ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2020
Gaussian process manifold interpolation for probabilistic atrial activation maps and uncertain conduction velocity.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Rapid calibration of atrial electrophysiology models using Gaussian process emulators in the ensemble Kalman filter.Scientific reports · 2026Article
- High-density evaluation of the arrhythmogenic substrate in persistent atrial fibrillation.Heart rhythm O2 · 2025Article
- BOATMAP: Bayesian Optimization Active Targeting for Monomorphic Arrhythmia Pace-mapping.Computers in biology and medicine · 2024Article
- Article
- A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data.Arrhythmia & electrophysiology review · 2024Review
- Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds.Scientific reports · 2022Article
- Manifold Approximating Graph Interpolation of Cardiac Local Activation Time.IEEE transactions on bio-medical engineering · 2022Article
- Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate.Medical & biological engineering & computing · 2022Review
- Fast Characterization of Inducible Regions of Atrial Fibrillation Models With Multi-Fidelity Gaussian Process Classification.Frontiers in physiology · 2022Article
- Applications of multimodality imaging for left atrial catheter ablation.European heart journal. Cardiovascular Imaging · 2021Review
- Identifying Atrial Fibrillation Mechanisms for Personalized Medicine.Journal of clinical medicine · 2021Review
- Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks.Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH (Conference) · 2021Article
- Enabling forward uncertainty quantification and sensitivity analysis in cardiac electrophysiology by reduced order modeling and machine learning.International journal for numerical methods in biomedical engineering · 2021Article
- Data integration for the numerical simulation of cardiac electrophysiology.Pacing and clinical electrophysiology : PACE · 2021Review
- Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators.Frontiers in physiology · 2021Article
- PIEMAP: Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps.Statistical atlases and computational models of the heart. STACOM (Workshop) · 2021Article
- The fickle heart: uncertainty quantification in cardiac and cardiovascular modelling and simulation.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020Article
- Article
- Dispersion of repolarization increases with cardiac resynchronization therapy and is associated with left ventricular reverse remodeling.Journal of electrocardiologyArticle
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Authors and funding
10 authors.
Funding
Abstract
In patients with atrial fibrillation, local activation time (LAT) maps are routinely used for characterizing patient pathophysiology. The gradient of LAT maps can be used to calculate conduction velocity (CV), which directly relates to material conductivity and may provide an important measure of atrial substrate properties. Including uncertainty in CV calculations would help with interpreting the reliability of these measurements. Here, we build upon a recent insight into reduced-rank Gaussian processes (GPs) to perform probabilistic interpolation of uncertain LAT directly on human atrial manifolds. Our Gaussian process manifold interpolation (GPMI) method accounts for the topology of the atrium, and allows for calculation of statistics for predicted CV. We demonstrate our method on two clinical cases, and perform validation against a simulated ground truth. CV uncertainty depends on data density, wave propagation direction and CV magnitude. GPMI is suitable for probabilistic interpolation of other uncertain quantities on non-Euclidean manifolds. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
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