Tracking of Atrial Fibrillation Drivers Based on Propagation Patterns: an In-Silico Study

Victor Gonçalves Marques, Ali Gharaviri, Simone Pezzuto, Angelo Auricchio, Pietro Bonizzi, Stef Zeemering, Ulrich Schotten

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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In some persistent atrial fibrillation (AF) patients, localized drivers may sustain AF and thus could represent possible ablation targets. In this work, we test in silico the feasibility of locating AF drivers from high-density electrode grid catheter mapping. A volumetric 3D atrial model was used to simulate 8 AF episodes driven by a stable reentry around a region of scar tissue (5 left atrium [LA], 3 right atrium [RA]). Sequential mapping in 1s segments was performed with a high-density electrode grid, starting from 20 uniformly distributed regions (12 LA, 8 RA). Conduction velocities estimated for each AF cycle were used to obtain temporal and directional parameters of the propagation. Trajectories of connected activation times were used to detect reentries or radial spread of activations. If no pattern was detected, the electrode array was moved in 5mm steps upstream of the propagation direction. The algorithm obtained accuracy, sensitivity, and precision of 87.2%, 23.4%, and 56.3% for reentries and 87.0%, 8.5%, and 26.8% for radial spread of activations, respectively. Reentries were found in average within 1.52 steps15 mm from the initial position of the grid. The results indicate that propagation patterns may be sufficient to track localized AF drivers sequentially during high-density mapping.

Original languageEnglish
Title of host publication2022 Computing in Cardiology (CinC)
Number of pages4
Publication statusPublished - 2022
Event49th Computing in Cardiology Conference 2022 - Tampere Hall, Tampere, Finland
Duration: 4 Sept 20227 Sept 2022
Conference number: 49


Conference49th Computing in Cardiology Conference 2022
Abbreviated titleCinC 2022
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