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Background: In electrocardiographic imaging (ECGI), selecting an optimal regularization parameter (λ) is crucial for obtaining accurate inverse electrograms. The effects of signal and geometry uncertainties on the inverse problem regularization have not been thoroughly quantified, and there is no established methodology to identify when λ is sub-optimal due to these uncertainties. This study introduces a novel approach to λ selection using Tikhonov regularization and L-curve optimization, specifically addressing the impact of electrical noise in body surface potential map (BSPM) signals and geometrical inaccuracies in the cardiac mesh., Methods: Nineteen atrial simulations (5 of regular rhythms and 14 of atrial fibrillation) ensuring variability in substrate complexity and activation patterns were used for computing the ECGI with added white Gaussian noise from 40 dB to -3dB. Cardiac mesh displacements (1-3 cm) were applied to simulate the uncertainty of atrial positioning and study its impact on the L-curve shape. The regularization parameter, the maximum curvature, and the most horizontal angle of the L-curve (β) were quantified. In addition, BSPM signals from real patients were used to validate our findings., Results: The maximum curvature of the L-curve was found to be inversely related to signal-to-noise ratio and atrial positioning errors. In contrast, the β angle is directly related to electrical noise and remains unaffected by geometrical errors. Our proposed adjustment of λ, based on the β angle, provides a more reliable ECGI solution than traditional corner-based methods. Our findings have been validated with simulations and real patient data, demonstrating practical applicability., Conclusion: Adjusting λ based on the amount of noise in the data (or on the β angle) allows finding optimal ECGI solutions than a λ purely found at the corner of the L-curve. It was observed that the relevant information in ECGI activation maps is preserved even under the presence of uncertainties when the regularization parameter is correctly selected. The proposed criteria for regularization parameter selection have the potential to enhance the accuracy and reliability of ECGI solutions., Competing Interests: Declaration of competing interest M.S. Guillem. A.M. Climent. Are co-founders of Corify Care SL. R. Molero, J. Reventós-Presmanes, A.M. Climent. And M.S. Guillem get honorary from Corify Care SL. M.S. Guillem, L. Mont is shareholders of Corify Care SL. L. Mont reports honoraria as consultant, lecturer, and Advisory Board from Boston Scientific, Abbott Medical, Johnson & Johnson, and Medtronic. He is a shareholder of Galgo Medical SL. I. Roca-Luque has received honoraria as a lecturer and consultant from Abbott and Biosense Webster., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The high incidence of cardiac arrythmias underlines the need for the assessment of pharmacological therapies. In this field of drug efficacy, as in the field of drug safety highlighted by the Comprehensive in Vitro Proarrhythmia Assay initiative, new pillars for research have become crucial: firstly, the integration of in-silico experiments, and secondly the evaluation of fully integrated biological systems, such as human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). In this study, we therefore aimed to combine in-vitro experiments and in-silico simulations to evaluate the antiarrhythmic effect of L-type calcium current (I CaL ) block in hiPSC-CMs. For this, hiPSC-CM preparations were cultured and an equivalent virtual tissue was modeled. Re-entry patterns of electrical activation were induced and several biomarkers were obtained before and after I CaL block. The virtual hiPSC-CM simulations were also reproduced using a tissue composed of adult ventricular cardiomyocytes (hAdultV-CMs). The analysis of phases, currents and safety factor for propagation showed an increased size of the re-entry core when I CaL was blocked as a result of depressed cellular excitability. The bigger wavefront curvature yielded reductions of 12.2%, 6.9%, and 4.2% in the frequency of the re-entry for hiPSC-CM cultures, virtual hiPSC-CM, and hAdultV-CM tissues, respectively. Furthermore, I CaL block led to a 47.8% shortening of the vulnerable window for re-entry in the virtual hiPSC-CM tissue and to re-entry vanishment in hAdultV-CM tissue. The consistent behavior between in-vitro and in-silico hiPSC-CMs and between in-silico hiPSC-CMs and hAdultV-CMs evidences that virtual hiPSC-CM tissues are suitable for assessing cardiac efficacy, as done in the present study through the analysis of I CaL block., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Identification of reentrant activity driving atrial fibrillation (AF) is increasingly important to ablative therapies. The goal of this work is to study how the automatically-classified quality of the electrograms (EGMs) affects reentrant AF driver localization. EGMs from 259 AF episodes obtained from 29 AF patients were recorded using 64-poles basket catheters and were manually classified according to their quality. An algorithm capable of identifying signal quality was developed using time and spectral domain parameters. Electrical reentries were identified in 3D phase maps using phase transform and were compared with those obtained with a 2D activation-based method. Effect of EGM quality was studied by discarding 3D phase reentries detected in regions with low-quality EGMs. Removal of reentries identified by 3D phase analysis in regions with low-quality EGMs improved its performance, increasing the area under the ROC curve (AUC) from 0.69 to 0.80. The EGMs quality classification algorithm showed an accurate performance for EGM classification (AUC 0.94) and reentry detection (AUC 0.80). Automatic classification of EGM quality based on time and spectral signal parameters is feasible and accurate, avoiding the manual labelling. Discard of reentries identified in regions with automatically-detected poor-quality EGMs improved the specificity of the 3D phase-based method for AF driver identification., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Atrial Flutter (AFL) termination by ablating the path responsible for the arrhythmia maintenance is an extended practice. However, the difficulty associated with the identification of the circuit in the case of atypical AFL motivates the development of diagnostic techniques. We propose body surface phase map analysis as a noninvasive tool to identify AFL circuits. Sixty seven lead body surface recordings were acquired in 9 patients during AFL (i.e. 3 typical, 6 atypical). Computed body surface phase maps from simulations of 5 reentrant behaviors in a realistic atrial structure were also used. Surface representation of the macro-reentrant activity was analyzed by tracking the singularity points (SPs) in surface phase maps obtained from band-pass filtered body surface potential maps. Spatial distribution of SPs showed significant differences between typical and atypical AFL. Whereas for typical AFL patients 70.78 ± 16.17% of the maps presented two SPs simultaneously in the areas defined around the midaxialliary lines, this condition was only satisfied in 5.15 ± 10.99% (p < 0.05) maps corresponding to atypical AFL patients. Simulations confirmed these results. Surface phase maps highlights the reentrant mechanism maintaining the arrhythmia and appear as a promising tool for the noninvasive characterization of the circuit maintaining AFL. The potential of the technique as a diagnosis tool needs to be evaluated in larger populations and, if it is confirmed, may help in planning ablation procedures., (Copyright © 2018. Published by Elsevier Ltd.)

In this paper we studied the implementation and performance of adaptive step methods for large systems of ordinary differential equations systems in graphics processing units, focusing on the simulation of three-dimensional electric cardiac activity. The Rush-Larsen method was applied in all the implemented solvers to improve efficiency. We compared the adaptive methods with the fixed step methods, and we found that the fixed step methods can be faster while the adaptive step methods are better in terms of accuracy and robustness., (© 2013 Elsevier Ltd. Published by Elsevier Ltd. All rights reserved.)