Spatial Resolution Requirements for Accurate Identification of Drivers of Atrial Fibrillation

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

Conduction Velocity and Anisotropic Properties of Fibrillation Waves During Acutely Induced and Long-Standing Persistent AF

BACKGROUND

Quantified features of local conduction heterogeneity due to pathological alterations of myocardial tissue could serve as a marker for the degree of electrical remodeling and hence be used to determine the stage of atrial fibrillation (AF).

OBJECTIVES

In this study, the authors investigated whether local directional heterogeneity (LDH) and anisotropy ratio, derived from estimated local conduction velocities (CVs) during AF, are suitable electrical parameters to stage AF.

METHODS

Epicardial mapping (244-electrode array, interelectrode distance 2.25 mm) of the right atrium was performed during acute atrial fibrillation (AAF) (n = 25, 32 ± 11 years of age) and during long-standing persistent atrial fibrillation (LSPAF) (n = 23, 64 ± 9 years of age). Episodes of 9 ± 4 seconds of AF were analyzed. Local CV vectors were constructed to assess the degree of anisotropy. Directions and magnitudes of individual vectors were compared with surrounding vectors to identify LDH.

RESULTS

Compared with the entire AAF group, LSPAF was characterized by slower conduction (71.5 ± 6.8 cm/s vs 67.6 ± 5.6 cm/s; P = 0.037) with a larger dispersion (1.59 ± 0.21 vs 1.95 ± 0.17; P < 0.001) and temporal variability (32.0 ± 4.7 cm/s vs 38.5 ± 3.3 cm/s; P < 0.001). Also, LSPAF was characterized by more LDH (19.6% ± 4.4% vs 26.0% ± 3.4%; P < 0.001) and a higher degree of anisotropy (1.38 ± 0.07 vs 1.51 ± 0.14; P < 0.001). Compared with the most complex type of AAF (type III), LSPAF was still associated with a larger CV dispersion, higher temporal variability of CV, and larger amount of LDH.

CONCLUSIONS

Increasing AF complexity was associated with increased spatiotemporal variability of local CV vectors, local conduction heterogeneity, and anisotropy ratio. By using these novel parameters, LSPAF could potentially be discriminated from the most complex type of AAF. These observations may indicate pathological alterations of myocardial tissue underlying progression of AF.

Pulsed Field Ablation for Atrial Fibrillation: Mechanisms, Advantages, and Limitations

Pulsed field ablation with irreversible electroporation for the treatment of atrial fibrillation involves tissue-specific and non-thermal energy-induced cell necrosis, which helps avoid complications, such as pulmonary vein stenosis, atrial collateral tissue damage, and extensive atrial structural damage, often encountered with traditional thermal ablation. In existing clinical trials, pulsed field ablation has shown excellent effects on pulmonary vein isolation in patients with paroxysmal and persistent atrial fibrillation. Pulsed field ablation is easy, simple, and quick and can reduce iatrogenic injury. Therefore, the application of pulsed field ablation technology in the treatment of atrial fibrillation has a promising future. Notably, the adjustment of parameters in pulsed field ablation with different ablation catheter systems can strongly affect the area and depth of the necrotic myocardium, which greatly affects the likelihood of atrial fibrillation recurrence and incidence of adverse complications after ablation. In this paper, we review the mechanisms, advantages, and limitations of pulsed field ablation based on the results of a series of previous studies and provide ideas and directions for future research.

A computational modeling framework for pre-clinical evaluation of cardiac mapping systems

There are a variety of difficulties in evaluating clinical cardiac mapping systems, most notably the inability to record the transmembrane potential throughout the entire heart during patient procedures which prevents the comparison to a relevant "gold standard". Cardiac mapping systems are comprised of hardware and software elements including sophisticated mathematical algorithms, both of which continue to undergo rapid innovation. The purpose of this study is to develop a computational modeling framework to evaluate the performance of cardiac mapping systems. The framework enables rigorous evaluation of a mapping system's ability to localize and characterize (i.e., focal or reentrant) arrhythmogenic sources in the heart. The main component of our tool is a library of computer simulations of various dynamic patterns throughout the entire heart in which the type and location of the arrhythmogenic sources are known. Our framework allows for performance evaluation for various electrode configurations, heart geometries, arrhythmias, and electrogram noise levels and involves blind comparison of mapping systems against a "silver standard" comprised of computer simulations in which the precise transmembrane potential patterns throughout the heart are known. A feasibility study was performed using simulations of patterns in the human left atria and three hypothetical virtual catheter electrode arrays. Activation times (AcT) and patterns (AcP) were computed for three virtual electrode arrays: two basket arrays with good and poor contact and one high-resolution grid with uniform spacing. The average root mean squared difference of AcTs of electrograms and those of the nearest endocardial action potential was less than 1 ms and therefore appears to be a poor performance metric. In an effort to standardize performance evaluation of mapping systems a novel performance metric is introduced based on the number of AcPs identified correctly and those considered spurious as well as misclassifications of arrhythmia type; spatial and temporal localization accuracy of correctly identified patterns was also quantified. This approach provides a rigorous quantitative analysis of cardiac mapping system performance. Proof of concept of this computational evaluation framework suggests that it could help safeguard that mapping systems perform as expected as well as provide estimates of system accuracy.

High Dominant Frequencies and Fractionated Potentials Do Not Indicate Focal or Rotational Activation During AF

BACKGROUND

Dominant frequencies (DFs) or complex fractionated atrial electrograms (CFAEs), indicative of focal sources or rotational activation, are used to identify target sites for atrial fibrillation (AF) ablation in clinical studies, although the relationship among DF, CFAE, and activation patterns remains unclear.

OBJECTIVES

This study sought to investigate the relationship between patterns of activation underlying DF and CFAE sites during AF.

METHODS

Epicardial high-resolution mapping of the right and left atrium including Bachmann's bundle was performed in 71 participants. We identified the highest dominant frequency (DF_max) and highest degree of CFAE (CFAE_max) with the use of existing clinical criteria and classified patterns of activation as focal or rotational activation and smooth propagation, conduction block (CB), collision and remnant activity, and fibrillation potentials as single, double, or fractionated potentials containing, respectively, 1, 2, or 3 or more negative deflections. Relationships among activation patterns, DF_max, and potential types were investigated.

RESULTS

DF_max were primarily located at the left atrioventricular groove and did not harbor focal activation (proportion focal waves: 0% [IQR: 0%-2%]). Compared with non-DF_max sites, DF_max were characterized by more frequent smooth propagation (22% [IQR: 7%-48%] vs 17% [IQR: 11%-24%]; P = 0.001), less frequent conduction block (69% [IQR: 51%-81%] vs 74% [IQR: 69%-78%]; P = 0.006), a higher proportion of single potentials (72% [IQR: 55%-84%] vs 6%1 [IQR: 55%-65%]; P = 0.003), and a lower proportion of fractionated potentials (4% [IQR: 1%-11%] vs 12% [IQR: 9%-15%]; P = 0.004). CFAE_max were mainly found at the pulmonary veins area, and only 1% [IQR: 0%-2%] of all CFAE_max contained focal activation. Compared with non-CFAE_max sites, CFAE_max sites were characterized by less frequent smooth propagation (1% [IQR: 0%-1%] vs 17% [IQR: 12%-24%]; P < 0.001) and more frequent remnant activity (20% [IQR: 12%-29%] vs 8% [IQR: 5%-10%]; P < 0.001), and harbored predominantly fractionated potentials (52% [IQR: 43%-66%] vs 12% [IQR: 9%-14%]; P < 0.001).

CONCLUSIONS

Focal or rotational patterns of activation were not consistently detected at DF_max domains and CFAE_max sites. These findings do not support the concept of targeting DF_max or CFAE_max according to existing criteria for AF ablation.

Graphene Biointerface for Cardiac Arrhythmia Diagnosis and Treatment

Heart rhythm disorders, known as arrhythmias, cause significant morbidity and are one of the leading causes of mortality. Cardiac arrhythmias are frequently treated by implantable devices, such as pacemakers and defibrillators, or by ablation therapy guided by electroanatomical mapping. Both implantable and ablation therapies require sophisticated biointerfaces for electrophysiological measurements of electrograms and delivery of therapeutic stimulation or ablation energy. In this work, a graphene biointerface for in vivo cardiac electrophysiology is reported for the first time. Leveraging sub-micrometer-thick tissue-conformable graphene arrays, sensing and stimulation of the open mammalian heart are demonstrated both in vitro and in vivo. Furthermore, the graphene biointerface treatment of atrioventricular block (the kind of arrhythmia where the electrical conduction from the atria to the ventricles is interrupted) is demonstrated. The graphene arrays show effective electrochemical properties, namely interface impedance down to 40 Ω cm² at 1 kHz, charge storage capacity up to 63.7 mC cm^-2 , and charge injection capacity up to 704 µC cm^-2 . Transparency of the graphene structures allows for simultaneous optical mapping of cardiac action potentials, calcium transients, and optogenetic stimulation while performing electrical measurements and stimulation. The report presents evidence of the significant potential of graphene biointerfaces for advanced cardiac electrophysiology and arrhythmia therapy.

