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Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model

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Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model. / Neic, Aurel; Campos, Fernando O.; Prassl, Anton J.; Niederer, Steven A.; Bishop, Martin J.; Vigmond, Edward J.; Plank, Gernot.

In: JOURNAL OF COMPUTATIONAL PHYSICS, Vol. 346, 01.10.2017, p. 191-211.

Research output: Contribution to journalArticle

Harvard

Neic, A, Campos, FO, Prassl, AJ, Niederer, SA, Bishop, MJ, Vigmond, EJ & Plank, G 2017, 'Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model', JOURNAL OF COMPUTATIONAL PHYSICS, vol. 346, pp. 191-211. https://doi.org/10.1016/j.jcp.2017.06.020

APA

Neic, A., Campos, F. O., Prassl, A. J., Niederer, S. A., Bishop, M. J., Vigmond, E. J., & Plank, G. (2017). Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model. JOURNAL OF COMPUTATIONAL PHYSICS, 346, 191-211. https://doi.org/10.1016/j.jcp.2017.06.020

Vancouver

Neic A, Campos FO, Prassl AJ, Niederer SA, Bishop MJ, Vigmond EJ et al. Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model. JOURNAL OF COMPUTATIONAL PHYSICS. 2017 Oct 1;346:191-211. https://doi.org/10.1016/j.jcp.2017.06.020

Author

Neic, Aurel ; Campos, Fernando O. ; Prassl, Anton J. ; Niederer, Steven A. ; Bishop, Martin J. ; Vigmond, Edward J. ; Plank, Gernot. / Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model. In: JOURNAL OF COMPUTATIONAL PHYSICS. 2017 ; Vol. 346. pp. 191-211.

Bibtex Download

@article{3f2f437b9cee41da9c8f5f2941bdba28,
title = "Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model",
abstract = "Anatomically accurate and biophysically detailed bidomain models of the human heart have proven a powerful tool for gaining quantitative insight into the links between electrical sources in the myocardium and the concomitant current flow in the surrounding medium as they represent their relationship mechanistically based on first principles. Such models are increasingly considered as a clinical research tool with the perspective of being used, ultimately, as a complementary diagnostic modality. An important prerequisite in many clinical modeling applications is the ability of models to faithfully replicate potential maps and electrograms recorded from a given patient. However, while the personalization of electrophysiology models based on the gold standard bidomain formulation is in principle feasible, the associated computational expenses are significant, rendering their use incompatible with clinical time frames. In this study we report on the development of a novel computationally efficient reaction-eikonal (R-E) model for modeling extracellular potential maps and electrograms. Using a biventricular human electrophysiology model, which incorporates a topologically realistic His-Purkinje system (HPS), we demonstrate by comparing against a high-resolution reaction-diffusion (R-D) bidomain model that the R-E model predicts extracellular potential fields, electrograms as well as ECGs at the body surface with high fidelity and offers vast computational savings greater than three orders of magnitude. Due to their efficiency R-E models are ideally suitable for forward simulations in clinical modeling studies which attempt to personalize electrophysiological model features.",
keywords = "Cardiac electrophysiology, Bidomain model, Eikonal model, Electrical activation and repolarization",
author = "Aurel Neic and Campos, {Fernando O.} and Prassl, {Anton J.} and Niederer, {Steven A.} and Bishop, {Martin J.} and Vigmond, {Edward J.} and Gernot Plank",
year = "2017",
month = oct,
day = "1",
doi = "10.1016/j.jcp.2017.06.020",
language = "English",
volume = "346",
pages = "191--211",
journal = "JOURNAL OF COMPUTATIONAL PHYSICS",
issn = "0021-9991",
publisher = "ACADEMIC PRESS INC",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model

AU - Neic, Aurel

AU - Campos, Fernando O.

AU - Prassl, Anton J.

AU - Niederer, Steven A.

AU - Bishop, Martin J.

AU - Vigmond, Edward J.

AU - Plank, Gernot

PY - 2017/10/1

Y1 - 2017/10/1

N2 - Anatomically accurate and biophysically detailed bidomain models of the human heart have proven a powerful tool for gaining quantitative insight into the links between electrical sources in the myocardium and the concomitant current flow in the surrounding medium as they represent their relationship mechanistically based on first principles. Such models are increasingly considered as a clinical research tool with the perspective of being used, ultimately, as a complementary diagnostic modality. An important prerequisite in many clinical modeling applications is the ability of models to faithfully replicate potential maps and electrograms recorded from a given patient. However, while the personalization of electrophysiology models based on the gold standard bidomain formulation is in principle feasible, the associated computational expenses are significant, rendering their use incompatible with clinical time frames. In this study we report on the development of a novel computationally efficient reaction-eikonal (R-E) model for modeling extracellular potential maps and electrograms. Using a biventricular human electrophysiology model, which incorporates a topologically realistic His-Purkinje system (HPS), we demonstrate by comparing against a high-resolution reaction-diffusion (R-D) bidomain model that the R-E model predicts extracellular potential fields, electrograms as well as ECGs at the body surface with high fidelity and offers vast computational savings greater than three orders of magnitude. Due to their efficiency R-E models are ideally suitable for forward simulations in clinical modeling studies which attempt to personalize electrophysiological model features.

AB - Anatomically accurate and biophysically detailed bidomain models of the human heart have proven a powerful tool for gaining quantitative insight into the links between electrical sources in the myocardium and the concomitant current flow in the surrounding medium as they represent their relationship mechanistically based on first principles. Such models are increasingly considered as a clinical research tool with the perspective of being used, ultimately, as a complementary diagnostic modality. An important prerequisite in many clinical modeling applications is the ability of models to faithfully replicate potential maps and electrograms recorded from a given patient. However, while the personalization of electrophysiology models based on the gold standard bidomain formulation is in principle feasible, the associated computational expenses are significant, rendering their use incompatible with clinical time frames. In this study we report on the development of a novel computationally efficient reaction-eikonal (R-E) model for modeling extracellular potential maps and electrograms. Using a biventricular human electrophysiology model, which incorporates a topologically realistic His-Purkinje system (HPS), we demonstrate by comparing against a high-resolution reaction-diffusion (R-D) bidomain model that the R-E model predicts extracellular potential fields, electrograms as well as ECGs at the body surface with high fidelity and offers vast computational savings greater than three orders of magnitude. Due to their efficiency R-E models are ideally suitable for forward simulations in clinical modeling studies which attempt to personalize electrophysiological model features.

KW - Cardiac electrophysiology

KW - Bidomain model

KW - Eikonal model

KW - Electrical activation and repolarization

UR - http://www.scopus.com/inward/record.url?scp=85021081806&partnerID=8YFLogxK

U2 - 10.1016/j.jcp.2017.06.020

DO - 10.1016/j.jcp.2017.06.020

M3 - Article

VL - 346

SP - 191

EP - 211

JO - JOURNAL OF COMPUTATIONAL PHYSICS

JF - JOURNAL OF COMPUTATIONAL PHYSICS

SN - 0021-9991

ER -

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