A fast computational model for the electrophysiology of the whole human heart

TL;DR

This paper introduces a fast, GPU-accelerated computational model for simulating the entire human heart's electrophysiology, integrating multiple conductive media and cellular models for accurate and efficient analysis of cardiac function.

Contribution

The novel model combines heterogeneous cardiac structures with GPU acceleration, enabling unprecedented speed and detailed simulations of whole-heart electrophysiology.

Findings

Successfully reproduces healthy and pathological cardiac electrophysiology

Achieves significant speedup with GPU acceleration for large-scale simulations

Enables systematic studies and optimization of therapies

Abstract

In this study we present a novel computational model for unprecedented simulations of the whole cardiac electrophysiology. According to the heterogeneous electrophysiologic properties of the heart, the whole cardiac geometry is decomposed into a set of coupled conductive media having different topology and electrical conductivities: (i) a network of slender bundles comprising a fast conduction atrial network, the AV-node and the ventricular bundles; (ii) the Purkinje network; and (iii) the atrial and ventricular myocardium. The propagation of the action potential in these conductive media is governed by the bidomain/monodomain equations, which are discretized in space using an in-house finite volume method and coupled to three different cellular models, the Courtemanche model [1] for the atrial myocytes, the Stewart model [2] for the Purkinje Network and the ten Tusscher-Panfilov model [3] for the ventricular myocytes. The developed numerical model correctly reproduces the cardiac electrophysiology of the whole human heart in healthy and pathologic conditions and it can be tailored to study and optimize resynchronization therapies or invasive surgical procedures. Importantly, the whole solver is GPU-accelerated using CUDA Fortran providing an unprecedented speedup, thus opening the way for systematic parametric studies and uncertainty quantification analyses.