Mechanistically based mapping of human cardiac fibrillation

Simultaneous Whole-Chamber Non-contact Mapping of Highest Dominant Frequency Sites During Persistent Atrial Fibrillation: A Prospective Ablation Study

Purpose

Sites of highest dominant frequency (HDF) are implicated by many proposed mechanisms underlying persistent atrial fibrillation (persAF). We hypothesized that prospectively identifying and ablating dynamic left atrial HDF sites would favorably impact the electrophysiological substrate of persAF. We aim to assess the feasibility of prospectively identifying HDF sites by global simultaneous left atrial mapping.

Methods

PersAF patients with no prior ablation history underwent global simultaneous left atrial non-contact mapping. 30 s of electrograms recorded during AF were exported into a bespoke MATLAB interface to identify HDF regions, which were then targeted for ablation, prior to pulmonary vein isolation. Following ablation of each region, change in AF cycle length (AFCL) was documented (≥ 10 ms considered significant). Baseline isopotential maps of ablated regions were retrospectively analyzed looking for rotors and focal activation or extinction events.

Results

A total of 51 HDF regions were identified and ablated in 10 patients (median DF 5.8Hz, range 4.4-7.1Hz). An increase in AFCL of was seen in 20 of the 51 regions (39%), including AF termination in 4 patients. 5 out of 10 patients (including the 4 patients where AF termination occurred with HDF-guided ablation) were free from AF recurrence at 1 year. The proportion of HDF occurrences in an ablated region was not associated with change in AFCL (τ = 0.11, p = 0.24). Regions where AFCL decreased by 10 ms or more (i.e., AF disorganization) after ablation also showed lowest baseline spectral organization (p < 0.033 for any comparison). Considering all ablated regions, the average proportion of HDF events which were also HRI events was 8.0 ± 13%. Focal activations predominated (537/1253 events) in the ablated regions on isopotential maps, were modestly associated with the proportion of HDF occurrences represented by the ablated region (Kendall's τ = 0.40, p < 0.0001), and very strongly associated with focal extinction events (τ = 0.79, p < 0.0001). Rotors were rare (4/1253 events).

Conclusion

Targeting dynamic HDF sites is feasible and can be efficacious, but lacks specificity in identifying relevant human persAF substrate. Spectral organization may have an adjunctive role in preventing unnecessary substrate ablation. Dynamic HDF sites are not associated with observable rotational activity on isopotential mapping, but epi-endocardial breakthroughs could be contributory.

The Electrophysiology of Atrial Fibrillation: From Basic Mechanisms to Catheter Ablation

The electrophysiology of atrial fibrillation (AF) has always been a deep mystery in understanding this complex arrhythmia. The pathophysiological mechanisms of AF are complex and often remain unclear despite extensive research. Therefore, the implementation of basic science knowledge to clinical practice is challenging. After more than 20 years, pulmonary vein isolation (PVI) remains the cornerstone ablation strategy for maintaining the sinus rhythm (SR). However, there is no doubt that, in many cases, especially in persistent and long-standing persistent AF, PVI is not enough, and eventually, the restoration of SR occurs after additional intervention in the rest of the atrial myocardium. Substrate mapping is a modern challenge as it can reveal focal sources or rotational activities that may be responsible for maintaining AF. Whether these areas are actually the cause of the AF maintenance is unknown. If this really happens, then the targeted ablation may be the solution; otherwise, more rough techniques such as atrial compartmentalization may prove to be more effective. In this article, we attempt a broad review of the known pathophysiological mechanisms of AF, and we present the recent efforts of advanced technology initially to reveal the electrical impulse during AF and then to intervene effectively with ablation.

Diverse activation patterns during persistent atrial fibrillation by noncontact charge-density mapping of human atrium

BACKGROUND

Global simultaneous recording of atrial activation during atrial fibrillation (AF) can elucidate underlying mechanisms contributing to AF maintenance. A better understanding of these mechanisms may allow for an individualized ablation strategy to treat persistent AF. The study aims to characterize left atrial endocardial activation patterns during AF using noncontact charge-density mapping.

METHODS

Twenty-five patients with persistent AF were studied. Activation patterns were characterized into three subtypes: (i) focal with centrifugal activation (FCA); (ii) localized rotational activation (LRA); and (iii) localized irregular activation (LIA). Continuous activation patterns were analyzed and distributed in 18 defined regions in the left atrium.