Markov modeling of phase singularity interaction effects in human atrial and ventricular fibrillation

Atrial and ventricular fibrillation (AF/VF) are characterized by the repetitive regeneration of topological defects known as phase singularities (PSs). The effect of PS interactions has not been previously studied in human AF and VF. We hypothesized that PS population size would influence the rate of PS formation and destruction in human AF and VF, due to increased inter-defect interaction. PS population statistics were studied in computational simulations (Aliev-Panfilov), human AF and human VF. The influence of inter-PS interactions was evaluated by comparison between directly modeled discrete-time Markov chain (DTMC) transition matrices of the PS population changes, and M/M/∞ birth-death transition matrices of PS dynamics, which assumes that PS formations and destructions are effectively statistically independent events. Across all systems examined, PS population changes differed from those expected with M/M/∞. In human AF and VF, the formation rates decreased slightly with PS population when modeled with the DTMC, compared with the static formation rate expected through M/M/∞, suggesting new formations were being inhibited. In human AF and VF, the destruction rates increased with PS population for both models, with the DTMC rate increase exceeding the M/M/∞ estimates, indicating that PS were being destroyed faster as the PS population grew. In human AF and VF, the change in PS formation and destruction rates as the population increased differed between the two models. This indicates that the presence of additional PS influenced the likelihood of new PS formation and destruction, consistent with the notion of self-inhibitory inter-PS interactions.

High-density and high coverage composite mapping of repetitive atrial activation patterns

BACKGROUND

Repetitive atrial activation patterns (RAAPs) during atrial fibrillation (AF) may be associated with localized mechanisms that maintain AF. Current electro-anatomical mapping systems are unsuitable for analyzing RAAPs due to the trade-off between spatial coverage and electrode density in clinical catheters. This work proposes a technique to overcome this trade-off by constructing composite maps from spatially overlapping sequential recordings.

METHODS

High-density epicardial contact mapping was performed during open-chest surgery in goats (n=16, left and right atria) with 3 or 22 weeks of sustained AF (249-electrode array, electrode distance 2.4 mm). A dataset mimicking sequential recordings was generated by segmenting the grid into four spatially overlapping regions (each region 6.5 cm², 48±10% overlap) without temporal overlap. RAAPs were detected in each region using recurrence plots of activation times. RAAPs in two different regions were joined in case of RAAP cross-recurrence between overlapping electrodes. We quantified the reconstruction success rate and quality of the composite maps.

RESULTS

Of 1021 RAAPs found in the full mapping array (32±13 per recording), 328 spatiotemporally stable RAAPs were analyzed. 247 composite maps were generated (75% success) with a quality of 0.86±0.21 (Pearson correlation). Success was significantly affected by the RAAP area. Quality was weakly correlated with the number of repetitions of RAAPs (r=0.13, p<0.05) and not affected by the atrial side (left or right) or AF duration (3 or 22 weeks of AF).

CONCLUSIONS

Constructing composite maps by combining spatially overlapping sequential recordings is feasible. Interpretation of these maps can play a central role in ablation planning.

Rotor mechanism and its mapping in atrial fibrillation

Treatment of atrial fibrillation (AF) remains challenging despite significant progress in understanding its underlying mechanisms. The first detailed, quantitative theory of functional re-entry, the 'leading circle' model, was developed more than 40 years ago. Subsequently, in decades of study, an alternative paradigm based on spiral waves has long been postulated to drive AF. The rotor as a 'spiral wave generator' is a curved 'vortex' formed by spin motion in the two-dimensional plane, identified using advanced mapping methods in experimental and clinical AF. However, it is challenging to achieve complementary results between experimental results and clinical studies due to the limitation in research methods and the complexity of the rotor mechanism. Here, we review knowledge garnered over decades on generation, electrophysiological properties, and three-dimensional (3D) structure diversity of the rotor mechanism and make a comparison among recent clinical approaches to identify rotors. Although initial studies of rotor ablation at many independent centres have achieved promising results, some inconclusive outcomes exist in others. We propose that the clinical rotor identification might be substantially influenced by (i) non-identical surface activation patterns, which resulted from a diverse 3D form of scroll wave, and (ii) inadequate resolution of mapping techniques. With rapidly advancing theoretical and technological developments, future work is required to resolve clinically relevant limitations in current basic and clinical research methodology, translate from one to the other, and resolve available mapping techniques.

Atrial fibrillation: Insights from animal models, computational modeling, and clinical studies

Atrial fibrillation (AF) is the most common human arrhythmia, affecting millions of patients worldwide. A combination of risk factors and comorbidities results in complex atrial remodeling, which increases AF vulnerability and persistence. Insights from animal models, clinical studies, and computational modeling have advanced the understanding of the mechanisms and pathophysiology of AF. Areas of heterogeneous pathological remodeling, as well as altered electrophysiological properties, serve as a substrate for AF drivers and spontaneous activations. The complex and individualized presentation of this arrhythmia suggests that mechanisms-based personalized approaches will likely be needed to overcome current challenges in AF management. In this paper, we review the insights on the mechanisms of AF obtained from animal models and clinical studies and how computational models integrate this knowledge to advance AF clinical management. We also assess the challenges that need to be overcome to implement these mechanistic models in clinical practice.

An Evaluation of Phase Analysis to Interpret Atrial Activation Patterns during Persistent Atrial Fibrillation for Targeted Ablation

BACKGROUND

Phase analysis has been used to identify and localize atrial fibrillation (AF) sources for targeted ablation. We previously demonstrated that repetitive wannabe reentry (incomplete reentry) often generated an apparent stable rotor using phase analysis. The misinterpretation caused by phase analysis using atrial electrograms (AEGs) may result from detecting inaccurate time points at phase inversion (π to -π) in the instantaneous phase waveform converted from AEG. The purpose of this study was to evaluate the accuracy of phase analysis to detect atrial activations recorded from the high-density mapping of AF in patients with persistent and long-standing persistent (LSP) AF.

METHODS AND RESULTS

During open heart surgery, we recorded activation from both atria simultaneously using 512 electrodes in 7 patients with persistent and LSP AF. The phase analysis was compared to manual measurements during 4 s of data. For the accuracy of activation sequence maps, a successful recording site was defined as having ≤4 mismatched activation times during the 4 s. In all AF episodes, the accuracy of the phase analysis was only 82% of the total number of activation times due to either activation time differences (14.7%), under-sensing (2.7%), or over-sensing (0.6%). Only 67.9% of the total recording sites met the requirement of a successful recording site by phase analysis. In unsuccessful recording sites, AEG characteristics were relatively irregular cycle length (CL), complex AEG, and double potential AEG.

CONCLUSION

The phase analysis was less accurate in recording sites with a relatively irregular CL, complex AEG, or double potential AEG. As a result, phase analysis may lead to the misinterpretation of atrial activation patterns during AF. A visual review of the original AEG is needed to confirm the detected AF sources of phase analysis before performing targeted ablation.

The physics of heart rhythm disorders

The global burden caused by cardiovascular disease is substantial, with heart disease representing the most common cause of death around the world. There remains a need to develop better mechanistic models of cardiac function in order to combat this health concern. Heart rhythm disorders, or arrhythmias, are one particular type of disease which has been amenable to quantitative investigation. Here we review the application of quantitative methodologies to explore dynamical questions pertaining to arrhythmias. We begin by describing single-cell models of cardiac myocytes, from which two and three dimensional models can be constructed. Special focus is placed on results relating to pattern formation across these spatially-distributed systems, especially the formation of spiral waves of activation. Next, we discuss mechanisms which can lead to the initiation of arrhythmias, focusing on the dynamical state of spatially discordant alternans, and outline proposed mechanisms perpetuating arrhythmias such as fibrillation. We then review experimental and clinical results related to the spatio-temporal mapping of heart rhythm disorders. Finally, we describe treatment options for heart rhythm disorders and demonstrate how statistical physics tools can provide insights into the dynamics of heart rhythm disorders.

Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate

Characterizing patient-specific atrial conduction properties is important for understanding arrhythmia drivers, for predicting potential arrhythmia pathways, and for personalising treatment approaches. One metric that characterizes the health of the myocardial substrate is atrial conduction velocity, which describes the speed and direction of propagation of the electrical wavefront through the myocardium. Atrial conduction velocity mapping algorithms are under continuous development in research laboratories and in industry. In this review article, we give a broad overview of different categories of currently published methods for calculating CV, and give insight into their different advantages and disadvantages overall. We classify techniques into local, global, and inverse methods, and discuss these techniques with respect to their faithfulness to the biophysics, incorporation of uncertainty quantification, and their ability to take account of the atrial manifold.

