Alejandro Liberos

Body surface localization of left and right atrial high-frequency rotors in atrial fibrillation patients: A clinical-computational study

BACKGROUND Ablation is an effective therapy in patients with atrial fibrillation (AF) in which an electrical driver can be identified. OBJECTIVE The aim of this study was to present and discuss a novel and strictly noninvasive approach to map and identify atrial regions responsible for AF perpetuation. METHODS Surface potential recordings of 14 patients with AF were recorded using a 67-lead recording system. Singularity points (SPs) were identified in surface phase maps after band-pass filtering at the highest dominant frequency (HDF). Mathematical models of combined atria and torso were constructed and used to investigate the ability of surface phase maps to estimate rotor activity in the atrial wall. RESULTS The simulations show that surface SPs originate at atrial SPs, but not all atrial SPs are reflected at the surface. Stable SPs were found in AF signals during 8.3% ± 5.7% vs. 73.1% ± 16.8% of the time in unfiltered vs. HDF-filtered patient data, respectively (P < .01). The average duration of each rotational pattern was also lower in unfiltered than in HDF-filtered AF signals (160 ± 43 ms vs. 342 ± 138 ms; P < .01), resulting in 2.8 ± 0.7 rotations per rotor. Band-pass filtering reduced the apparent meandering of surface HDF rotors by reducing the effect of the atrial electrical activity occurring at different frequencies. Torso surface SPs representing HDF rotors during AF were reflected at specific areas corresponding to the fastest atrial location. CONCLUSION Phase analysis of surface potential signals after HDF filtering during AF shows reentrant drivers localized to either the left atrium or the right atrium, helping in localizing ablation targets.

Balance between sodium and calcium currents underlying chronic atrial fibrillation termination: An in silico intersubject variability study

Background Atrial remodeling as a result of long-standing persistent atrial fibrillation (AF) induces substrate modifications that lead to different perpetuation mechanisms than in paroxysmal AF and a reduction in the efficacy of antiarrhythmic treatments. Objective The purpose of this study was to identify the ionic current modifications that could destabilize reentries during chronic AF and serve to personalize antiarrhythmic strategies. Methods A population of 173 mathematical models of remodeled human atrial tissue with realistic intersubject variability was developed based on action potential recordings of 149 patients diagnosed with AF. The relationship of each ionic current with AF maintenance and the dynamics of functional reentries (rotor meandering, dominant frequency) were evaluated by means of 3-dimensional simulations. Results Self-sustained reentries were maintained in 126 (73%) of the simulations. AF perpetuation was associated with higher expressions of INa and ICaL (P <.01), with no significant differences in the remaining currents. ICaL blockade promoted AF extinction in 30% of these 126 models. The mechanism of AF termination was related with collisions between rotors because of an increase in rotor meandering (1.71 ± 2.01cm2) and presented an increased efficacy in models with a depressed INa (P <.01). Conclusion Mathematical simulations based on a population of models representing intersubject variability allow the identification of ionic mechanisms underlying rotor dynamics and the definition of new personalized pharmacologic strategies. Our results suggest that the underlying mechanism of the diverging success of ICaL block as an antiarrhythmic strategy is dependent on the basal availability of sodium and calcium ion channel conductivities.

Noninvasive Estimation of Epicardial Dominant High‐Frequency Regions during Atrial Fibrillation

Ablation of high dominant frequency (DF) sources in patients with atrial fibrillation (AF) is an effective treatment option for paroxysmal AF. The aim of this study was to evaluate the accuracy of noninvasive estimation of DF and electrical patterns determination by solving the inverse problem of the electrocardiography.

Adaptive step ODE algorithms for the 3D simulation of electric heart activity with graphics processing units

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.

Highest dominant frequency and rotor positions are robust markers of driver location during noninvasive mapping of atrial fibrillation: A computational study