RESULTS

A total of 144 AF segments with 1068 activation patterns were analyzed. The most common pattern during AF was LIA (63%) which consists of four disparate features of activation: slow conduction (45%), pivoting (30%), collision (16%), and acceleration (7%). LRA was the second-most common pattern (20%). FCA accounted for 17% of all activations, arising frequently from the pulmonary veins (PVs)/ostia. A majority of patients (24/25; 96%) showed continuous and highly dynamic patterns of activation comprising multiple combinations of FCA, LRA, and LIA, transitioning from one to the other without a discernible order. Preferential conduction areas were typically seen in the mid-anterior (48%) and lower-posterior (40%) walls.

CONCLUSION

Atrial fibrillation is characterized by heterogeneous activation patterns identified in PV-ostia and non-PV regions throughout the LA at varying locations between individuals. Clinical implications of individualized ablation strategies guided by charge-density mapping need to be determined.

Standardised Framework for Quantitative Analysis of Fibrillation Dynamics

The analysis of complex mechanisms underlying ventricular fibrillation (VF) and atrial fibrillation (AF) requires sophisticated tools for studying spatio-temporal action potential (AP) propagation dynamics. However, fibrillation analysis tools are often custom-made or proprietary, and vary between research groups. With no optimal standardised framework for analysis, results from different studies have led to disparate findings. Given the technical gap, here we present a comprehensive framework and set of principles for quantifying properties of wavefront dynamics in phase-processed data recorded during myocardial fibrillation with potentiometric dyes. Phase transformation of the fibrillatory data is particularly useful for identifying self-perpetuating spiral waves or rotational drivers (RDs) rotating around a phase singularity (PS). RDs have been implicated in sustaining fibrillation, and thus accurate localisation and quantification of RDs is crucial for understanding specific fibrillatory mechanisms. In this work, we assess how variation of analysis parameters and thresholds in the tracking of PSs and quantification of RDs could result in different interpretations of the underlying fibrillation mechanism. These techniques have been described and applied to experimental AF and VF data, and AF simulations, and examples are provided from each of these data sets to demonstrate the range of fibrillatory behaviours and adaptability of these tools. The presented methodologies are available as an open source software and offer an off-the-shelf research toolkit for quantifying and analysing fibrillatory mechanisms.

A robust computational framework for estimating 3D Bi-Atrial chamber wall thickness

Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. The atrial wall thickness (AWT) can potentially improve our understanding of the mechanism underlying atrial structure that drives AF and provides important clinical information. However, most existing studies for estimating AWT rely on ruler-based measurements performed on only a few selected locations in 2D or 3D using digital calipers. Only a few studies have developed automatic approaches to estimate the AWT in the left atrium, and there are currently no methods to robustly estimate the AWT of both atrial chambers. Therefore, we have developed a computational pipeline to automatically calculate the 3D AWT across bi-atrial chambers and extensively validated our pipeline on both ex vivo and in vivo human atria data. The atrial geometry was first obtained by segmenting the atrial wall from the MRIs using a novel machine learning approach. The epicardial and endocardial surfaces were then separated using a multi-planar convex hull approach to define boundary conditions, from which, a Laplace equation was solved numerically to automatically separate bi-atrial chambers. To robustly estimate the AWT in each atrial chamber, coupled partial differential equations by coupling the Laplace solution with two surface trajectory functions were formulated and solved. Our pipeline enabled the reconstruction and visualization of the 3D AWT for bi-atrial chambers with a relative error of 8% and outperformed existing algorithms by >7%. Our approach can potentially lead to improved clinical diagnosis, patient stratification, and clinical guidance during ablation treatment for patients with AF.

Rotors Detected by Phase Analysis of Filtered, Epicardial Atrial Fibrillation Electrograms Colocalize With Regions of Conduction Block

BACKGROUND

Several recent studies suggest rotors detected by phase mapping may act as main drivers of persistent atrial fibrillation. However, the electrophysiological nature of detected rotors remains unclear. We performed a direct, 1:1 comparison between phase and activation time mapping in high-density, epicardial, direct-contact mapping files of human atrial fibrillation.

METHODS

Thirty-eight unipolar electrogram files of 10 s duration were recorded in patients with atrial fibrillation (n=20 patients) using a 16×16 electrode array placed on the epicardial surface of the left atrial posterior wall or the right atrial free wall. Phase maps and isochrone wave maps were constructed for all recordings. For each detected phase singularity (PS) with a lifespan of >1 cycle length, the corresponding conduction pattern was investigated in the isochrone wave maps.

