Key result
Probabilistic calibration framework successfully recovers atrial electrophysiology parameters from sparse synthetic measurements.
Why the study?
Detailed cardiac electrophysiology models are not routinely used clinically to guide personalized interventions, partly due to the challenge of calibrating models from limited clinical measurements.
A novel probabilistic calibration framework using latent Gaussian processes can accurately infer personalized atrial electrophysiology parameters from sparse clinical measurements, facilitating the creation of cardiac digital twins.
Supports digital twin creation from sparse atrial data; leaves open validation in human electrophysiology studies.
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.
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Coveney et al. (2022) studied Cardiac arrhythmia (computational modeling). Probabilistic calibration using latent Gaussian processes on manifolds vs. Ground truth (synthetic data) was evaluated on Prediction accuracy of effective refractory period (ERP) and electrophysiology parameter fields. A novel probabilistic calibration framework using latent Gaussian processes on atrial manifolds successfully recovered electrophysiology parameter fields from sparse synthetic measurements.
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