Impact of Adenosine on Wavefront Propagation in Persistent Atrial Fibrillation: Insights From Global Noncontact Charge Density Mapping of the Left Atrium

Background

Adenosine shortens action potential duration and refractoriness and provokes atrial fibrillation. This study aimed to evaluate the effect of adenosine on mechanisms of wavefront propagation during atrial fibrillation.

Methods and Results

The study included 22 patients undergoing catheter ablation for persistent atrial fibrillation. Left atrial mapping was performed using the AcQMap charge density system before and after administration of intravenous adenosine at 1 or more of 3 time points during the procedure (before pulmonary vein isolation, after pulmonary vein isolation, and after nonpulmonary vein isolation ablation). Wave‐front propagation patterns were evaluated allowing identification and quantification of localized rotational activation (LRA), localized irregular activation, and focal firing. Additional signal processing was performed to identify phase singularities and calculate global atrial fibrillation cycle length and dominant frequency. A total of 35 paired maps were analyzed. Adenosine shortened mean atrial fibrillation cycle length from 181.7±14.3 to 165.1±16.3, (mean difference 16.6 ms; 95% CI, 11.3–21.9, P<0.0005) and increased dominant frequency from 6.0±0.7 Hz to 6.6±0.8 Hz (95% CI, 0.4–0.9, P<0.0005). This was associated with a 50% increase in the number of LRA occurrences (16.1±7.6–24.2±8.1; mean difference 8.1, 95% CI, 4.1–12, P<0.0005) as well as a 20% increase in the number of phase singularities detected (30.1±7.8–36.6±9.3; mean difference 6.5; 95% CI, 2.6–10.0, P=0.002). The percentage of left atrial surface area with LRA increased with adenosine and 42 of 70 zones (60%) with highest density of LRA coincided with high density LRA zones at baseline with only 28% stable across multiple maps.

Conclusions

Adenosine accelerates atrial fibrillation and promotes rotational activation patterns with no impact on focal activation. There is little evidence that rotational activation seen with adenosine represents promising targets for ablation aimed at sites of stable arrhythmogenic sources in the left atrium.

Intracardiac Inverse Potential Mapping Using the Method of Fundamental Solutions

Introduction: Atrial fibrillation (AF) is the most prevalent cardiac dysrhythmia and percutaneous catheter ablation is widely used to treat it. Panoramic mapping with multi-electrode catheters can identify ablation targets in persistent AF, but is limited by poor contact and inadequate coverage. Objective: To investigate the accuracy of inverse mapping of endocardial surface potentials from electrograms sampled with noncontact basket catheters. Methods: Our group has developed a computationally efficient inverse 3D mapping technique using a meshless method that employs the Method of Fundamental Solutions (MFS). An in-silico test bed was used to compare ground-truth surface potentials with corresponding inverse maps reconstructed from noncontact potentials sampled with virtual catheters. Ground-truth surface potentials were derived from high-density clinical contact mapping data and computer models. Results: Solutions of the intracardiac potential inverse problem with the MFS are robust, fast and accurate. Endocardial surface potentials can be faithfully reconstructed from noncontact recordings in real-time if the geometry of cardiac surface and the location of electrodes relative to it are known. Larger catheters with appropriate electrode density are needed to resolve complex reentrant atrial rhythms. Conclusion: Real-time panoramic potential mapping is feasible with noncontact intracardiac catheters using the MFS. Significance: Accurate endocardial potential maps can be reconstructed in AF with appropriately designed noncontact multi-electrode catheters.

Global Substrate Mapping and Targeted Ablation with Novel Gold-tip Catheter in De Novo Persistent AF

Results from catheter ablation for persistent AF are suboptimal, with no strategy other than pulmonary vein isolation showing clear benefit. Recently employed empirical strategies beyond pulmonary vein isolation involve widespread atrial ablation in all patients and do not take into account patient-specific differences in AF mechanisms or phenotype. Charge density mapping using the non-contact AcQMap system (Acutus Medical) allows visualisation of whole-chamber activation during AF and reveals localised patterns of complex activation thought to represent important mechanisms for AF maintenance that can be targeted with focal ablation. In this review, the authors outline the fundamentals of this technology, the initial data exploring the mechanistic role of activation patterns seen and the application to ablation of persistent AF.

Similarity Score for the Identification of Active Sites in Patients With Atrial Fibrillation

Background

Atrial fibrillation (AF) is the most common cardiac arrhythmia and precursor to other cardiac diseases. Catheter ablation is associated with limited success rates in patients with persistent AF. Currently, existing mapping systems fail to identify critical target sites for ablation. Recently, we proposed and validated several individual techniques, such as dominant frequency (DF), multiscale frequency (MSF), kurtosis (Kt), and multiscale entropy (MSE), to identify active sites of arrhythmias using simulated intracardiac electrograms (iEGMs). However, the individual performances of these techniques to identify arrhythmogenic substrates are not reliable.

Objective

This study aimed to develop a similarity score using various iEGM analysis techniques to more accurately identify the spatial location of active sites of arrhythmia in patients with AF.

Methods

Clinical bipolar iEGMs were obtained from patients with AF who underwent either successful (m = 4) or unsuccessful (m = 4) catheter ablation. A similarity score (0–3) was developed via the earth mover’s distance (EMD) approach based on a combination of DF, MSF, MSE, and Kt techniques.

Results

Individual techniques successfully discriminated between successful and unsuccessful AF ablation patients but were not reliable in identifying active spatial sites of AF. However, the proposed similarity score was able to pinpoint the spatial sites with high values (active AF sites) that were observed only in patients with unsuccessful AF termination, suggesting that these active sites were missed during the ablation procedure.

Conclusion

Arrhythmogenic substrates with abnormal electrical activity are identified in patients with unsuccessful AF termination after catheter ablation, suggesting clinical efficacy of similarity score.

Rotor Localization and Phase Mapping of Cardiac Excitation Waves Using Deep Neural Networks

The analysis of electrical impulse phenomena in cardiac muscle tissue is important for the diagnosis of heart rhythm disorders and other cardiac pathophysiology. Cardiac mapping techniques acquire local temporal measurements and combine them to visualize the spread of electrophysiological wave phenomena across the heart surface. However, low spatial resolution, sparse measurement locations, noise and other artifacts make it challenging to accurately visualize spatio-temporal activity. For instance, electro-anatomical catheter mapping is severely limited by the sparsity of the measurements, and optical mapping is prone to noise and motion artifacts. In the past, several approaches have been proposed to create more reliable maps from noisy or sparse mapping data. Here, we demonstrate that deep learning can be used to compute phase maps and detect phase singularities in optical mapping videos of ventricular fibrillation, as well as in very noisy, low-resolution and extremely sparse simulated data of reentrant wave chaos mimicking catheter mapping data. The self-supervised deep learning approach is fundamentally different from classical phase mapping techniques. Rather than encoding a phase signal from time-series data, a deep neural network instead learns to directly associate phase maps and the positions of phase singularities with short spatio-temporal sequences of electrical data. We tested several neural network architectures, based on a convolutional neural network (CNN) with an encoding and decoding structure, to predict phase maps or rotor core positions either directly or indirectly via the prediction of phase maps and a subsequent classical calculation of phase singularities. Predictions can be performed across different data, with models being trained on one species and then successfully applied to another, or being trained solely on simulated data and then applied to experimental data. Neural networks provide a promising alternative to conventional phase mapping and rotor core localization methods. Future uses may include the analysis of optical mapping studies in basic cardiovascular research, as well as the mapping of atrial fibrillation in the clinical setting.

Computational Analysis of Mapping Catheter Geometry and Contact Quality Effects on Rotor Detection in Atrial Fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia and catheter mapping has been proved to be an effective approach for detecting AF drivers to be targeted by ablation. Among drivers, the so-called rotors have gained the most attention: their identification and spatial location could help to understand which patient-specific mechanisms are acting, and thus to guide the ablation execution. Since rotor detection by multi-electrode catheters may be influenced by several structural parameters including inter-electrode spacing, catheter coverage, and endocardium-catheter distance, in this study we proposed a tool for testing the ability of different catheter shapes to detect rotors in different conditions. An approach based on the solution of the monodomain equations coupled with a modified Courtemanche ionic atrial model, that considers an electrical remodeling, was applied to simulate spiral wave dynamics on a 2D model for 7.75 s. The developed framework allowed the acquisition of unipolar signals at 2 KHz. Two high-density multipolar catheters were simulated (Advisor™ HD Grid and PentaRay^®) and placed in a 2D region in which the simulated spiral wave persists longer. The configuration of the catheters was then modified by changing the number of electrodes, inter-electrodes distance, position, and atrial-wall distance for assessing how they would affect the rotor detection. In contact with the wall and at 1 mm distance from it, all the configurations detected the rotor correctly, irrespective of geometry, coverage, and inter-electrode distance. In the HDGrid-like geometry, the increase of the inter-electrode distance from 3 to 6 mm caused rotor detection failure at 2 mm distance from the LA wall. In the PentaRay-like configuration, regardless of inter-electrode distance, rotor detection failed at 3 mm endocardium-catheter distance. The asymmetry of this catheter resulted in rotation-dependent rotor detection. To conclude, the computational framework we developed is based on realistic catheter shapes designed with parameter configurations which resemble clinical settings. Results showed it is well suited to investigate how mapping catheter geometry and location affect AF driver detection, therefore it is a reliable tool to design and test new mapping catheters.

