OBJECTIVE: Local activation time (LAT) mapping of the atria is important for targeted treatment of atrial arrhythmias, but current methods do not interpolate on the atrial manifold and neglect uncertainties associated with LAT observations. In this paper, we describe novel methods to, first, quantify uncertainties in LAT arising from bipolar electrogram analysis and assignment of electrode recordings to the anatomical mesh, second, interpolate uncertain LAT measurements directly on left atrial manifolds to obtain complete probabilistic activation maps, and finally, interpolate LAT jointly across both the manifold and different S1-S2 pacing protocols. METHODS: A modified center of mass approach was used to process bipolar electrograms, yielding a LAT estimate and error distribution from the electrogram morphology. An error distribution for assigning measurements to the anatomical mesh was estimated. Probabilistic LAT maps were produced by interpolating on a left atrial manifold using Gaussian Markov random fields, taking into account observation errors and characterizing LAT predictions by their mean and standard deviation. This approach was extended to interpolate across S1-S2 pacing protocols. RESULTS: We evaluated our approach using recordings from three patients undergoing atrial ablation. Cross-validation showed consistent and accurate prediction of LAT observations both at different locations on the left atrium and for different S1-S2 intervals. SIGNIFICANCE: Interpolation of scalar and vector fields across anatomical structures from point measurements is a challenging problem in biomedical engineering, compounded by uncertainties in measurements and meshes. New methods and approaches are required, and in this paper, we have demonstrated an effective method for probabilistic interpolation of uncertain LAT.
OBJECTIVE: Local activation time (LAT) mapping of the atria is important for targeted treatment of atrial arrhythmias, but current methods do not interpolate on the atrial manifold and neglect uncertainties associated with LAT observations. In this paper, we describe novel methods to, first, quantify uncertainties in LAT arising from bipolar electrogram analysis and assignment of electrode recordings to the anatomical mesh, second, interpolate uncertain LAT measurements directly on left atrial manifolds to obtain complete probabilistic activation maps, and finally, interpolate LAT jointly across both the manifold and different S1-S2 pacing protocols. METHODS: A modified center of mass approach was used to process bipolar electrograms, yielding a LAT estimate and error distribution from the electrogram morphology. An error distribution for assigning measurements to the anatomical mesh was estimated. Probabilistic LAT maps were produced by interpolating on a left atrial manifold using Gaussian Markov random fields, taking into account observation errors and characterizing LAT predictions by their mean and standard deviation. This approach was extended to interpolate across S1-S2 pacing protocols. RESULTS: We evaluated our approach using recordings from three patients undergoing atrial ablation. Cross-validation showed consistent and accurate prediction of LAT observations both at different locations on the left atrium and for different S1-S2 intervals. SIGNIFICANCE: Interpolation of scalar and vector fields across anatomical structures from point measurements is a challenging problem in biomedical engineering, compounded by uncertainties in measurements and meshes. New methods and approaches are required, and in this paper, we have demonstrated an effective method for probabilistic interpolation of uncertain LAT.
Authors: Richard H Clayton; Yasser Aboelkassem; Chris D Cantwell; Cesare Corrado; Tammo Delhaas; Wouter Huberts; Chon Lok Lei; Haibo Ni; Alexander V Panfilov; Caroline Roney; Rodrigo Weber Dos Santos Journal: Philos Trans A Math Phys Eng Sci Date: 2020-05-25 Impact factor: 4.226
Authors: Mark Nothstein; Armin Luik; Amir Jadidi; Jorge Sánchez; Laura A Unger; Eike M Wülfers; Olaf Dössel; Gunnar Seemann; Claus Schmitt; Axel Loewe Journal: Front Physiol Date: 2021-05-24 Impact factor: 4.566
Authors: Sam Coveney; Cesare Corrado; Caroline H Roney; Daniel O'Hare; Steven E Williams; Mark D O'Neill; Steven A Niederer; Richard H Clayton; Jeremy E Oakley; Richard D Wilkinson Journal: Philos Trans A Math Phys Eng Sci Date: 2020-05-25 Impact factor: 4.226
Authors: Caroline H Roney; Charles Sillett; John Whitaker; Jose Alonso Solis Lemus; Iain Sim; Irum Kotadia; Mark O'Neill; Steven E Williams; Steven A Niederer Journal: Eur Heart J Cardiovasc Imaging Date: 2021-12-18 Impact factor: 6.875
Authors: Lia Gander; Simone Pezzuto; Ali Gharaviri; Rolf Krause; Paris Perdikaris; Francisco Sahli Costabal Journal: Front Physiol Date: 2022-03-07 Impact factor: 4.566