) using multi-spline open and parallel regional mapping catheter geometries. Graphs were generated based on varied mutual information thresholds between electrode pairs and the local efficiency for each graph was calculated. RESULTS: High-resolution mapping catheter geometries can distinguish between rotational and irregular activation patterns using the derivative of local efficiency as a function of increasing mutual information threshold. The derivative is decreased for rotational activation patterns comparing to irregular activations in both a simplified 2D model (0.0017 ± 1 × 10(−4) vs. 0.0032 ± 1 × 10(−4), p < 0.01) and a more realistic 3D model (0.00092 ± 5 × 10(−5) vs. 0.0014 ± 4 × 10(−5), p < 0.01). Average local efficiency derivative can also distinguish between degrees of remodeling. Simulations using the MSCSF model, with 10 vs. 90% remodeling, display distinct derivatives in the grid design parallel spline catheter configuration (0.0015 ± 5 × 10(−5) vs. 0.0019 ± 6 × 10(−5), p < 0.01) and the flower shaped open spline configuration (0.0011 ± 5 × 10(−5) vs. 0.0016 ± 4 × 10(−5), p < 0.01). CONCLUSION: A decreased derivative of local efficiency characterizes rotational activation and varies with atrial remodeling. This suggests a distinct communication pattern in cardiac rotational activation detectable via high-resolution regional mapping and could enable identification of electrical drivers for targeted ablation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10840-021-01101-z.
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spelling pubmed-94706492022-09-15 Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective Sha, Qun Elliott, Luizetta Zhang, Xiangming Levy, Tzachi Sharma, Tushar Abdelaal, Ahmed J Interv Card Electrophysiol Article PURPOSE: Effective identification of electrical drivers within remodeled tissue is a key for improving ablation treatment for atrial fibrillation. We have developed a mutual information, graph-based approach to identify and propose fault tolerance metric of local efficiency as a distinguishing feature of rotational activation and remodeled atrial tissue. METHODS: Voltage data were extracted from atrial tissue simulations (2D Karma, 3D physiological, and the Multiscale Cardiac Simulation Framework (MSCSF)) using multi-spline open and parallel regional mapping catheter geometries. Graphs were generated based on varied mutual information thresholds between electrode pairs and the local efficiency for each graph was calculated. RESULTS: High-resolution mapping catheter geometries can distinguish between rotational and irregular activation patterns using the derivative of local efficiency as a function of increasing mutual information threshold. The derivative is decreased for rotational activation patterns comparing to irregular activations in both a simplified 2D model (0.0017 ± 1 × 10(−4) vs. 0.0032 ± 1 × 10(−4), p < 0.01) and a more realistic 3D model (0.00092 ± 5 × 10(−5) vs. 0.0014 ± 4 × 10(−5), p < 0.01). Average local efficiency derivative can also distinguish between degrees of remodeling. Simulations using the MSCSF model, with 10 vs. 90% remodeling, display distinct derivatives in the grid design parallel spline catheter configuration (0.0015 ± 5 × 10(−5) vs. 0.0019 ± 6 × 10(−5), p < 0.01) and the flower shaped open spline configuration (0.0011 ± 5 × 10(−5) vs. 0.0016 ± 4 × 10(−5), p < 0.01). CONCLUSION: A decreased derivative of local efficiency characterizes rotational activation and varies with atrial remodeling. This suggests a distinct communication pattern in cardiac rotational activation detectable via high-resolution regional mapping and could enable identification of electrical drivers for targeted ablation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10840-021-01101-z. Springer US 2022-01-04 2022 /pmc/articles/PMC9470649/ /pubmed/34981289 http://dx.doi.org/10.1007/s10840-021-01101-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Sha, Qun
Elliott, Luizetta
Zhang, Xiangming
Levy, Tzachi
Sharma, Tushar
Abdelaal, Ahmed
Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective
title Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective
title_full Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective
title_fullStr Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective
title_full_unstemmed Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective
title_short Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective
title_sort atrial fibrillation driver identification through regional mutual information networks: a modeling perspective
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9470649/
https://www.ncbi.nlm.nih.gov/pubmed/34981289
http://dx.doi.org/10.1007/s10840-021-01101-z
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