Gaussian process manifold interpolation for probabilistic atrial activation maps and uncertain conduction velocity

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 at...

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Published in:Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences physical, and engineering sciences, 2020-06, Vol.378 (2173), p.20190345-20190345
Main Authors: Coveney, Sam, Corrado, Cesare, Roney, Caroline H, O'Hare, Daniel, Williams, Steven E, O'Neill, Mark D, Niederer, Steven A, Clayton, Richard H, Oakley, Jeremy E, Wilkinson, Richard D
Format: Article
Language:English
Subjects:
Atrial Fibrillation - pathology
Atrial Fibrillation - physiopathology
Atrial Function
Heart Atria - pathology
Heart Atria - physiopathology
Heart Conduction System - physiology
Heart Conduction System - physiopathology
Humans
Models, Cardiovascular
Normal Distribution
Probability
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Summary: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'.
ISSN:1364-503X
1471-2962
DOI:10.1098/rsta.2019.0345