Complexity and recurrence of body surface electrocardiograms correlate with estimated reentrant atrial activity using electrocardiographic imaging in atrial fibrillation patients.

Background: Complexity and signal recurrence metrics obtained from body surface potential mapping (BSPM) allow quantifying atrial fibrillation (AF) substrate complexity. This study aims to correlate electrocardiographic imaging (ECGI) detected reentrant patterns with BSPM-calculated signal complexity and recurrence metrics.

Methods: BSPM signals were recorded from 28 AF patients (17 male, 11 women, 62.

Improving electrocardiographic imaging solutions: A comprehensive study on regularization parameter selection in L-curve optimization in the Atria.

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.

Regional conduction velocities determined by noninvasive mapping are associated with arrhythmia-free survival after atrial fibrillation ablation.

Background: Atrial arrhythmogenic substrate is a key determinant of atrial fibrillation (AF) recurrence after pulmonary vein isolation (PVI), and reduced conduction velocities have been linked to adverse outcome. However, a noninvasive method to assess such electrophysiologic substrate is not available to date.

Objective: This study aimed to noninvasively assess regional conduction velocities and their association with arrhythmia-free survival after PVI.

Non-invasive estimation of atrial fibrillation driver position using long-short term memory neural networks and body surface potentials.

Background And Objective: Atrial Fibrillation (AF) is a supraventricular tachyarrhythmia that can lead to thromboembolism, hearlt failure, ischemic stroke, and a decreased quality of life. Characterizing the locations where the mechanisms of AF are initialized and maintained is key to accomplishing an effective ablation of the targets, hence restoring sinus rhythm. Many methods have been investigated to locate such targets in a non-invasive way, such as Electrocardiographic Imaging, which enables an on-invasive and panoramic characterization of cardiac electrical activity using recording Body Surface Potentials (BSP) and a torso model of the patient.

Standardized 2D atrial mapping and its clinical applications.

The visualization and comparison of electrophysiological information in the atrium among different patients could be facilitated by a standardized 2D atrial mapping. However, due to the complexity of the atrial anatomy, unfolding the 3D geometry into a 2D atrial mapping is challenging. In this study, we aim to develop a standardized approach to achieve a 2D atrial mapping that connects the left and right atria, while maintaining fixed positions and sizes of atrial segments across individuals.

AF driver detection in pulmonary vein area by electropcardiographic imaging: Relation with a favorable outcome of pulmonary vein isolation.

Pulmonary vein isolation (PVI) is the most successful treatment for atrial fibrillation (AF) nowadays. However, not all AF patients benefit from PVI. In this study, we evaluate the use of ECGI to identify reentries and relate rotor density in the pulmonary vein (PV) area as an indicator of PVI outcome.

Robustness of imageless electrocardiographic imaging against uncertainty in atrial morphology and location.

Introduction: Electrocardiographic Imaging is a non-invasive technique that requires cardiac Imaging for the reconstruction of cardiac electrical activity. In this study, we explored imageless ECGI by quantifying the errors of using heart meshes with either an inaccurate location inside the thorax or an inaccurate geometry.

Methods: Multiple‑lead body surface recordings of 25 atrial fibrillation (AF) patients were recorded.

Electrocardiographic imaging in the atria.

The inverse problem of electrocardiography or electrocardiographic imaging (ECGI) is a technique for reconstructing electrical information about cardiac surfaces from noninvasive or non-contact recordings. ECGI has been used to characterize atrial and ventricular arrhythmias. Although it is a technology with years of progress, its development to characterize atrial arrhythmias is challenging.

Effects of torso mesh density and electrode distribution on the accuracy of electrocardiographic imaging during atrial fibrillation.

Electrocardiographic Imaging (ECGI) allows computing the electrical activity in the heart non-invasively using geometrical information of the patient and multiple body surface signals. In the present study we investigate the influence of the number of nodes of geometrical meshes and recording ECG electrodes distribution to compute ECGI during atrial fibrillation (AF). Torso meshes from 100 to 2000 nodes heterogeneously and homogeneously distributed were compared.

Atrial fibrosis identification with unipolar electrogram eigenvalue distribution analysis in multi-electrode arrays.

Atrial fibrosis plays a key role in the initiation and progression of atrial fibrillation (AF). Atrial fibrosis is typically identified by a peak-to-peak amplitude of bipolar electrograms (b-EGMs) lower than 0.5 mV, which may be considered as ablation targets.

Identification of atrial fibrillation drivers by means of concentric ring electrodes.

Background And Objective: The prevalence of atrial fibrillation (AF) has tripled in the last 50 years due to population aging. High-frequency (DFdriver) activated atrial regions lead the activation of the rest of the atria, disrupting the propagation wavefront. Fourier based spectral analysis of body surface potential maps have been proposed for DFdriver identification, although these approaches present serious drawbacks due to their limited spectral resolution for short AF epochs and the blurring effect of the volume conductor.

Non-invasive Estimation of Atrial Fibrillation Driver Position With Convolutional Neural Networks and Body Surface Potentials.

Atrial fibrillation (AF) is characterized by complex and irregular propagation patterns, and AF onset locations and drivers responsible for its perpetuation are the main targets for ablation procedures. ECG imaging (ECGI) has been demonstrated as a promising tool to identify AF drivers and guide ablation procedures, being able to reconstruct the electrophysiological activity on the heart surface by using a non-invasive recording of body surface potentials (BSP). However, the inverse problem of ECGI is ill-posed, and it requires accurate mathematical modeling of both atria and torso, mainly from CT or MR images.