RESULTS

When using sinusoidal recomposition and Hilbert Transform, 138 PSs were detected. One hundred and four out of 138 PSs were detected within 1 electrode distance (1.5 mm) from a line of conduction block between nonrotating wavefronts detected by activation mapping. Far fewer rotating wavefronts were detected when rotating activity was identified based on wave mapping (18 out of 8219 detected waves). Fourteen out of these 18 cases were detected as PSs in phase mapping. Phase analysis of filtered electrograms produced by simulated wavefronts separated by conduction block also identified PSs on the line of conduction block.

CONCLUSIONS

PSs identified by phase analysis of filtered epicardial electrograms colocalize with conduction block lines identified by activation mapping. Detection of PSs using phase analysis has a low specificity for identifying rotating wavefronts during human atrial fibrillation using activation mapping.

EHRA White Paper: knowledge gaps in arrhythmia management—status 2019

Clinicians accept that there are many unknowns when we make diagnostic and therapeutic decisions. Acceptance of uncertainty is essential for the pursuit of the profession: bedside decisions must often be made on the basis of incomplete evidence. Over the years, physicians sometimes even do not realize anymore which the fundamental gaps in our knowledge are. As clinical scientists, however, we have to halt and consider what we do not know yet, and how we can move forward addressing those unknowns. The European Heart Rhythm Association (EHRA) believes that scanning the field of arrhythmia / cardiac electrophysiology to identify knowledge gaps which are not yet the subject of organized research, should be undertaken on a regular basis. Such a review (White Paper) should concentrate on research which is feasible, realistic, and clinically relevant, and should not deal with futuristic aspirations. It fits with the EHRA mission that these White Papers should be shared on a global basis in order to foster collaborative and needed research which will ultimately lead to better care for our patients. The present EHRA White Paper summarizes knowledge gaps in the management of atrial fibrillation, ventricular tachycardia/sudden death and heart failure.

Fully Automatic Left Atrium Segmentation From Late Gadolinium Enhanced Magnetic Resonance Imaging Using a Dual Fully Convolutional Neural Network

Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. Current treatments for AF remain suboptimal due to a lack of understanding of the underlying atrial structures that directly sustain AF. Existing approaches for analyzing atrial structures in 3-D, especially from late gadolinium-enhanced (LGE) magnetic resonance imaging, rely heavily on manual segmentation methods that are extremely labor-intensive and prone to errors. As a result, a robust and automated method for analyzing atrial structures in 3-D is of high interest. We have, therefore, developed AtriaNet, a 16-layer convolutional neural network (CNN), on 154 3-D LGE-MRIs with a spatial resolution of 0.625 mm ×0.625 mm ×1.25 mm from patients with AF, to automatically segment the left atrial (LA) epicardium and endocardium. AtriaNet consists of a multi-scaled, dual-pathway architecture that captures both the local atrial tissue geometry and the global positional information of LA using 13 successive convolutions and three further convolutions for merging. By utilizing computationally efficient batch prediction, AtriaNet was able to successfully process each 3-D LGE-MRI within 1 min. Furthermore, benchmarking experiments have shown that AtriaNet has outperformed the state-of-the-art CNNs, with a DICE score of 0.940 and 0.942 for the LA epicardium and endocardium, respectively, and an inter-patient variance of <0.001. The estimated LA diameter and volume computed from the automatic segmentations were accurate to within 1.59 mm and 4.01 cm3 of the ground truths. Our proposed CNN was tested on the largest known data set for LA segmentation, and to the best of our knowledge, it is the most robust approach that has ever been developed for segmenting LGE-MRIs. The increased accuracy of atrial reconstruction and analysis could potentially improve the understanding and treatment of AF.

