Atrial arrhythmias, and specifically atrial fibrillation (AF), induce rapid and irregular activation patterns that appear on the torso surface as abnormal P-waves in electrocardiograms and body surface potential maps (BSPM). In recent years both P-waves and the BSPM have been used to identify the mechanisms underlying AF, such as localizing ectopic foci or high-frequency rotors. However, the relationship between the activation of the different areas of the atria and the characteristics of the BSPM and P-wave signals are still far from being completely understood. In this work we developed a multi-scale framework, which combines a highly-detailed 3D atrial model and a torso model to study the relationship between atrial activation and surface signals in sinus rhythm. Using this multi scale model, it was revealed that the best places for recording P-waves are the frontal upper right and the frontal and rear left quadrants of the torso. Our results also suggest that only nine regions (of the twenty-one structures in which the atrial surface was divided) make a significant contribution to the BSPM and determine the main P-wave characteristics.

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The combination of machine learning methods together with computational modeling and simulation of the cardiovascular system brings the possibility of obtaining very valuable information about new therapies or clinical devices through in-silico experiments. However, the application of machine learning methods demands access to large cohorts of patients. As an alternative to medical data acquisition and processing, which often requires some degree of manual intervention, the generation of virtual cohorts made of synthetic patients can be automated. However, the generation of a synthetic sample can still be computationally demanding to guarantee that it is clinically meaningful and that it reflects enough inter-patient variability. This paper addresses the problem of generating virtual patient cohorts of thoracic aorta geometries that can be used for in-silico trials. In particular, we focus on the problem of generating a cohort of patients that meet a particular clinical criterion, regardless the access to a reference sample of that phenotype. We formalize the problem of clinically-driven sampling and assess several sampling strategies with two goals, sampling efficiency, i.e., that the generated individuals actually belong to the target population, and that the statistical properties of the cohort can be controlled. Our results show that generative adversarial networks can produce reliable, clinically-driven cohorts of thoracic aortas with good efficiency. Moreover, non-linear predictors can serve as an efficient alternative to the sometimes expensive evaluation of anatomical or functional parameters of the organ of interest.

Cardiac electromechanical simulations of the heart with fibers extracted from experimental data produce functional scores closer to healthy ranges than rule-based models disregarding architecture connectivity.

Modeling the cardiac conduction system is a challenging problem in the context of computational cardiac electrophysiology. Its ventricular section, the Purkinje system, is responsible for triggering tissue electrical activation at discrete terminal locations, which subsequently spreads throughout the ventricles. In this paper, we present an algorithm that is capable of estimating the location of the Purkinje system triggering points from a set of random measurements on tissue. We present the properties and the performance of the algorithm under controlled synthetic scenarios. Results show that the method is capable of locating most of the triggering points in scenarios with a fair ratio between terminals and measurements. When the ratio is low, the method can locate the terminals with major impact in the overall activation map. Mean absolute errors obtained indicate that solutions provided by the algorithm are useful to accurately simulate a complete patient ventricular activation map. Copyright © 2017 John Wiley & Sons, Ltd.

Supplemental Digital Content is available in the text.

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