Classification of Fibrillation Organisation Using Electrocardiograms to Guide Mechanism-Directed Treatments

Background: Atrial fibrillation (AF) and ventricular fibrillation (VF) are complex heart rhythm disorders and may be sustained by distinct electrophysiological mechanisms. Disorganised self-perpetuating multiple-wavelets and organised rotational drivers (RDs) localising to specific areas are both possible mechanisms by which fibrillation is sustained. Determining the underlying mechanisms of fibrillation may be helpful in tailoring treatment strategies. We investigated whether global fibrillation organisation, a surrogate for fibrillation mechanism, can be determined from electrocardiograms (ECGs) using band-power (BP) feature analysis and machine learning. Methods: In this study, we proposed a novel ECG classification framework to differentiate fibrillation organisation levels. BP features were derived from surface ECGs and fed to a linear discriminant analysis classifier to predict fibrillation organisation level. Two datasets, single-channel ECGs of rat VF (n = 9) and 12-lead ECGs of human AF (n = 17), were used for model evaluation in a leave-one-out (LOO) manner. Results: The proposed method correctly predicted the organisation level from rat VF ECG with the sensitivity of 75%, specificity of 80%, and accuracy of 78%, and from clinical AF ECG with the sensitivity of 80%, specificity of 92%, and accuracy of 88%. Conclusion: Our proposed method can distinguish between AF/VF of different global organisation levels non-invasively from the ECG alone. This may aid in patient selection and guiding mechanism-directed tailored treatment strategies.

Fibrotic Remodeling during Persistent Atrial Fibrillation: In Silico Investigation of the Role of Calcium for Human Atrial Myofibroblast Electrophysiology

During atrial fibrillation, cardiac tissue undergoes different remodeling processes at different scales from the molecular level to the tissue level. One central player that contributes to both electrical and structural remodeling is the myofibroblast. Based on recent experimental evidence on myofibroblasts' ability to contract, we extended a biophysical myofibroblast model with Ca²⁺ handling components and studied the effect on cellular and tissue electrophysiology. Using genetic algorithms, we fitted the myofibroblast model parameters to the existing in vitro data. In silico experiments showed that Ca²⁺ currents can explain the experimentally observed variability regarding the myofibroblast resting membrane potential. The presence of an L-type Ca²⁺ current can trigger automaticity in the myofibroblast with a cycle length of 799.9 ms. Myocyte action potentials were prolonged when coupled to myofibroblasts with Ca²⁺ handling machinery. Different spatial myofibroblast distribution patterns increased the vulnerable window to induce arrhythmia from 12 ms in non-fibrotic tissue to 22 ± 2.5 ms and altered the reentry dynamics. Our findings suggest that Ca²⁺ handling can considerably affect myofibroblast electrophysiology and alter the electrical propagation in atrial tissue composed of myocytes coupled with myofibroblasts. These findings can inform experimental validation experiments to further elucidate the role of myofibroblast Ca²⁺ handling in atrial arrhythmogenesis.

Time-Averaged Wavefront Analysis Demonstrates Preferential Pathways of Atrial Fibrillation, Predicting Pulmonary Vein Isolation Acute Response

Electrical activation during atrial fibrillation (AF) appears chaotic and disorganised, which impedes characterisation of the underlying substrate and treatment planning. While globally chaotic, there may be local preferential activation pathways that represent potential ablation targets. This study aimed to identify preferential activation pathways during AF and predict the acute ablation response when these are targeted by pulmonary vein isolation (PVI). In patients with persistent AF (n = 14), simultaneous biatrial contact mapping with basket catheters was performed pre-ablation and following each ablation strategy (PVI, roof, and mitral lines). Unipolar wavefront activation directions were averaged over 10 s to identify preferential activation pathways. Clinical cases were classified as responders or non-responders to PVI during the procedure. Clinical data were augmented with a virtual cohort of 100 models. In AF pre-ablation, pathways originated from the pulmonary vein (PV) antra in PVI responders (7/7) but not in PVI non-responders (6/6). We proposed a novel index that measured activation waves from the PV antra into the atrial body. This index was significantly higher in PVI responders than non-responders (clinical: 16.3 vs. 3.7%, p = 0.04; simulated: 21.1 vs. 14.1%, p = 0.02). Overall, this novel technique and proof of concept study demonstrated that preferential activation pathways exist during AF. Targeting patient-specific activation pathways that flowed from the PV antra to the left atrial body using PVI resulted in AF termination during the procedure. These PV activation flow pathways may correspond to the presence of drivers in the PV regions.

Topological charge-density method of identifying phase singularities in cardiac fibrillation

Spiral waves represent the key motifs of typical self-sustained dynamical patterns in excitable systems such as cardiac tissue. The motion of phase singularities (PSs) that lies at the center of spiral waves captures many qualitative and, in some cases, quantitative features of their complex dynamics. Recent clinical studies suggested that ablating the tissue at PS locations may cure atrial fibrillation. Here, we propose a different method to determine the location of PSs. Starting from the definition of the topological charge of spiral waves, we obtain the expression of the topological charge density in a discrete case. With this expression, we can calculate the topological charge at each grid in the space directly, so as to accurately identify the position of PSs.

Extra-pulmonary vein driver mapping and ablation for persistent atrial fibrillation in obese patients

AIMS

The aim of this study was to determine whether driver ablation effectively treats persistent atrial fibrillation (AF) in obese patients.

METHODS AND RESULTS

We randomly assigned 124 persistent AF obese patients to two groups, one undergoing conventional ablation (n = 62) and the other undergoing driver ablation (n = 62). Sixty-two non-obese patients with persistent AF undergoing driver ablation served as matched controls. Bipolar electrogram dispersion was analysed for driver mapping. Epicardial adipose tissue (EAT) volume was measured using cardiac computed tomography. Obese patients had a higher proportion of driver regions in the posterior wall (56.5% vs. 32.3%, P = 0.007). Driver complexity, measured as the average number and area of driver regions, was higher in the obese group than in the non-obese group (3.5 ± 1.0 vs. 2.9 ± 0.9, P < 0.001; 15.5% ± 4.2% vs. 9.8 ± 2.6%, P < 0.001, respectively). Left atrial EAT volume correlated better with the proportion of area of driver regions than did body mass index (BMI) and total EAT (BMI: r2 = 0.250, P < 0.001; total EAT: r2 = 0.379, P < 0.001; and left atrial EAT: r2 = 0.439, P < 0.001). The rate of AF termination was significantly higher in the driver ablation group than in the conventional ablation group (82.9% vs. 22.8%, P < 0.001). During the follow-up period of 16.9 ± 6.5 months, patients in the driver ablation group had significantly better AF-free survival (91.91% vs. 79.0%, log rank test, P = 0.026) and AF/atrial tachycardia-free survival (83.9% vs. 64.5%, log rank test, P = 0.011) than did patients in the conventional ablation group.

CONCLUSION

Obesity is associated with increased driver complexity. Driver ablation improves long-term outcomes in obese patients with persistent AF.

Paradigm shifts in electrophysiological mechanisms of atrial fibrillation

Determining the sequence of activation is a major source of information for understanding the electrophysiological mechanism(s) of atrial fibrillation (AF). However, the complex morphology of the electrograms hampers their analysis, and has stimulated generations of electrophysiologists to develop a large variety of technologies for recording, pre-processing, and analysis of fibrillation electrograms. This variability of approaches is mirrored by a large variability in the interpretation of fibrillation electrograms and, thereby, opinions regarding the basic electrophysiological mechanism(s) of AF vary widely. Multiple wavelets, different types of re-entry including rotors, double layers, multiple focal activation patterns all have been advocated, and a comprehensive and commonly accepted paradigm for the fundamental mechanisms of AF is still lacking. Here, we summarize the Maastricht perspective and Cleveland perspective regarding AF mechanism(s). We also describe some of the key observations in mapping of AF reported over the past decades, and how they changed over the years, often as results of new techniques introduced in the experimental field of AF research.

