ArticleScientific reports2022
Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Real-time GPU-accelerated coupled cardiac system: Integrating bidirectional interactions between living optogenetic monolayers and computational simulations.PLoS computational biology · 2026Article
- Rapid calibration of atrial electrophysiology models using Gaussian process emulators in the ensemble Kalman filter.Scientific reports · 2026Article
- Sensitivity analysis of factors in a microfluidics CFD model of coagulation and cardiac applications.Biomechanics and modeling in mechanobiology · 2026Article
- An efficient end-to-end computational framework for the generation of ECG calibrated volumetric models of human atrial electrophysiology.Medical image analysis · 2026Article
- Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Review
- Survey and perspective on verification, validation, and uncertainty quantification of digital twins for precision medicine.NPJ digital medicine · 2025Review
- A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data.Arrhythmia & electrophysiology review · 2024Review
Corrections and comments
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Authors and funding
7 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.