Key result
The proposed conduction velocity estimation algorithm increased the percentage of nodes with correctly estimated conduction velocity compared to the reference inverse spatial gradient method (78.1% vs 60.8%).
Why the study?
Measurement of conduction velocity is challenging during atypical or irregular activation patterns, such as during tachycardia and fibrillation, because of the complexity of the underlying mechanics.
Does the proposed CV estimation algorithm improve the accuracy of conduction velocity estimation compared to the reference inverse spatial gradient algorithm in a computational model of atrial reentries?
Does the proposed CV estimation algorithm improve the accuracy of conduction velocity estimation compared to the reference inverse spatial gradient algorithm in a computational model of atrial reentries?
Absolute Event Rate: 78.1% vs 60.8%
A novel algorithm for conduction velocity estimation improves accuracy during complex arrhythmias like reentries and wavefront collisions in computational models.
Improves CV estimation accuracy in computational reentry models; leaves open clinical translation and validation.
Conduction velocity (CV) is an important electrophysiological biomarker for identifying slowconducting cardiac regions related to pro-arrhythmic behavior. Measurement of CV is challenging during atypical or irregular activation patterns, such as those present during tachycardia and fibrillation because of the complexity of the underlying mechanics. We propose an approach for CV estimation designed for taking into account reentries and wavefront collisions. The algorithm is based on a set of constraints imposed on the velocity vector fields. The performance of the proposed algorithm was evaluated against the reference inverse spatial gradient algorithm by using an eikonal reentrant computational model over a left atria anatomy with different mesh resolutions and complexity of the propagation patterns. Our newly proposed CV mapping is able to identify anchoring zones and the collision of wavefronts without an overestimation of these regions.The performance of our proposed method does improve the reference inverse spatial gradient, with an increased percentage of nodes in which CV is estimated correctly (60.8 7.1 % vs 78.1 10.7 %).
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Hernández-Romero et al. (2022) studied Cardiac arrhythmias (computational model). Proposed CV estimation algorithm (inverse spatial gradient with constraints and RBF interpolation) vs. Reference inverse spatial gradient algorithm was evaluated on Percentage of nodes in which CV is estimated correctly. The proposed conduction velocity estimation algorithm increased the percentage of nodes with correctly estimated conduction velocity compared to the reference inverse spatial gradient method (78.1% vs 60.8%).
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