2022/06/08 by Sam Coveney, Coveney, Sam, Caroline H. Roney +11 · 1 citation
Medicine · Neuroscience · #Advanced MRI Techniques and Applications #Applications (stat.AP) #Cardiac electrophysiology and arrhythmias #FOS: Computer and information sciences #Functional Brain Connectivity Studies
paper · doi:10.48550/arxiv.2206.03804
openalex publication_date 2022/06/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.