Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model Article - Octobre 2017

Aurel Neic, Fernando Campos, Anton Prassl, Steven Niederer, Martin Bishop, Edward Vigmond, Gernot Plank

Aurel Neic, Fernando Campos, Anton Prassl, Steven Niederer, Martin Bishop, Edward Vigmond, Gernot Plank, « Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model  », Journal of Computational Physics, octobre 2017, pp. 191-211. ISSN 0021-9991

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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