Higher reproducibility of phase derived metrics from electrocardiographic imaging during atrial fibrillation in patients remaining in sinus rhythm after pulmonary vein isolation.

Background: Electrocardiographic imaging (ECGI) allows evaluating the complexity of the reentrant activity of atrial fibrillation (AF) patients. In this study, we evaluated the ability of ECGI metrics to predict the success of pulmonary vein isolation (PVI) to treat AF.

Methods: ECGI of 24 AF patients (6 males, 13 paroxysmal, 61.

Electrocardiographic Imaging for Atrial Fibrillation: A Perspective From Computer Models and Animal Experiments to Clinical Value.

Electrocardiographic imaging (ECGI) is a technique to reconstruct non-invasively the electrical activity on the heart surface from body-surface potential recordings and geometric information of the torso and the heart. ECGI has shown scientific and clinical value when used to characterize and treat both atrial and ventricular arrhythmias. Regarding atrial fibrillation (AF), the characterization of the electrical propagation and the underlying substrate favoring AF is inherently more challenging than for ventricular arrhythmias, due to the progressive and heterogeneous nature of the disease and its manifestation, the small volume and wall thickness of the atria, and the relatively large role of microstructural abnormalities in AF.

Optimized single-point left ventricular pacing leads to improved resynchronization compared with multipoint pacing.

Background: Multipoint pacing (MPP) in cardiac resynchronization therapy (CRT) activates the left ventricle from two locations, thereby shortening the QRS duration and enabling better resynchronization; however, compared with conventional CRT, MPP reduces battery longevity. On the other hand, electrocardiogram-based optimization using the fusion-optimized intervals (FOI) method achieves more significant reverse remodeling than nominal CRT programming. Our study aimed to determine whether MPP could attain better resynchronization than single-point pacing (SPP) optimized by FOI.

Characterization of atrial arrhythmias in body surface potential mapping: A computational study.

Purpose: Atrial tachycardia (AT), flutter (AFL) and fibrillation (AF) are very common cardiac arrhythmias and are driven by localized sources that can be ablation targets. Non-invasive body surface potential mapping (BSPM) can be useful for early diagnosis and ablation planning. We aimed to characterize and differentiate the arrhythmic mechanisms behind AT, AFL and AF from the BSPM perspective using basic features reflecting their electrophysiology.

Ranolazine-Mediated Attenuation of Mechanoelectric Feedback in Atrial Myocyte Monolayers.

Background: Mechanical stretch increases Na inflow into myocytes, related to mechanisms including stretch-activated channels or Na/H exchanger activation, involving Ca increase that leads to changes in electrophysiological properties favoring arrhythmia induction. Ranolazine is an antianginal drug with confirmed beneficial effects against cardiac arrhythmias associated with the augmentation of current and Ca overload.

Objective: This study investigates the effects of mechanical stretch on activation patterns in atrial cell monolayers and its pharmacological response to ranolazine.

Standardizing Single-Frame Phase Singularity Identification Algorithms and Parameters in Phase Mapping During Human Atrial Fibrillation.

Purpose: Recent investigations failed to reproduce the positive rotor-guided ablation outcomes shown by initial studies for treating persistent atrial fibrillation (persAF). Phase singularity (PS) is an important feature for AF driver detection, but algorithms for automated PS identification differ. We aim to investigate the performance of four different techniques for automated PS detection.

A robust wavelet-based approach for dominant frequency analysis of atrial fibrillation in body surface signals.

Objective: Atrial dominant frequency (DF) maps undergoing atrial fibrillation (AF) presented good spatial correlation with those obtained with the non-invasive body surface potential mapping (BSPM). In this study, a robust BSPM-DF calculation method based on wavelet analysis is proposed.

Approach: Continuous wavelet transform along 40 scales in the pseudo-frequency range of 3-30 Hz is performed in each BSPM signal using a Gaussian mother wavelet.

Noninvasive Assessment of Complexity of Atrial Fibrillation: Correlation With Contact Mapping and Impact of Ablation.

Background: It is difficult to noninvasively phenotype atrial fibrillation (AF) in a way that reflects clinical end points such as response to therapy. We set out to map electrical patterns of disorganization and regions of reentrant activity in AF from the body surface using electrocardiographic imaging, calibrated to panoramic intracardiac recordings and referenced to AF termination by ablation.

Methods: Bi-atrial intracardiac electrograms of 47 patients with AF at ablation (30 persistent, 29 male, 63±9 years) were recorded with 64-pole basket catheters and simultaneous 57-lead body surface ECGs.

Automatic quality electrogram assessment improves phase-based reentrant activity identification in atrial fibrillation.

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.

Optical imaging of voltage and calcium in isolated hearts: Linking spatiotemporal heterogeneities and ventricular fibrillation initiation.

Background: Alternans have been associated with the development of ventricular fibrillation and its control has been proposed as antiarrhythmic strategy. However, cardiac arrhythmias are a spatiotemporal phenomenon in which multiple factors are involved (e.g.

Clinical Characteristics and Electrophysiological Mechanisms Underlying Brugada ECG in Patients With Severe Hyperkalemia.

Background Several metabolic conditions can cause the Brugada ECG pattern, also called Brugada phenotype (BrPh). We aimed to define the clinical characteristics and outcome of BrPh patients and elucidate the mechanisms underlying BrPh attributed to hyperkalemia. Methods and Results We prospectively identified patients hospitalized with severe hyperkalemia and ECG diagnosis of BrPh and compared their clinical characteristics and outcome with patients with hyperkalemia but no BrPh ECG.

Phase singularity point tracking for the identification of typical and atypical flutter patients: A clinical-computational study.

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.