Mapping the Substrate of Atrial Fibrillation: Tools and Techniques

Atrial fibrillation (AF) is the most common cardiac arrhythmia that affects an estimated 33.5 million people worldwide. Despite its prevalence and economic burden, treatments remain relatively ineffective. Interventional treatments using catheter ablation have shown more success in cure rates than pharmacologic methods for AF. However, success rates diminish drastically in patients with more advanced forms of the disease. The focus of this research is to develop a mapping strategy to improve the success of ablation. To achieve this goal, I used a computational model of excitation in order to simulate atrial fibrillation and evaluate mapping strategies that could guide ablation. I first propose a substrate guided mapping strategy to allow patient-specific treatment rather than a one size fits all approach. Ablation guided by this method reduced AF episode durations compared to baseline durations and an equal amount of random ablation in computational simulations. Because the accuracy of electrogram mapping is dependent upon catheter-tissue contact, I then provide a method to identify the distance between the electrode recording sites and the tissue surface using only the electrogram signal. The algorithm was validated both in silico and in vivo. Finally, I develop a classification algorithm for the identification of activation patterns using simultaneous, multi-site electrode recordings to aid in the development of an appropriate ablation strategy during AF. These findings provide a framework for future mapping and ablation studies in humans and assist in the development of individualized ablation strategies for patients with higher disease burden

Analysis of Atrial Electrograms

This work provides methods to measure and analyze features of atrial electrograms - especially complex fractionated atrial electrograms (CFAEs) - mathematically. Automated classification of CFAEs into clinical meaningful classes is applied and the newly gained electrogram information is visualized on patient specific 3D models of the atria. Clinical applications of the presented methods showed that quantitative measures of CFAEs reveal beneficial information about the underlying arrhythmia

Recurring patterns of atrial fibrillation in surface ECG predict restoration of sinus rhythm by catheter ablation

Background Non-invasive tools to help identify patients likely to benefit from catheter ablation (CA) of atrial fibrillation (AF) would facilitate personalised treatment planning. Aim To investigate atrial waveform organisation through recurrence plot indices (RPI) and their ability to predict CA outcome. Methods One minute 12-lead ECG was recorded before CA from 62 patients with AF (32 paroxysmal AF; 45 men; age 57±10 years). Organisation of atrial waveforms from i) TQ intervals in V1 and ii) QRST suppressed continuous AF waveforms (CAFW), were quantified using RPI: percentage recurrence (PR), percentage determinism (PD), entropy of recurrence (ER). Ability to predict acute (terminating vs. non-terminating AF), 3-month and 6-month postoperative outcome (AF vs. AF free) were assessed. Results RPI either by TQ or CAFW analysis did not change significantly with acute outcome. Patients arrhythmia-free at 6-month follow-up had higher organisation in TQ intervals by PD (

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

This is an author-created, un-copyedited versíon of an article published in Physiological Measurement. IOP Publishing Ltd is not responsíble for any errors or omissíons in this versíon of the manuscript or any versíon derived from it. The Versíon of Record is available online at https://doi.org/10.1088/1361-6579/ab97c1.[EN] Objective: Atrial dominant frequency (DF) maps undergoing atrial fibrillation (AF) presented good spatial correlation with those obtained with the non-invasive body surface potential mapping (BSPM). In this study, a robust BSPM-DF calculation method based on wavelet analysis is proposed. Approach: Continuous wavelet transform along 40 scales in the pseudo-frequency range of 3¿30 Hz is performed in each BSPM signal using a Gaussian mother wavelet. DFs are estimated from the intervals between the peaks, representing the activation times, in the maximum energy scale. The results are compared with the traditionally widely applied Welch periodogram and the robustness was tested on different protocols: increasing levels of white Gaussian noise, artificial DF harmonics presence and reduction in the number of leads. A total of 11 AF simulations and 12 AF patients are considered in the analysis. For each patient, intracardiac electrograms were acquired in 15 locations from both atria. The accuracy of both methods was assessed by calculating the absolute errors of the highest DFBSPM (HDFBSPM) with respect to the atrial HDF, either simulated or intracardially measured, and assumed correct if ¿1 Hz. The spatial distribution of the errors between torso DFs and atrial HDFs were compared with atria driving mechanism locations. Torso HDF regions, defined as portions of the maps with |DF ¿ HDFBSPM| ¿ 0.5 Hz were identified and the percentage of the torso occuping these regions was compared between methods. The robustness of both methods to white Gaussian noise, ventricular influence and harmonics, and to lower spatial resolution BSPM lead layouts was analyzed: computer AF models (567 leads vs 256 leads down to 16 leads) and patient data (67 leads vs 32 and 16 leads). Main results: The proposed method allowed an improvement in non-invasive estimation of the atria HDF. For the models the median relative errors were 7.14% for the wavelet-based algorithm vs 60.00% for the Welch method; in patients, the errors were 10.03% vs 12.66%, respectively. The wavelet method outperformed the Welch approach in correct estimations of atrial HDFs in models (81.82% vs 45.45%, respectively) and patients (66.67% vs 41.67%). A low positive BSPM-DF map correlation was seen between the techniques (0.47 for models and 0.63 for patients), highlighting the overall differences in DF distributions. The wavelet-based algorithm was more robust to white Gaussian noise, residual ventricular activity and harmonics, and presented more consistent results in lead layouts with low spatial resolution. Significance: Estimation of atrial HDFs using BSPM is improved by the proposed wavelet-based algorithm, helping to increase the non-invasive diagnostic ability in AF.This study was supported in part by grants from Sao Paulo Research Foundation (2017/19775-3), Instituto de Salud Carlos III FEDER (Fondo Europeo de Desarrollo Regional PI17/01106) and Generalitat Valenciana Grants (AICO/2018/267).Marques, V.; Rodrigo Bort, M.; Guillem Sánchez, MS.; Salinet, J. (2020). A robust wavelet-based approach for dominant frequency analysis of atrial fibrillation in body surface signals. Physiological Measurement. 41(7):1-14. https://doi.org/10.1088/1361-6579/ab97c1S11441