Improved Multiscale Entropy Technique with Nearest-Neighbor Moving-Average Kernel for Nonlinear and Nonstationary Short-Time Biomedical Signal Analysis

Analysis of biomedical signals can yield invaluable information for prognosis, diagnosis, therapy evaluation, risk assessment, and disease prevention which is often recorded as short time series data that challenges existing complexity classification algorithms such as Shannon entropy (SE) and other techniques. The purpose of this study was to improve previously developed multiscale entropy (MSE) technique by incorporating nearest-neighbor moving-average kernel, which can be used for analysis of nonlinear and non-stationary short time series physiological data. The approach was tested for robustness with respect to noise analysis using simulated sinusoidal and ECG waveforms. Feasibility of MSE to discriminate between normal sinus rhythm (NSR) and atrial fibrillation (AF) was tested on a single-lead ECG. In addition, the MSE algorithm was applied to identify pivot points of rotors that were induced in ex vivo isolated rabbit hearts. The improved MSE technique robustly estimated the complexity of the signal compared to that of SE with various noises, discriminated NSR and AF on single-lead ECG, and precisely identified the pivot points of ex vivo rotors by providing better contrast between the rotor core and the peripheral region. The improved MSE technique can provide efficient complexity analysis of variety of nonlinear and nonstationary short-time biomedical signals.

Technical Considerations on Phase Mapping for Identification of Atrial Reentrant Activity in Direct- and Inverse-Computed Electrograms

BACKGROUND

Phase mapping has become a broadly used technique to identify atrial reentrant circuits for ablative therapy guidance. This work studies the phase mapping process and how the signal nature and its filtering affect the reentrant pattern characterization in electrogram (EGM), body surface potential mapping, and electrocardiographic imaging signals.

METHODS AND RESULTS

EGM, body surface potential mapping, and electrocardiographic imaging phase maps were obtained from 17 simulations of atrial fibrillation, atrial flutter, and focal atrial tachycardia. Reentrant activity was identified by singularity point recognition in raw signals and in signals after narrow band-pass filtering at the highest dominant frequency (HDF). Reentrant activity was dominantly present in the EGM recordings only for atrial fibrillation and some atrial flutter propagations patterns, and HDF filtering allowed increasing the reentrant activity detection from 60% to 70% of time in atrial fibrillation in unipolar recordings and from 0% to 62% in bipolar. In body surface potential mapping maps, HDF filtering increased from 10% to 90% the sensitivity, although provoked a residual false reentrant activity ≈30% of time. In electrocardiographic imaging, HDF filtering allowed to increase ≤100% the time with detected rotors, although provoked the apparition of false rotors during 100% of time. Nevertheless, raw electrocardiographic imaging phase maps presented reentrant activity just in atrial fibrillation recordings accounting for ≈80% of time.

CONCLUSIONS

Rotor identification is accurate and sensitive and does not require additional signal processing in measured or noninvasively computed unipolar EGMs. Bipolar EGMs and body surface potential mapping do require HDF filtering to detect rotors at the expense of a decreased specificity.

Memory effects, transient growth, and wave breakup in a model of paced atrium

The mechanisms underlying cardiac fibrillation have been investigated for over a century, but we are still finding surprising results that change our view of this phenomenon. The present study focuses on the transition from normal rhythm to spiral wave chaos associated with a gradual increase in the pacing rate. While some of our findings are consistent with existing experimental, numerical, and theoretical studies of this problem, one result appears to contradict the accepted picture. Specifically we show that, in a two-dimensional model of paced homogeneous atrial tissue, transition from discordant alternans to conduction block, wave breakup, reentry, and spiral wave chaos is associated with the transient growth of finite amplitude disturbances rather than a conventional instability. It is mathematically very similar to subcritical, or bypass, transition from laminar fluid flow to turbulence, which allows many of the tools developed in the context of fluid turbulence to be used for improving our understanding of cardiac arrhythmias.

Three-dimensional Integrated Functional, Structural, and Computational Mapping to Define the Structural "Fingerprints" of Heart-Specific Atrial Fibrillation Drivers in Human Heart Ex Vivo

BACKGROUND

Structural remodeling of human atria plays a key role in sustaining atrial fibrillation (AF), but insufficient quantitative analysis of human atrial structure impedes the treatment of AF. We aimed to develop a novel 3-dimensional (3D) structural and computational simulation analysis tool that could reveal the structural contributors to human reentrant AF drivers.

