Manifold Approximating Graph Interpolation of Cardiac Local Activation Time

Abstract

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.

The Impact of Electrogram Type and Conduction Velocity Estimation Techniques on Assessments of Conduction Velocity during Ventricular Substrate Mapping

“…The variation in the CV results among the selected techniques shown in Figure 1 (B, C, D, and E) may be due to interpolation, averaging, and the locality of the CV estimation [13]. For instance, different interpolation methods yield perceived differences in LAT maps [14]. The gradient method estimates CV over a small area of the mesh (a single element), so it provides a local measurement and can detect areas of CV heterogeneity; however, it is dependent on LAT interpolation, so more prone to error.…”

A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data

“…Furthermore, the surface of the heart is an irregular domain that is hard to represent mathematically and leverage for interpolation. [ 44 ]…”

“…Furthermore, the surface of the heart is an irregular domain that is hard to represent mathematically and leverage for interpolation. 44 However, in contrast to signal annotation, users cannot currently modify interpolation schemes in clinical practice, making their impact more difficult to study. Various interpolation schemes have already been reviewed, along with their effects on EAM.…”

“…37,[45][46][47][48] A comparison of current techniques aimed at the reconstruction of activation maps using sparse EAM recordings is shown in Table 2. 44,[49][50][51][52] Conduction Velocity Estimation…”