Deep Learning Classification of Unipolar Electrograms in Human Atrial Fibrillation: Application in Focal Source Mapping

Focal sources are potential targets for atrial fibrillation (AF) catheter ablation, but they can be time-consuming and challenging to identify when unipolar electrograms (EGM) are numerous and complex. Our aim was to apply deep learning (DL) to raw unipolar EGMs in order to automate putative focal sources detection. We included 78 patients from the Focal Source and Trigger (FaST) randomized controlled trial that evaluated the efficacy of adjunctive FaST ablation compared to pulmonary vein isolation alone in reducing AF recurrence. FaST sites were identified based on manual classification of sustained periodic unipolar QS EGMs over 5-s. All periodic unipolar EGMs were divided into training (n = 10,004) and testing cohorts (n = 3,180). DL was developed using residual convolutional neural network to discriminate between FaST and non-FaST. A gradient-based method was applied to interpret the DL model. DL classified FaST with a receiver operator characteristic area under curve of 0.904 ± 0.010 (cross-validation) and 0.923 ± 0.003 (testing). At a prespecified sensitivity of 90%, the specificity and accuracy were 81.9 and 82.5%, respectively, in detecting FaST. DL had similar performance (sensitivity 78%, specificity 89%) to that of FaST re-classification by cardiologists (sensitivity 78%, specificity 79%). The gradient-based interpretation demonstrated accurate tracking of unipolar QS complexes by select DL convolutional layers. In conclusion, our novel DL model trained on raw unipolar EGMs allowed automated and accurate classification of FaST sites. Performance was similar to FaST re-classification by cardiologists. Future application of DL to classify FaST may improve the efficiency of real-time focal source detection for targeted AF ablation therapy.

Novel approaches to mechanism-based atrial fibrillation ablation

Modern cardiac electrophysiology has reported significant advances in the understanding of mechanisms underlying complex wave propagation patterns during atrial fibrillation (AF), although disagreements remain. One school of thought adheres to the long-held postulate that AF is the result of randomly propagating wavelets that wonder throughout the atria. Another school supports the notion that AF is deterministic in that it depends on a small number of high-frequency rotors generating three-dimensional scroll waves that propagate throughout the atria. The spiralling waves are thought to interact with anatomic and functional obstacles, leading to fragmentation and new wavelet formation associated with the irregular activation patterns documented on AF tracings. The deterministic hypothesis is consistent with demonstrable hierarchical gradients of activation frequency and AF termination on ablation at specific (non-random) atrial regions. During the last decade, data from realistic animal models and pilot clinical series have triggered a new era of novel methodologies to identify and ablate AF drivers outside the pulmonary veins. New generation electroanatomical mapping systems and multielectrode mapping catheters, complimented by powerful mathematical analyses, have generated the necessary platforms and tools for moving these approaches into clinical procedures. Recent clinical data using such platforms have provided encouraging evidence supporting the feasibility of targeting and effectively ablating driver regions in addition to pulmonary vein isolation in persistent AF. Here, we review state-of-the-art technologies and provide a comprehensive historical perspective, characterization, classification, and expected outcomes of current mechanism-based methods for AF ablation. We discuss also the challenges and expected future directions that scientists and clinicians will face in their efforts to understand AF dynamics and successfully implement any novel method into regular clinical practice.

Mapping Technologies for Catheter Ablation of Atrial Fibrillation Beyond Pulmonary Vein Isolation

Catheter ablation remains the most effective and relatively minimally invasive therapy for rhythm control in patients with AF. Ablation has consistently shown a reduction of arrhythmia-related symptoms and significant improvement in patients’ quality of life compared with medical treatment. The ablation strategy relies on a well-established anatomical approach of effective pulmonary vein isolation. Additional anatomical targets have been reported with the aim of increasing procedure success in complex substrates. However, larger ablated areas with uncertainty of targeting relevant regions for AF initiation or maintenance are not exempt from the potential risk of complications and pro-arrhythmia. Recent developments in mapping tools and computational methods for advanced signal processing during AF have reported novel strategies to identify atrial regions associated with AF maintenance. These novel tools – although mainly limited to research series – represent a significant step forward towards the understanding of complex patterns of propagation during AF and the potential achievement of patient-tailored AF ablation strategies for the near future.

Three dimensional reconstruction to visualize atrial fibrillation activation patterns on curved atrial geometry

BACKGROUND

The rotational activation created by spiral waves may be a mechanism for atrial fibrillation (AF), yet it is unclear how activation patterns obtained from endocardial baskets are influenced by the 3D geometric curvature of the atrium or 'unfolding' into 2D maps. We develop algorithms that can visualize spiral waves and their tip locations on curved atrial geometries. We use these algorithms to quantify differences in AF maps and spiral tip locations between 3D basket reconstructions, projection onto 3D anatomical shells and unfolded 2D surfaces.

METHODS

We tested our algorithms in N = 20 patients in whom AF was recorded from 64-pole baskets (Abbott, CA). Phase maps were generated by non-proprietary software to identify the tips of spiral waves, indicated by phase singularities. The number and density of spiral tips were compared in patient-specific 3D shells constructed from the basket, as well as 3D maps from clinical electroanatomic mapping systems and 2D maps.

RESULTS

Patients (59.4±12.7 yrs, 60% M) showed 1.7±0.8 phase singularities/patient, in whom ablation terminated AF in 11/20 patients (55%). There was no difference in the location of phase singularities, between 3D curved surfaces and 2D unfolded surfaces, with a median correlation coefficient between phase singularity density maps of 0.985 (0.978-0.990). No significant impact was noted by phase singularities location in more curved regions or relative to the basket location (p>0.1).

CONCLUSIONS

AF maps and phase singularities mapped by endocardial baskets are qualitatively and quantitatively similar whether calculated by 3D phase maps on patient-specific curved atrial geometries or in 2D. Phase maps on patient-specific geometries may be easier to interpret relative to critical structures for ablation planning.

Using machine learning to identify local cellular properties that support re-entrant activation in patient-specific models of atrial fibrillation

AIMS

Atrial fibrillation (AF) is sustained by re-entrant activation patterns. Ablation strategies have been proposed that target regions of tissue that may support re-entrant activation patterns. We aimed to characterize the tissue properties associated with regions that tether re-entrant activation patterns in a validated virtual patient cohort.

METHODS AND RESULTS

Atrial fibrillation patient-specific models (seven paroxysmal and three persistent) were generated and validated against local activation time (LAT) measurements during an S1-S2 pacing protocol from the coronary sinus and high right atrium, respectively. Atrial models were stimulated with burst pacing from three locations in the proximity of each pulmonary vein to initiate re-entrant activation patterns. Five atria exhibited sustained activation patterns for at least 80 s. Models with short maximum action potential durations (APDs) were associated with sustained activation. Phase singularities were mapped across the atria sustained activation patterns. Regions with a low maximum conduction velocity (CV) were associated with tethering of phase singularities. A support vector machine (SVM) was trained on maximum local conduction velocity and action potential duration to identify regions that tether phase singularities. The SVM identified regions of tissue that could support tethering with 91% accuracy. This accuracy increased to 95% when the SVM was also trained on surface area.

CONCLUSION

In a virtual patient cohort, local tissue properties, that can be measured (CV) or estimated (APD; using effective refractory period as a surrogate) clinically, identified regions of tissue that tether phase singularities. Combing CV and APD with atrial surface area further improved the accuracy in identifying regions that tether phase singularities.

New Insights Into Understanding Rotor Versus Focal Activation in Patients With Persistent Atrial Fibrillation

OBJECTIVES

This study was to test the hypotheses that: 1) when using phase analysis, repetitive Wannabe re-entry produces a phase singularity point (i.e., a rotor); and 2) the location of the stable rotor is close to the focal source.

BACKGROUND

Recent contact mapping studies in patients with persistent atrial fibrillation (AF) demonstrated that phase analysis produced a different mechanistic result than classical activation sequence analysis. Our studies in patients with persistent AF showed that focal sources sometimes produced repetitive Wannabe re-entry, that is, incomplete re-entry.

METHODS

During open heart surgery, we recorded activation from both atria simultaneously using 510 to 512 electrodes in 12 patients with persistent AF. We performed activation sequence mapping and phase analyses on 4 s of mapped data. For each detected stable rotor (>2 full rotations [720°] recurring at the same site), the corresponding activation patterns were examined from the activation sequence maps.

RESULTS

During AF, phase singularity points (rotors) were identified in both atria in all patients. However, stable phase singularity points were only present in 6 of 12 patients. The range of stable phase singularity points per patient was 0 to 6 (total 14). Stable phase singularity points were produced due to repetitive Wannabe re-entry generated from a focal source or by passive activation. A conduction block sometimes created a stable phase singularity point (n = 2). The average distance between a focal source and a stable rotor was 0.9 ± 0.3 cm.

