Graph-Based Interpolation of Local Activation Time on the Cardiac Surface

Hellar, Jennifer; Cosentino, Romain; John, Mathews; Post, Allison; Buchan, Skylar; Razavi, Mehdi; Aazhang, Behnaam

doi:10.1109/ieeeconf53345.2021.9723133

Cited by 1 publication

(1 citation statement)

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“…This method, Manifold Approximating Graph Interpolation of Cardiac LAT (MAGIC-LAT), follows directly from an intuitive re-formulation of the interpolation problem into a semi-supervised learning framework which incorporates key results in the fields of graph signal processing [29], [30] and geometric manifold learning [31]- [35]. A preliminary version of this work has been reported [36].…”

Section: Introductionmentioning

confidence: 99%

Manifold Approximating Graph Interpolation of Cardiac Local Activation Time

Hellar

Cosentino²,

John³

et al. 2022

IEEE Trans. Biomed. Eng.

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Local activation time (LAT) mapping of cardiac chambers is vital for targeted treatment of cardiac arrhythmias in catheter ablation procedures. Current methods require too many LAT observations for an accurate interpolation of the necessarily sparse LAT signal extracted from intracardiac electrograms (EGMs). Additionally, conventional performance metrics for LAT interpolation algorithms do not accurately measure the quality of interpolated maps. We propose, first, a novel method for spatial interpolation of the LAT signal which requires relatively few observations; second, a realistic sub-sampling protocol for LAT interpolation testing; and third, a new color-based metric for evaluation of interpolation quality that quantifies perceived differences in LAT maps. Methods: We utilize a graph signal processing framework to reformulate the irregular spatial interpolation problem into a semi-supervised learning problem on the manifold with a closed-form solution. The metric proposed uses a color difference equation and color theory to quantify visual differences in generated LAT maps. Results: We evaluate our approach on a dataset consisting of seven LAT maps from four patients obtained by the CARTO electroanatomic mapping system during premature ventricular complex (PVC) ablation procedures. Random sub-sampling and reinterpolation of the LAT observations show excellent accuracy for relatively few observations, achieving on average 6% lower error than state-of-the-art techniques for only 100 observations. Conclusion: Our study suggests that graph signal processing methods can improve LAT mapping for cardiac ablation procedures. Significance: The proposed method can reduce patient time in surgery by decreasing the number of LAT observations needed for an accurate LAT map.

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