Fast and Reliable Reduced-Order Models for Cardiac Electrophysiology

TL;DR

This paper presents an adaptive, projection-based surrogate model for cardiac electrophysiology that uses an a posteriori error estimator to efficiently and accurately simulate heart electrical activity, aiding medical diagnosis and treatment.

Contribution

It introduces an adaptive surrogate modeling approach with an error estimator for efficient simulation of cardiac electrophysiology, improving over traditional methods.

Findings

The surrogate model achieves high accuracy in benchmark tests.

The adaptive approach reduces computational cost significantly.

The error estimator effectively guides model updates.

Abstract

Mathematical models of the human heart are increasingly playing a vital role in understanding the working mechanisms of the heart, both under healthy functioning and during disease. The aim is to aid medical practitioners diagnose and treat the many ailments affecting the heart. Towards this, modelling cardiac electrophysiology is crucial as the heart's electrical activity underlies the contraction mechanism and the resulting pumping action. The governing equations and the constitutive laws describing the electrical activity in the heart are coupled, nonlinear, and involve a fast moving wave front, which is generally solved by the finite element method. The simulation of this complex system as part of a virtual heart model is challenging due to the necessity of fine spatial and temporal resolution of the domain. Therefore, efficient surrogate models are needed to predict the dynamics under varying parameters and inputs. In this work, we develop an adaptive, projection-based surrogate model for cardiac electrophysiology. We introduce an a posteriori error estimator that can accurately and efficiently quantify the accuracy of the surrogate model. Using the error estimator, we systematically update our surrogate model through a greedy search of the parameter space. Furthermore, using the error estimator, the parameter search space is dynamically updated such that the most relevant samples get chosen at every iteration. The proposed adaptive surrogate modelling technique is tested on three benchmark models to illustrate its efficiency, accuracy, and ability of generalization.