CONCLUSIONS

Repetitive Wannabe re-entry generated stable rotors adjacent to a focal source. No true re-entry occurred.

Novel mapping techniques for rotor core detection using simulated intracardiac electrograms

BACKGROUND

Catheter ablation is associated with limited success rates in patients with persistent atrial fibrillation (AF). Currently, existing mapping systems fail to identify critical target sites for ablation. Recently, we proposed and validated several techniques (multiscale frequency [MSF], Shannon entropy [SE], kurtosis [Kt], and multiscale entropy [MSE]) to identify pivot point of rotors using ex-vivo optical mapping animal experiments. However, the performance of these techniques is unclear for the clinically recorded intracardiac electrograms (EGMs), due to the different nature of the signals.

OBJECTIVE

This study aims to evaluate the performance of MSF, MSE, SE, and Kt techniques to identify the pivot point of the rotor using unipolar and bipolar EGMs obtained from numerical simulations.

METHODS

Stationary and meandering rotors were simulated in a 2D human atria. The performances of new approaches were quantified by comparing the "true" core of the rotor with the core identified by the techniques. Also, the performances of all techniques were evaluated in the presence of noise, scar, and for the case of the multielectrode multispline and grid catheters.

RESULTS

Our results demonstrate that all the approaches are able to accurately identify the pivot point of both stationary and meandering rotors from both unipolar and bipolar EGMs. The presence of noise and scar tissue did not significantly affect the performance of the techniques. Finally, the core of the rotors was correctly identified for the case of multielectrode multispline and grid catheter simulations.

CONCLUSION

The core of rotors can be successfully identified from EGMs using novel techniques; thus, providing motivation for future clinical implementations.

M/M/Infinity Birth-Death Processes - A Quantitative Representational Framework to Summarize and Explain Phase Singularity and Wavelet Dynamics in Atrial Fibrillation

Rationale

A quantitative framework to summarize and explain the quasi-stationary population dynamics of unstable phase singularities (PS) and wavelets in human atrial fibrillation (AF) is at present lacking. Building on recent evidence showing that the formation and destruction of PS and wavelets in AF can be represented as renewal processes, we sought to establish such a quantitative framework, which could also potentially provide insight into the mechanisms of spontaneous AF termination.

Objectives

Here, we hypothesized that the observed number of PS or wavelets in AF could be governed by a common set of renewal rate constants λ _f (for PS or wavelet formation) and λ _d (PS or wavelet destruction), with steady-state population dynamics modeled as an M/M/∞ birth-death process. We further hypothesized that changes to the M/M/∞ birth-death matrix would explain spontaneous AF termination.

Methods and Results

AF was studied in in a multimodality, multispecies study in humans, animal experimental models (rats and sheep) and Ramirez-Nattel-Courtemanche model computer simulations. We demonstrated: (i) that λ _f and λ _d can be combined in a Markov M/M/∞ process to accurately model the observed average number and population distribution of PS and wavelets in all systems at different scales of mapping; and (ii) that slowing of the rate constants λ _f and λ _d is associated with slower mixing rates of the M/M/∞ birth-death matrix, providing an explanation for spontaneous AF termination.

Conclusion

M/M/∞ birth-death processes provide an accurate quantitative representational architecture to characterize PS and wavelet population dynamics in AF, by providing governing equations to understand the regeneration of PS and wavelets during sustained AF, as well as providing insight into the mechanism of spontaneous AF termination.

Focal and pseudo/rotational activations in human atrial fibrillation defined with automated periodicity mapping

INTRODUCTION

Defining atrial fibrillation (AF) wave propagation is challenging unless local signal features are discrete or periodic. Periodic focal or rotational activity may identify AF drivers. Our objective was to characterize AF propagation at sites with periodic activation to evaluate the prevalence and relationship between focal and rotational activation.

METHODS

We included 80 patients (61 ± 10 years, persistent AF 49%) from the FaST randomized trial that compared the efficacy of adjunctive focal site ablation versus pulmonary vein isolation. Patients underwent left atrial (LA) activation mapping with a 20-pole circular catheter during spontaneous or induced AF. Five-second bipolar and unipolar electrograms in AF were analyzed. Periodic sites were identified by spectral analysis of the bipolar electrogram. Activation maps of periodic sites were constructed using an automated, validated tracking algorithm, and classified into three patterns: focal sites (FS), rotation (RO), or pseudo-rotation (pRO).

RESULTS

The most common propagation pattern at periodic sites was FS for 5-s in all patients (4.9 ± 1.9 per patient). RO and pRO were observed in two and seven patients, respectively, but were all transient (3-5 cycles). Activation from a FS evolved into transient RO/pRO in five patients. No patient had autonomous RO/pRO activations. Patients with RO/pRO had greater LA surface area with periodicity (78 ± 7 vs. 63 ± 16%, p = .0002) and shorter LA periodicity CL (166 ± 10 vs. 190±28 ms, p = .0001) than the rest.

CONCLUSION

Using automated, regional AF periodicity mapping, FS is more prevalent and temporally stable than RO/pRO. Most RO/pRO evolve from neighboring FS. These findings and their implications for AF maintenance require verification with global, panoramic mapping.

3D Electrophysiological Modeling of Interstitial Fibrosis Networks and Their Role in Ventricular Arrhythmias in Non-Ischemic Cardiomyopathy

OBJECTIVE

Interstitial fibrosis is a pathological expansion of the heart's inter-cellular collagen matrix. It is a potential complication of nonischemic cardiomyopathy (NICM), a class of diseases involving electrical and or mechanical dysfunction of cardiac tissue not caused by atherosclerosis. Patients with NICM and interstitial fibrosis often suffer from life threatening arrhythmias, which we aim to simulate in this study.

METHODS

Our methodology builds on an efficient discrete finite element (DFE) method which allows for the representation of fibrosis as infinitesimal splits in a mesh. We update the DFE method with a local connectivity analysis which creates a consistent topology in the fibrosis network. This is particularly important in nonischemic disease due to the potential presence of large and contiguous fibrotic regions and therefore potentially complex fibrosis networks.

RESULTS

In experiments with an image-based model, we demonstrate that our methodology is able to simulate reentrant electrical events associated with cardiac arrhythmias. These reentries depended crucially upon sufficient fibrosis density, which was marked by conduction slowing at high pacing rates. We also created a 2D test-case which demonstrated that fibrosis topologies can modulate transient conduction block, and thereby reentrant activations.

CONCLUSION

Ventricular arrhythmias due to interstitial fibrosis in NICM can be efficiently simulated using our methods in medical image based geometries. Furthermore, fibrosis topology modulates transient conduction block, and should be accounted for in electrophysiological simulations with interstitial fibrosis.

SIGNIFICANCE

Our study provides methodology which has the potential to predict arrhythmias and to optimize treatments non-invasively for nonischemic cardiomyopathies.

A Novel Tool for the Identification and Characterization of Repetitive Patterns in High-Density Contact Mapping of Atrial Fibrillation

Introduction

Electrical contact mapping provides a detailed view of conduction patterns in the atria during atrial fibrillation (AF). Identification of repetitive wave front propagation mechanisms potentially initiating or sustaining AF might provide more insights into temporal and spatial distribution of candidate AF mechanism and identify targets for catheter ablation. We developed a novel tool based on recurrence plots to automatically identify and characterize repetitive conduction patterns in high-density contact mapping of AF.

Materials and Methods

Recurrence plots were constructed by first transforming atrial electrograms recorded by a multi-electrode array to activation-phase signals and then quantifying the degree of similarity between snapshots of the activation-phase in the electrode array. An AF cycle length dependent distance threshold was applied to discriminate between repetitive and non-repetitive snapshots. Intervals containing repetitive conduction patterns were detected in a recurrence plot as regions with a high recurrence rate. Intervals that contained similar repetitive patterns were then grouped into clusters. To demonstrate the ability to detect and quantify the incidence, duration and size of repetitive patterns, the tool was applied to left and right atrial recordings in a goat model of different duration of persistent AF [3 weeks AF (3 wkAF, n = 8) and 22 weeks AF (22 wkAF, n = 8)], using a 249-electrode mapping array (2.4 mm inter-electrode distance).

Results

Recurrence plots identified frequent recurrences of activation patterns in all recordings and indicated a strong correlation between recurrence plot threshold and AF cycle length. Prolonged AF duration was associated with shorter repetitive pattern duration [mean maximum duration 3 wkAF: 74 cycles, 95% confidence interval (54-94) vs. 22 wkAF: 41 cycles (21-62), p = 0.03], and smaller recurrent regions within repetitive patterns [3 wkAF 1.7 cm² (1.0-2.3) vs. 22 wkAF 0.5 cm² (0.0-1.2), p = 0.02]. Both breakthrough patterns and re-entry were identified as repetitive conduction patterns.

