Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds

TL;DR

This paper introduces a probabilistic framework using Gaussian processes on atrial manifolds to calibrate cardiac electrophysiology models from limited clinical measurements, enabling personalized heart modeling.

Contribution

It presents a novel method for calibrating electrophysiology parameters on the heart's atrial surface using Gaussian processes and surrogate functions, improving model personalization.

Findings

Successfully recovers synthetic parameter fields from measurements

Applicable to various clinical measurement types

Framework generalizes to other calibration problems on manifolds

Abstract

Models of electrical excitation and recovery in the heart have become increasingly detailed, but have yet to be used routinely in the clinical setting to guide personalized intervention in patients. One of the main challenges is calibrating models from the limited measurements that can be made in a patient during a standard clinical procedure. In this work, we propose a novel framework for the probabilistic calibration of electrophysiology parameters on the left atrium of the heart using local measurements of cardiac excitability. Parameter fields are represented as Gaussian processes on manifolds and are linked to measurements via surrogate functions that map from local parameter values to measurements. The posterior distribution of parameter fields is then obtained. We show that our method can recover parameter fields used to generate localised synthetic measurements of effective refractory period. Our methodology is applicable to other measurement types collected with clinical protocols, and more generally for calibration where model parameters vary over a manifold.