Identifying Potential Re-entrant Circuit Locations from Atrial Fibre Maps

Abstract

Re-entrant circuits have been identified as potential drivers of atrial fibrillation (AF). In this paper, we develop a novel computational framework for finding the locations of re-entrant circuits from high resolution fibre orientation data. The technique follows a statistical approach whereby we generate continuous fibre tracts across the tissue and couple adjacent fibres stochastically if they are within a given distance of each other. By varying the connection distance, we identify which regions are most susceptible to forming re-entrant circuits if muscle fibres are uncoupled, through the action of fibrosis or otherwise. Our results highlight the sleeves of the pulmonary veins, the posterior left atrium and the left atrial appendage as the regions most susceptible to re-entrant circuit formation. This is consistent with known risk locations in clinical AF. If the model can be personalised for individual patients undergoing ablation, future versions may be able to suggest suitable ablation targets.

Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid in silico and in vivo Dataset

“…While our approach shows promising results and highlights the essential features of intracardiac signals to characterize atrial substrate, validation through independent experimental and clinical data is desirable. Future studies could include LGE-MRI data to validate the proposed approach and explore the arrangement of the fibrotic tissue effect on the electrogram morphology (Sánchez et al, 2019b ).…”

“…Unipolar synthetic signals were filtered using a band-pass between 0.05 and 900 Hz. Afterward, bipolar electrograms were calculated by subtracting the signals from the corresponding pairs of electrodes and filtered by a clinically used band-pass filter between 30 and 300 Hz (Deno et al, 2017 ; Unger et al, 2019 ).…”

Identifying locations susceptible to micro-anatomical reentry using a spatial network representation of atrial fibre maps

“…These are found by applying a discrete diffusion model (DDM) to the network, see SM section 1.4. The DDM is related to several extremely simple physics models of micro-anatomical reentry [18–22]. These models are not electrophysiologically realistic models of AF.…”

“…The DDM is related to several extremely simple physics models of micro-anatomical reentry [18][19][20][21][22]. These models are not electrophysiologically realistic models of AF.…”

Inference of ventricular activation properties from non-invasive electrocardiography

“…Recent studies have shown the power of patient-specific image-based modelling and simulation for therapy guidance, arrhythmic biomarkers interpretation and patient's phenotypic variability interpretation ( Potse et al., 2014 ; Zettinig et al., 2014 ; Gillette et al., 2017 ; Kahlmann et al., 2017 ; Lyon et al., 2018 ; Bukhari et al., 2019 ; Niederer et al., 2019 ; Boyle et al., 2019 ; Martinez-Navarro et al., 2021 ). This technology has paved the way towards realising the ‘digital twin’ vision ( Corral-Acero et al., 2020 ), referring to a comprehensive virtual tool that coherently integrates a patient's clinical data with mechanistic physiological knowledge and that can inform therapeutic and diagnostic decision-making through simulations.…”