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

Research output: Contribution to journalArticle

Aurel Neic, Fernando O. Campos, Anton J. Prassl, Steven A. Niederer, Martin J. Bishop, Edward J. Vigmond, Gernot Plank

Original languageEnglish
Pages (from-to)191-211
Number of pages21
JournalJOURNAL OF COMPUTATIONAL PHYSICS
Volume346
Early online date15 Jun 2017
DOIs
Publication statusPublished - 1 Oct 2017

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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.

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