Conclusion

Recurrence plots provide a novel way to delineate high-density contact mapping of AF. Dominant repetitive conduction patterns were identified in a goat model of sustained AF. Application of the developed methodology using the new generation of multi-electrode catheters could identify additional targets for catheter ablation of AF.

Unmasking Arrhythmogenic Hubs of Reentry Driving Persistent Atrial Fibrillation for Patient-Specific Treatment

Background

Atrial fibrillation (AF) driver mechanisms are obscured to clinical multielectrode mapping approaches that provide partial, surface‐only visualization of unstable 3‐dimensional atrial conduction. We hypothesized that transient modulation of refractoriness by pharmacologic challenge during multielectrode mapping improves visualization of hidden paths of reentrant AF drivers for targeted ablation.

Methods and Results

Pharmacologic challenge with adenosine was tested in ex vivo human hearts with a history of AF and cardiac diseases by multielectrode and high‐resolution subsurface near‐infrared optical mapping, integrated with 3‐dimensional structural imaging and heart‐specific computational simulations. Adenosine challenge was also studied on acutely terminated AF drivers in 10 patients with persistent AF. Ex vivo, adenosine stabilized reentrant driver paths within arrhythmogenic fibrotic hubs and improved visualization of reentrant paths, previously seen as focal or unstable breakthrough activation pattern, for targeted AF ablation. Computational simulations suggested that shortening of atrial refractoriness by adenosine may (1) improve driver stability by annihilating spatially unstable functional blocks and tightening reentrant circuits around fibrotic substrates, thus unmasking the common reentrant path; and (2) destabilize already stable reentrant drivers along fibrotic substrates by accelerating competing fibrillatory wavelets or secondary drivers. In patients with persistent AF, adenosine challenge unmasked hidden common reentry paths (9/15 AF drivers, 41±26% to 68±25% visualization), but worsened visualization of previously visible reentry paths (6/15, 74±14% to 34±12%). AF driver ablation led to acute termination of AF.

Conclusions

Our ex vivo to in vivo human translational study suggests that transiently altering atrial refractoriness can stabilize reentrant paths and unmask arrhythmogenic hubs to guide targeted AF driver ablation treatment.

Optical Mapping-Validated Machine Learning Improves Atrial Fibrillation Driver Detection by Multi-Electrode Mapping

BACKGROUND

Atrial fibrillation (AF) can be maintained by localized intramural reentrant drivers. However, AF driver detection by clinical surface-only multielectrode mapping (MEM) has relied on subjective interpretation of activation maps. We hypothesized that application of machine learning to electrogram frequency spectra may accurately automate driver detection by MEM and add some objectivity to the interpretation of MEM findings.

METHODS

Temporally and spatially stable single AF drivers were mapped simultaneously in explanted human atria (n=11) by subsurface near-infrared optical mapping (NIOM; 0.3 mm² resolution) and 64-electrode MEM (higher density or lower density with 3 and 9 mm² resolution, respectively). Unipolar MEM and NIOM recordings were processed by Fourier transform analysis into 28 407 total Fourier spectra. Thirty-five features for machine learning were extracted from each Fourier spectrum.

RESULTS

Targeted driver ablation and NIOM activation maps efficiently defined the center and periphery of AF driver preferential tracks and provided validated annotations for driver versus nondriver electrodes in MEM arrays. Compared with analysis of single electrogram frequency features, averaging the features from each of the 8 neighboring electrodes, significantly improved classification of AF driver electrograms. The classification metrics increased when less strict annotation, including driver periphery electrodes, were added to driver center annotation. Notably, f1-score for the binary classification of higher-density catheter data set was significantly higher than that of lower-density catheter (0.81±0.02 versus 0.66±0.04, P<0.05). The trained algorithm correctly highlighted 86% of driver regions with higher density but only 80% with lower-density MEM arrays (81% for lower-density+higher-density arrays together).

CONCLUSIONS

The machine learning model pretrained on Fourier spectrum features allows efficient classification of electrograms recordings as AF driver or nondriver compared with the NIOM gold-standard. Future application of NIOM-validated machine learning approach may improve the accuracy of AF driver detection for targeted ablation treatment in patients.

Toward Mechanism-Directed Electrophenotype-Based Treatments for Atrial Fibrillation

Current treatment approaches for persistent atrial fibrillation (AF) have a ceiling of success of around 50%. This is despite 15 years of developing adjunctive ablation strategies in addition to pulmonary vein isolation to target the underlying arrhythmogenic substrate in AF. A major shortcoming of our current approach to AF treatment is its predominantly empirical nature. This has in part been due to a lack of consensus on the mechanisms that sustain human AF. In this article, we review evidence suggesting that the previous debates on AF being either an organized arrhythmia with a focal driver or a disorganized rhythm sustained by multiple wavelets, may prove to be a false dichotomy. Instead, a range of fibrillation electrophenotypes exists along a continuous spectrum, and the predominant mechanism in an individual case is determined by the nature and extent of remodeling of the underlying substrate. We propose moving beyond the current empirical approach to AF treatment, highlight the need to prescribe AF treatments based on the underlying AF electrophenotype, and review several possible novel mapping algorithms that may be useful in discerning the AF electrophenotype to guide tailored treatments, including Granger Causality mapping.

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.

METHODS

2048-channel virtual electrogram (VEGM) and electrocardiogram signals were collected for 30 s from 10 patients undergoing persAF ablation. QRST-subtraction was performed and VEGMs were processed using sinusoidal wavelet reconstruction. The phase was obtained using Hilbert transform. PSs were detected using four algorithms: (1) 2D image processing based and neighbor-indexing algorithm; (2) 3D neighbor-indexing algorithm; (3) 2D kernel convolutional algorithm estimating topological charge; (4) topological charge estimation on 3D mesh. PS annotations were compared using the structural similarity index (SSIM) and Pearson's correlation coefficient (CORR). Optimized parameters to improve detection accuracy were found for all four algorithms using F β score and 10-fold cross-validation compared with manual annotation. Local clustering with density-based spatial clustering of applications with noise (DBSCAN) was proposed to improve algorithms 3 and 4.

RESULTS

The PS density maps created by each algorithm with default parameters were poorly correlated. Phase gradient threshold and search radius (or kernels) were shown to affect PS detections. The processing times for the algorithms were significantly different (p < 0.0001). The F β scores for algorithms 1, 2, 3, 3 + DBSCAN, 4 and 4 + DBSCAN were 0.547, 0.645, 0.742, 0.828, 0.656, and 0.831. Algorithm 4 + DBSCAN achieved the best classification performance with acceptable processing time (2.0 ± 0.3 s).

CONCLUSION

AF driver identification is dependent on the PS detection algorithms and their parameters, which could explain some of the inconsistencies in rotor-guided ablation outcomes in different studies. For 3D triangulated meshes, algorithm 4 + DBSCAN with optimal parameters was the best solution for real-time, automated PS detection due to accuracy and speed. Similarly, algorithm 3 + DBSCAN with optimal parameters is preferred for uniform 2D meshes. Such algorithms - and parameters - should be preferred in future clinical studies for identifying AF drivers and minimizing methodological heterogeneities. This would facilitate comparisons in rotor-guided ablation outcomes in future works.

Gaussian Process-Based Spatiotemporal Modeling of Electrical Wave Propagation in Human Atrium. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020;2020:2602-2605. [PMID: 33018539 DOI: 10.1109/embc44109.2020.9176468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Rhythm regularity of the heart depends on how electrical impulses spread through the cardiac conduction system. Any abnormal activities in the electrical impulses can lead to serious cardiac disorders or sudden death. It is important to understand the electrical activities of the human heart in both healthy and diseased conditions to determine the cause of cardiac disorders and explore the best therapeutic designs. Mathematical models calibrated with clinical and/or in-vitro data are popularly used to study cardiac function and investigate treatment effects. Most of the current human heart models are highly integrated and couple over a hundred equations across different organizational scales of ion channel, cell, and muscle. The model complex poses a significant computational challenge on cardiac simulation. This study developed a metamodel to replace the time-consuming simulation model. Specifically, Gaussian Process (GP) is used to reconstruct the spatiotemporal variations of the cell membrane potential in left atrium. Four different covariance functions were used to infer the potential distributions. The GP model provides an accurate estimation of the spatiotemporal propagation of electrical waves with a small set of data and shows great advantage in computations as compared to traditional models.

