A technique for measuring anisotropy in atrial conduction to estimate conduction velocity and atrial fibre direction

Abstract

Background Cardiac conduction properties exhibit large variability, and affect patient-specific arrhythmia mechanisms. However, it is challenging to clinically measure conduction velocity (CV), anisotropy and fibre direction. Our aim is to develop a technique to estimate conduction anisotropy and fibre direction from clinically available electrical recordings. Methods We developed and validated automated algorithms for estimating cardiac CV anisotropy, from any distribution of recording locations on the atrial surface. The first algorithm is for elliptical wavefront fitting to a single activation map (method 1), which works well close to the pacing location, but decreases in accuracy further from the pacing location (due to spatial heterogeneity in the conductivity and fibre fields). As such, we developed a second methodology for measuring local conduction anisotropy, using data from two or three activation maps (method 2: ellipse fitting to wavefront propagation velocity vectors from multiple activation maps). Results Ellipse fitting to CV vectors from two activation maps (method 2) leads to an improved estimation of longitudinal and transverse CV compared to method 1, but fibre direction estimation is still relatively poor. Using three activation maps with method 2 provides accurate estimation, with approximately of atrial fibres estimated within . We applied the technique to clinical activation maps to demonstrate the presence of heterogeneous conduction anisotropy, and then tested the effects of this conduction anisotropy on predicted arrhythmia dynamics using computational simulation. Conclusions We have developed novel algorithms for calculating CV and measuring the direction dependency of atrial activation to estimate atrial fibre direction, without the need for specialised pacing protocols, using clinically available electrical recordings.

Physics-Informed Neural Networks for Cardiac Activation Mapping

“…This is a critical step toward reliably estimating conduction velocities and avoiding artificially high conduction velocity values. Our methodology directly predicts conduction velocities, without the need of creating ad-hoc techniques [36]. Further, it allows us to quantify the uncertainty in our predictions via randomized prior functions, which represents a useful tool in the clinical setting [3].…”

Probabilistic Interpolation of Uncertain Local Activation Times on Human Atrial Manifolds

“…Electrograms are recorded using a multipolar catheter which records electrical activity on the endocardial surface at one or more locations, sometimes for multiple pacing protocols and catheter positions. LAT maps can be used to calculate conduction velocity maps [3], and different pacing protocols allow the dynamic response of the electro-anatomical substrate to be inferred [4]. Although advances in catheter design have increased the number of recordings that can be made simultaneously, longer procedure times pose a risk to the patient and a cost for the health care system, which places limits on the spatial density of the mapping observations, as well as how many pacing protocols and pacing locations can be used.…”

Challenges Associated with Interpreting Mechanisms of AF

“…Activation time maps can be post-processed to calculate conduction velocity maps. [51][52][53] Electrogram Features…”

“…Re-entry anchor location and driver formation may also depend on electrophysiology, conduction velocity dynamics, cardiac wavelength and anisotropy. 52,[121][122][123][124] approach that aims to streamline activation patterns. 127 Roney et al…”