BACKGROUND Dominant frequency (DF) and rotor mapping have been proposed as noninvasive techniques to guide localization of drivers maintaining atrial fibrillation (AF). OBJECTIVE The purpose of this study was to evaluate the robustness of both techniques in identifying atrial drivers noninvasively under the effect of electrical noise or model uncertainties. METHODS Inverse-computed DFs and phase maps were obtained from 30 different mathematical AF simulations. Epicardial highest dominant frequency (HDF) regions and rotor location were compared with the same inverse-computed measurements after addition of noise to the ECG, size variations of the atria, and linear or angular deviations in the atrial location inside the thorax. RESULTS Inverse-computed electrograms (EGMs) individually correlated poorly with the original EGMs in the absence of induced uncertainties (0.45 ± 0.12) and were worse with 10-dB noise (0.22 ± 0.11), 3-cm displacement (0.01 ± 0.02), or 36° rotation (0.02 ± 0.03). However, inverse-computed HDF regions showed robustness against induced uncertainties: from 82% ± 18% match for the best conditions, down to 73% ± 23% for 10-dB noise, 77% ± 21% for 5-cm displacement, and 60% ± 22% for 36° rotation. The distance from the inverse-computed rotor to the original rotor was also affected by uncertainties: 0.8 ± 1.61 cm for the best conditions, 2.4 ± 3.6 cm for 10-dB noise, 4.3 ± 3.2 cm for 4-cm displacement, and 4.0 ± 2.1 cm for 36° rotation. Restriction of rotor detections to the HDF area increased rotor detection accuracy from 4.5 ± 4.5 cm to 3.2 ± 3.1 cm (P <.05) with 0-dB noise. CONCLUSION The combination of frequency and phase-derived measurements increases the accuracy of noninvasive localization of atrial rotors driving AF in the presence of noise and uncertainties in atrial location or size.

Solving Inaccuracies in Anatomical Models for Electrocardiographic Inverse Problem Resolution by Maximizing Reconstruction Quality

Electrocardiographic Imaging has become an increasingly used technique for non-invasive diagnosis of cardiac arrhythmias, although the need for medical imaging technology to determine the anatomy hinders its introduction in the clinical practice. This paper explores the ability of a new metric based on the inverse reconstruction quality for the location and orientation of the atrial surface inside the torso. Body surface electrical signals from 31 realistic mathematical models and four AF patients were used to estimate the optimal position of the atria inside the torso. The curvature of the L-curve from the Tikhonov method, which was found to be related to the inverse reconstruction quality, was measured after application of deviations in atrial position and orientation. Independent deviations in the atrial position were solved by finding the maximal L-curve curvature with an error of 1.7 ± 2.4 mm in mathematical models and 9.1 ± 11.5 mm in patients. For the case of independent angular deviations, the error in location by using the L-curve was 5.8±7.1° in mathematical models and 12.4° ± 13.2° in patients. The ability of the L-curve curvature was tested also under superimposed uncertainties in the three axis of translation and in the three axis of rotation, and the error in location was of 2.3 ± 3.2 mm and 6.4° ± 7.1° in mathematical models, and 7.9±10.7 mm and 12.1°±15.5° in patients. The curvature of L-curve is a useful marker for the atrial position and would allow emending the inaccuracies in its location.

Atrial sources identification by causality analysis during atrial fibrillation.

Ablation of electrical drivers during atrial fibrillation (AF) has been proved as an effective therapy to prevent recurrence of fibrillatory episodes. This study presents a new methodology based on causality analysis that is able to identify the hierarchical dominance of atrial areas driving AF. Realistic mathematical models of the atrial electrical activity during AF were used to assess the validity of our method. Identification of the dominant atrial propagation patterns was achieved by computing causal relations between multiple electrogram signals. The causal relationships between atrial areas during the fibrillatory processes were summarized into a recurrence map, highlighting the hierarchy and dominant areas. Recurrence maps computed from causality analysis allowed the identification of sites responsible for maintenance of the arrhythmia. These maps were able to locate the position of the atrial driver in fibrillatory processes with a single rotor, with 2 rotors or with several drivers. Additionally, the correspondence between the nodal values of the recurrence map and the distance to the rotor core has been established. Causal analysis consistently estimated propagation patterns and location of atrial drivers during AF. This methodology could guide ablation procedures in AF patients.

Data Analysis in Cardiac Arrhythmias

Cardiac arrhythmias are an increasingly present in developed countries and represent a major health and economic burden. The occurrence of cardiac arrhythmias is closely linked to the electrical function of the heart. Consequently, the analysis of the electrical signal generated by the heart tissue, either recorded invasively or noninvasively, provides valuable information for the study of cardiac arrhythmias. In this chapter, novel cardiac signal analysis techniques that allow the study and diagnosis of cardiac arrhythmias are described, with emphasis on cardiac mapping which allows for spatiotemporal analysis of cardiac signals.Cardiac mapping can serve as a diagnostic tool by recording cardiac signals either in close contact to the heart tissue or noninvasively from the body surface, and allows the identification of cardiac sites responsible of the development or maintenance of arrhythmias. Cardiac mapping can also be used for research in cardiac arrhythmias in order to understand their mechanisms. For this purpose, both synthetic signals generated by computer simulations and animal experimental models allow for more controlled physiological conditions and complete access to the organ.