The purpose of this manuscript is to develop a reaction-diffusion heart model for closed-loop evaluation of heart-pacemaker interaction, and to provide a hardware setup for the implementation of the closed-loop system. The heart model, implemented on a workstation, is based on the cardiac monodomain formulation and a phenomenological model of cardiac cells, which we fitted to the electrophysiological properties of the different cardiac tissues. We modelled the pacemaker as a timed automaton, deployed on an Arduino 2 board. The Arduino and the workstation communicate through a PCI acquisition board. Additionally, we developed a graphical user interface for easy handling of the framework. The myocyte model resembles the electrophysiological properties of atrial and ventricular tissue. The heart model reproduces healthy activation sequence and proved to be computationally efficient (i.e., 1 s simulation requires about 5 s). Furthermore, we successfully simulated the interaction between heart and pacemaker models in three well-known pathological contexts. Our results showed that the PDE formulation is appropriate for the simulation in closed-loop. While computationally more expensive, a PDE model is more flexible and allows to represent more complex scenarios than timed or hybrid automata. Furthermore, users can interact more easily with the framework thanks to the graphical representation of the spatiotemporal evolution of the membrane potentials. By representing the heart as a reaction-diffusion model, the proposed closed-loop system provides a novel and promising framework for the assessment of cardiac pacemakers.

In this paper, we present CardioMat, a Matlab toolbox for cardiac electrophysiology simulation based on patient-specific anatomies. The strength of CardioMat is the easy and fast construction of electrophysiology cardiac digital twins from segmented anatomical images in a general-purpose software such as Matlab. CardioMat implements a quasi-automatic pipeline that guides the user toward the construction of anatomically detailed cardiac electrophysiology models. Importantly, the CardioMat framework includes the generation of physiologically plausible fiber orientation and Purkinje networks. The main novelty of our framework is its ability to handle voxel-based geometries as produced by segmentation procedures directly, without the need for an unstructured mesh. Indeed, the CardioMat monodomain solver uses a smoothed boundary approach and runs completely on GPU for fast simulations. We employed CardioMat in different application scenarios to show its potentialities and provide preliminary assessment of the feasibility, diagnostic performance, and accuracy of the toolbox. In particular, we showed that CardioMat simulations derived from post-infarction patients hold high sensitivity, specificity, predictive value, and accuracy for localization of deceleration zones in sinus rhythm.

In this work, we present a computational study of the Brugada Syndrome (BrS) phenotype aimed at investigating the main factors contributing to the development of arrhythmias. We developed a model that incorporated a BrS substrate within a region resembling the right ventricular outflow tract (RVOT) in a three-dimensional anisotropic ventricular cardiac tissue with transmural heterogeneity. Consistent with our previous two-dimensional study, our results confirmed the requirement of both electrophysiological alterations and structural abnormalities to trigger arrhythmic events. In particular, we found that the combination of electrophysiological alterations and structural abnormalities caused percolation in the tissue, eventually leading to sustained reentry. Moreover, our model is able to replicate the majority of epicardial electrogram features observed in the arrhythmic substrate of Brugada patients, furthermore, the behavior of our model agrees with clinical findings on BrS patients. We identified the density and size of structural abnormalities, the degree of myocyte electrophysiological alteration, and the size of the arrhythmic substrate as risk factors for the genesis of arrhythmias. These findings could be used in a model-based approach to develop processing techniques that highlight arrhythmogenic features in BrS patients’ recorded electrograms, improving risk stratification in patients.

Brugada Syndrome (BrS) is a primary electrical epicardial disease characterized by ST-segment elevation followed by a negative T-wave in the right precordial leads on the surface electrocardiogram (ECG), also known as the ‘type 1’ ECG pattern. The risk stratification of asymptomatic individuals with spontaneous type 1 ECG pattern remains challenging. Clinical and electrocardiographic prognostic markers are known. As none of these predictors alone is highly reliable in terms of arrhythmic prognosis, several multi-factor risk scores have been proposed for this purpose. This article presents a new workflow for processing endocardial signals acquired with high-density RV electro-anatomical mapping (HDEAM) from BrS patients. The workflow, which relies solely on Matlab software, calculates various electrical parameters and creates multi-parametric maps of the right ventricle. The workflow, but it has already been employed in several research studies involving patients carried out by our group, showing its potential positive impact in clinical studies. Here, we will provide a technical description of its functionalities, along with the results obtained on a BrS patient who underwent an endocardial HDEAM.

Simulations of cardiac electrophysiology (CEP) are gaining momentum beyond basic mechanistic studies, as an approach for supporting clinical decision making. The potential for in silico technologies observed from the research community is immense, with studies demonstrating significantly improved therapeutical outcome with little to no additional burden for patients. Studies replicating virtually induction protocols in post myocardial infarction patients are among the most reproduced and promise to identify non invasively ablation targets for therapeutical intervention. Two main factors hinder the translation of these technologies from pure research to applications: virtually no reproducibility of results, and lack of standardized procedures. Inspired by a previously published virtual induction study by Arevalo et al. (2016), We address the issues of reproducibility and efficiency providing auto-VARP, a framework for automated virtual arrhythmia inducibility studies, built upon openCARP and the carputils framework. Standardization relies on the previously published forCEPSS framework and is ensured by defining the whole induction study with input files that can be easily shared to ensure reproducibility since the whole pipeline relies on open software. Our approach also ensures numerical efficiency by separating the induction study into four stages: (i) pre-pacing with forCEPSS, (ii) S1 pacing for each steady state, (iii) S2 induction with different extrastimuli, (iv) testing of sustenance of induced reentries. We demonstrate the approach in a large virtual subject cohort to investigate numerical artifacts that may arise when improper setups are provided to perform virtual induction, and additionally showcase auto-VARP in a biventricular mesh. auto-VARP addresses effectively the current gap in automation and reproducibility of results providing a uniform methodology that can be implemented even by non expert users. auto-VARP is highly scalable and adaptable to markedly different anatomies. Although less flexible than in house implementations it provides automated tools to share setups and does not require re-implementation of any process.