METHODS AND RESULTS

High-resolution panoramic epicardial optical mapping of the coronary-perfused explanted intact human atria (63-year-old woman, chronic hypertension, heart weight 608 g) was conducted during sinus rhythm and sustained AF maintained by spatially stable reentrant AF drivers in the left and right atrium. The whole atria (107×61×85 mm³) were then imaged with contrast-enhancement MRI (9.4 T, 180×180×360-μm³ resolution). The entire 3D human atria were analyzed for wall thickness (0.4-11.7 mm), myofiber orientations, and transmural fibrosis (36.9% subendocardium; 14.2% midwall; 3.4% subepicardium). The 3D computational analysis revealed that a specific combination of wall thickness and fibrosis ranges were primarily present in the optically defined AF driver regions versus nondriver tissue. Finally, a 3D human heart-specific atrial computer model was developed by integrating 3D structural and functional mapping data to test AF induction, maintenance, and ablation strategies. This 3D model reproduced the optically defined reentrant AF drivers, which were uninducible when fibrosis and myofiber anisotropy were removed from the model.

CONCLUSIONS

Our novel 3D computational high-resolution framework may be used to quantitatively analyze structural substrates, such as wall thickness, myofiber orientation, and fibrosis, underlying localized AF drivers, and aid the development of new patient-specific treatments.

Novel approaches for quantitative electrogram analysis for intraprocedural guidance for catheter ablation: A case of a patient with persistent atrial fibrillation

PURPOSE

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia that causes stroke affecting more than 2.3 million people in the US and is increasing in prevalence due to ageing population causing a new global epidemic. Catheter ablation with pulmonary vein isolation (PVI) to terminate AF is successful for paroxysmal AF but suffers limitations with persistent AF patients as current mapping methods cannot identify AF active substrates outside of PVI region. Recent evidences in the mechanistic understating of AF pathophysiology suggest that ectopic activity, localized re-entrant circuit with fibrillatory propagation and multiple circuit re-entries may all be involved in human AF. The authors developed novel electrogram analysis methods and validated using optical mapping data from isolated rabbit hearts to accurately identify rotor pivot points. The purpose of this study was to assess the feasibility of generating patient-specific 3D maps for intraprocedural guidance for catheter ablation using intracardiac electrograms from a persistent AF patient using novel electrogram analysis methods.

METHODS

A persistent AF patient with clinical appointment for AF ablation was recruited for this study with IRB approval. 1055 electrograms throughout the left and right atrium were obtained for offline analysis with the novel approaches such as multiscale entropy, multiscale frequency, recurrence period density entropy, kurtosis and empirical mode decomposition to generate patient specific 3D maps. 3D Shannon Entropy, Renyi Entropy and Dominant frequency maps were also generated for comparison purposes along with local activation time and complex fractionated electrogram analysis maps.

RESULTS

Patient specific 3D maps were obtained for each of the different approach. The 3D maps indicate potential active sites outside the PVI region. However, presence of rotors cannot be confirmed and validation of these approaches is required on a larger dataset.

CONCLUSIONS

Conventional catheter mapping system can be used for generating patient specific 3D maps with short time series analysis using the novel approaches.

Waveform Integrity in Atrial Fibrillation: The Forgotten Issue of Cardiac Electrophysiology

Atrial fibrillation (AF) is the most common arrhythmia in clinical practice with an increasing prevalence of about 15% in the elderly. Despite other alternatives, catheter ablation is currently considered as the first-line therapy for the treatment of AF. This strategy relies on cardiac electrophysiology systems, which use intracardiac electrograms (EGM) as the basis to determine the cardiac structures contributing to sustain the arrhythmia. However, the noise-free acquisition of these recordings is impossible and they are often contaminated by different perturbations. Although suppression of nuisance signals without affecting the original EGM pattern is essential for any other later analysis, not much attention has been paid to this issue, being frequently considered as trivial. The present work introduces the first thorough study on the significant fallout that regular filtering, aimed at reducing acquisition noise, provokes on EGM pattern morphology. This approach has been compared with more refined denoising strategies. Performance has been assessed both in time and frequency by well established parameters for EGM characterization. The study comprised synthesized and real EGMs with unipolar and bipolar recordings. Results reported that regular filtering altered substantially atrial waveform morphology and was unable to remove moderate amounts of noise, thus turning time and spectral characterization of the EGM notably inaccurate. Methods based on Wavelet transform provided the highest ability to preserve EGM morphology with improvements between 20 and beyond 40%, to minimize dominant atrial frequency estimation error with up to 25% reduction, as well as to reduce huge levels of noise with up to 10 dB better reduction. Consequently, these algorithms are recommended as a replacement of regular filtering to avoid significant alterations in the EGMs. This could lead to more accurate and truthful analyses of atrial activity dynamics aimed at understanding and locating the sources of AF.