Anatomically based procedures to ablate atrial fibrillation (AF) are often successful in terminating paroxysmal AF. However, the ability to terminate persistent AF remains disappointing. New mechanistic approaches use multiple-electrode basket catheter mapping to localize and target AF drivers in the form of rotors but significant concerns remain about their accuracy. We aimed to evaluate how electrode-endocardium distance, far-field sources and inter-electrode distance affect the accuracy of localizing rotors. Sustained rotor activation of the atria was simulated numerically and mapped using a virtual basket catheter with varying electrode densities placed at different positions within the atrial cavity. Unipolar electrograms were calculated on the entire endocardial surface and at each of the electrodes. Rotors were tracked on the interpolated basket phase maps and compared with the respective atrial voltage and endocardial phase maps, which served as references. Rotor detection by the basket maps varied between 35–94% of the simulation time, depending on the basket’s position and the electrode-to-endocardial wall distance. However, two different types of phantom rotors appeared also on the basket maps. The first type was due to the far-field sources and the second type was due to interpolation between the electrodes; increasing electrode density decreased the incidence of the second but not the first type of phantom rotors. In the simulations study, basket catheter-based phase mapping detected rotors even when the basket was not in full contact with the endocardial wall, but always generated a number of phantom rotors in the presence of only a single real rotor, which would be the desired ablation target. Phantom rotors may mislead and contribute to failure in AF ablation procedures.

Non-invasive localization of continuous atrial ectopic beats remains a cornerstone for the treatment of atrial arrhythmias. The lack of accurate tools to guide electrophysiologists leads to an increase in the recurrence rate of ablation procedures. Existing approaches are based on the analysis of the P-waves main characteristics and the forward body surface potential maps (BSPMs) or on the inverse estimation of the electric activity of the heart from those BSPMs. These methods have not provided an efficient and systematic tool to localize ectopic triggers. In this work, we propose the use of machine learning techniques to spatially cluster and classify ectopic atrial foci into clearly differentiated atrial regions by using the body surface P-wave integral map (BSPiM) as a biomarker. Our simulated results show that ectopic foci with similar BSPiM naturally cluster into differentiated non-intersected atrial regions and that new patterns could be correctly classified with an accuracy of 97% when considering 2 clusters and 96% for 4 clusters. Our results also suggest that an increase in the number of clusters is feasible at the cost of decreasing accuracy.

The electrical connections between the atrial coronary sinus (CS) IntroductionBody surface potential mapping (BSPM) systems offer non-invasive ways to explore the behaviour of the atria. However, it is still very difficult to determine the specific location of ectopic foci when disturbances appear on the recorded signals by only analysing the BSPM. Instead, some experimental studies [1,2] have built and exploited a database of clinical patterns obtained from patients who underwent paced mapping before a diagnostic electrophysiological study. These patterns correspond to body surface P-wave integral maps (BSPiM) where ectopic beats are paced in different atrial sites, and are used to infer how the atria is really activating and how its electrical contribution is observed on the torso surface. This indicator is well correlated with P-wave polarity and amplitude and more importantly, it is robust against fast polarity changes and interpatient variability. To be able to get insight into those clinical studies, biophysical models can be used to analyse the relationship between ectopic atrial activity and BSPiM. However, it requires a very accurate atrial model capable of reproducing existing clinical measurements. This atrial model must include all the anatomical regions to allow the electrical wavefront propagates following muscular connections.In this respect, several clinical studies of the human atria have histologically shown the existence of striated myocardial muscle at discrete locations along the sleeve of the coronary sinus (CS) that electrically connects CS and the left atrial (LA) myocardium [3,4]. The anatomy of these interatrial connections and their location have been previously shown as having high variability between patients. This leads to strong differences in the pathway followed by the atrial depolarization wavefront [3] and influences the BSPiM.Up to now, there is no consensus on the number and location of these muscular connections so 3D computational models of the human atrial anatomy and electrophysiology have not historically included the CS and its bridges to LA as an essential region responsible for the depolarization atrial pattern [5][6][7][8]. However, the influence of these bridges on the depolarization wavefront were partly taken into account in the multi-scale 3D human atrial-torso models previously developed by our group [9,10].The present study combines our most recent multi-scale 3D human atrial-torso model [9] with the analysis of BSPiMs with the aim of determining which configuration of the CS-LA connections reproduces more accurately the set of clinically observed body surface P-wave integral maps (BSPiM). 2.Material and methods Anatomical multi-scale modelOur 3D human atrial model consists of a computational finite element mesh with a homogeneous wall thickness between 600 and 900 μm (754.893 nodes and 515.005 elements), built with linear hexahedral elements and with

Introduction: Focal atrial tachycardia is commonly treated by radio frequency ablation with an acceptable long-term success. Although the location of ectopic foci tends to appear in specific hot-spots, they can be located virtually in any atrial region. Multi-electrode surface ECG systems allow acquiring dense body surface potential maps (BSPM) for non-invasive therapy planning of cardiac arrhythmia. However, the activation of the atria could be affected by fibrosis and therefore biomarkers based on BSPM need to take these effects into account. We aim to analyze the effect of fibrosis on a BSPM derived index, and its potential application to predict the location of ectopic foci in the atria.Methodology: We have developed a 3D atrial model that includes 5 distributions of patchy fibrosis in the left atrium at 5 different stages. Each stage corresponds to a different amount of fibrosis that ranges from 2 to 40%. The 25 resulting 3D models were used for simulation of Focal Atrial Tachycardia (FAT), triggered from 19 different locations described in clinical studies. BSPM were obtained for all simulations, and the body surface potential integral maps (BSPiM) were calculated to describe atrial activations. A machine learning (ML) pipeline using a supervised learning model and support vector machine was developed to learn the BSPM patterns of each of the 475 activation sequences and relate them to the origin of the FAT source.Results: Activation maps for stages with more than 15% of fibrosis were greatly affected, producing conduction blocks and delays in propagation. BSPiMs did not always cluster into non-overlapped groups since BSPiMs were highly altered by the conduction blocks. From stage 3 (15% fibrosis) the BSPiMs showed differences for ectopic beats placed around the area of the pulmonary veins. Classification results were mostly above 84% for all the configurations studied when a large enough number of electrodes were used to map the torso. However, the presence of fibrosis increases the area of the ectopic focus location and therefore decreases the utility for the electrophysiologist.Conclusions: The results indicate that the proposed ML pipeline is a promising methodology for non-invasive ectopic foci localization from BSPM signal even when fibrosis is present.