Atrial fibrillation source area probability mapping using electrogram patterns of multipole catheters

BACKGROUND

Catheter ablation therapy involving isolation of pulmonary veins (PVs) from the left atrium is performed to terminate atrial fibrillation (AF). Unfortunately, standalone PV isolation procedure has shown to be a suboptimal success with AF continuation or recurrence. One reason, especially in patients with persistent or high-burden paroxysmal AF, is known to be due to the formation of repeating-pattern AF sources with a meandering core inside the atria. However, there is a need for accurate mapping and localization of these sources during catheter ablation.

METHODS

A novel AF source area probability (ASAP) mapping algorithm was developed and evaluated in 2D and 3D atrial simulated tissues with various arrhythmia scenarios and a retrospective study with three cases of clinical human AF. The ASAP mapping analyzes the electrograms collected from a multipole diagnostic catheter that is commonly used during catheter ablation procedure to intelligently sample the atria and delineate the trajectory path of a meandering repeating-pattern AF source. ASAP starts by placing the diagnostic catheter at an arbitrary location in the atria. It analyzes the recorded bipolar electrograms to build an ASAP map over the atrium anatomy and suggests an optimal location for the subsequent catheter location. ASAP then determines from the constructed ASAP map if an AF source has been delineated. If so, the catheter navigation is stopped and the algorithm provides the area of the AF source. Otherwise, the catheter is navigated to the suggested location, and the process is continued until an AF-source area is delineated.

RESULTS

ASAP delineated the AF source in over 95% of the simulated human AF cases within less than eight catheter placements regardless of the initial catheter placement. The success of ASAP in the clinical AF was confirmed by the ablation outcomes and the electrogram patterns at the delineated area.

CONCLUSION

Our analysis indicates the potential of the ASAP mapping to provide accurate information about the area of the meandering repeating-pattern AF sources as AF ablation targets for effective AF termination. Our algorithm could improve the success of AF catheter ablation therapy by locating and subsequently targeting patient-specific and repeating-pattern AF sources inside the atria.

Granger Causality-Based Analysis for Classification of Fibrillation Mechanisms and Localization of Rotational Drivers

BACKGROUND

The mechanisms sustaining myocardial fibrillation remain disputed, partly due to a lack of mapping tools that can accurately identify the mechanism with low spatial resolution clinical recordings. Granger causality (GC) analysis, an econometric tool for quantifying causal relationships between complex time-series, was developed as a novel fibrillation mapping tool and adapted to low spatial resolution sequentially acquired data.

METHODS

Ventricular fibrillation (VF) optical mapping was performed in Langendorff-perfused Sprague-Dawley rat hearts (n=18), where novel algorithms were developed using GC-based analysis to (1) quantify causal dependence of neighboring signals and plot GC vectors, (2) quantify global organization with the causality pairing index, a measure of neighboring causal signal pairs, and (3) localize rotational drivers (RDs) by quantifying the circular interdependence of neighboring signals with the circular interdependence value. GC-based mapping tools were optimized for low spatial resolution from downsampled optical mapping data, validated against high-resolution phase analysis and further tested in previous VF optical mapping recordings of coronary perfused donor heart left ventricular wedge preparations (n=12), and adapted for sequentially acquired intracardiac electrograms during human persistent atrial fibrillation mapping (n=16).

RESULTS

Global VF organization quantified by causality pairing index showed a negative correlation at progressively lower resolutions (50% resolution: P=0.006, R2=0.38, 12.5% resolution, P=0.004, R2=0.41) with a phase analysis derived measure of disorganization, locations occupied by phase singularities. In organized VF with high causality pairing index values, GC vector mapping characterized dominant propagating patterns and localized stable RDs, with the circular interdependence value showing a significant difference in driver versus nondriver regions (0.91±0.05 versus 0.35±0.06, P=0.0002). These findings were further confirmed in human VF. In persistent atrial fibrillation, a positive correlation was found between the causality pairing index and presence of stable RDs (P=0.0005,R2=0.56). Fifty percent of patients had RDs, with a low incidence of 0.9±0.3 RDs per patient.

CONCLUSIONS

GC-based fibrillation analysis can measure global fibrillation organization, characterize dominant propagating patterns, and map RDs using low spatial resolution sequentially acquired data.

Time resolution for wavefront and phase singularity tracking using activation maps in cardiac propagation models

The dynamics of cardiac fibrillation can be described by the number, the trajectory, the stability, and the lifespan of phase singularities (PSs). Accurate PS tracking is straightforward in simple uniform tissues but becomes more challenging as fibrosis, structural heterogeneity, and strong anisotropy are combined. In this paper, we derive a mathematical formulation for PS tracking in two-dimensional reaction-diffusion models. The method simultaneously tracks wavefronts and PS based on activation maps at full spatiotemporal resolution. PS tracking is formulated as a linear assignment problem solved by the Hungarian algorithm. The cost matrix incorporates information about distances between PS, chirality, and wavefronts. A graph of PS trajectories is generated to represent the creations and annihilations of PS pairs. Structure-preserving graph transformations are applied to provide a simplified description at longer observation time scales. The approach is validated in 180 simulations of fibrillation in four different types of substrates featuring, respectively, wavebreaks, ionic heterogeneities, fibrosis, and breakthrough patterns. The time step of PS tracking is studied in the range from 0.1 to 10 ms. The results show the benefits of improving time resolution from 1 to 0.1 ms. The tracking error rate decreases by an order of magnitude because the occurrence of simultaneous events becomes less likely. As observed on PS survival curves, the graph-based analysis facilitates the identification of macroscopically stable rotors despite wavefront fragmentation by fibrosis.

Challenges Associated with Interpreting Mechanisms of AF

Determining optimal treatment strategies for complex arrhythmogenesis in AF is confounded by the lack of consensus regarding the mechanisms causing AF. Studies report different mechanisms for AF, ranging from hierarchical drivers to anarchical multiple activation wavelets. Differences in the assessment of AF mechanisms are likely due to AF being recorded across diverse models using different investigational tools, spatial scales and clinical populations. The authors review different AF mechanisms, including anatomical and functional re-entry, hierarchical drivers and anarchical multiple wavelets. They then describe different cardiac mapping techniques and analysis tools, including activation mapping, phase mapping and fibrosis identification. They explain and review different data challenges, including differences between recording devices in spatial and temporal resolutions, spatial coverage and recording surface, and report clinical outcomes using different data modalities. They suggest future research directions for investigating the mechanisms underlying human AF.

Focal source and trigger mapping in atrial fibrillation: Randomized controlled trial evaluating a novel adjunctive ablation strategy

BACKGROUND

Intraoperative mapping has demonstrated focal activations during human atrial fibrillation (AF). These putative AF sources can manifest sustained periodic bipolar and unipolar QS electrograms (EGMs). We have automated the detection of these EGM features using our validated Focal Source and Trigger (FaST) computational algorithm.

OBJECTIVE

The purpose of this study was to conduct a randomized controlled pilot evaluating the feasibility and efficacy of FaST mapping/ablation as an adjunct to pulmonary vein isolation (PVI) in reducing AF recurrence.

METHODS

We randomized 80 patients with high-burden paroxysmal or persistent AF (age 61 ± 10 years; 75% male) to PVI alone (n = 41) or PVI+FaST mapping/ablation (n = 39). The primary endpoint was time to AF recurrence >30 seconds between 3 and 12 months after 1 procedure.

RESULTS

FaST sites were identified in all but 1 patient and were localized to pulmonary vein (PV) (2.1 ± 1.1 per patient) and extra-PV regions (2.8 ± 1.4 per patient). FaST mapping and ablation times were 27 ± 9 minutes and 8.5 ± 5 minutes, respectively. Patients with AF termination during ablation had greater AF cycle length prolongation with PVI+FaST than PVI (Δ20 ± 14 ms vs Δ5 ± 17 ms; P = .046). Freedom from AF recurrence at 12 months was higher in PVI+FaST vs PVI for patients off antiarrhythmic drugs (74% vs 51%; hazard ratio 0.48; 95% confidence interval 0.21-1.08; P = .064) but did not quite reach statistical significance. Major adverse events were similar between the 2 groups.

CONCLUSION

In this randomized controlled pilot, real-time FaST mapping provided an intuitive, automated approach for localizing focal AF sources. FaST ablation as an adjunct to PVI may reduce AF recurrence, which requires verification with a larger multicenter trial.

RADAR: A Multicenter Food and Drug Administration Investigational Device Exemption Clinical Trial of Persistent Atrial Fibrillation

Supplemental Digital Content is available in the text.

Pulmonary vein isolation is insufficient to treat all patients with persistent atrial fibrillation (AF), and effective adjunctive ablation strategies are needed. Ablation of AF drivers holds promise, but current technologies to identify drivers are limited by spatial resolution. In a single-arm, first-in-human, investigator-initiated Food and Drug Administration Investigational Device Exemption study, we used a novel system for real-time, high-resolution identification of AF drivers in persistent AF.

Computational models in cardiology

The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.