The stationary wavelet transform as an efficient reductor of powerline interference for atrial bipolar electrograms in cardiac electrophysiology

[EN] Objective :The most relevant source of signal contamination in the cardiac electrophysiology (EP) laboratory is the ubiquitous powerline interference (PLI). To reduce this perturbation, algorithms including common fixed-bandwidth and adaptive-notch filters have been proposed. Although such methods have proven to add artificial fractionation to intra-atrial electrograms (EGMs), they are still frequently used. However, such morphological alteration can conceal the accurate interpretation of EGMs, specially to evaluate the mechanisms supporting atrial fibrillation (AF), which is the most common cardiac arrhythmia. Given the clinical relevance of AF, a novel algorithm aimed at reducing PLI on highly contaminated bipolar EGMs and, simultaneously, preserving their morphology is proposed. Approach: The method is based on the wavelet shrinkage and has been validated through customized indices on a set of synthesized EGMs to accurately quantify the achieved level of PLI reduction and signal morphology alteration. Visual validation of the algorithm¿s performance has also been included for some real EGM excerpts. Main results: The method has outperformed common filtering-based and wavelet-based strategies in the analyzed scenario. Moreover, it possesses advantages such as insensitivity to amplitude and frequency variations in the PLI, and the capability of joint removal of several interferences. Significance: The use of this algorithm in routine cardiac EP studies may enable improved and truthful evaluation of AF mechanisms.Research supported by grants DPI2017-83952-C3 MINECO/AEI/FEDER, UE and SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha.Martinez-Iniesta, M.; Ródenas, J.; Rieta, JJ.; Alcaraz, R. (2019). 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Endocardial activation mapping of human atrial fibrillation

Successful ablation of arrhythmias depends upon interpretation of the mechanism. However, in persistent atrial fibrillation (AF) ablation is currently directed towards the mechanism that initiates paroxysmal AF. We sought to address the hypothesis that atrial activation patterns during persistent AF may help determine the underlying mechanism. Activation mapping of AF wavefronts is labor intensive and often restricted to short time segments in limited atrial locations. RETRO-Mapping was developed to identify uniform wavefronts that occur during AF, and summate all wavefront vectors on to an orbital plot. Uniform wavefronts were mapped using RETRO-Mapping during sinus rhythm, atrial tachycardia, and atrial fibrillation, and validated against detailed manual analysis of the same wavefronts with conventional isochronal mapping. RETRO-Mapping was found to have comparable accuracy to isochronal mapping. RETRO-Mapping was then used to investigate atrial activation patterns during persistent AF. Atrial activation patterns demonstrated evidence of spatiotemporal stability over long time periods. Orbital plots created at different time points in the same location remained unchanged. Together with this important discovery, both fractionation and bipolar voltage were also demonstrated to express stability over time. Spatiotemporal stability during persistent AF enables sequential mapping as an acceptable technique. This property also allowed the development of a method for displaying sequentially mapped locations on a single map – RETRO-Choropleth Map. These findings go against the multiple wavelet hypothesis with random activation. Having gained insights in to these stable activation patterns, extensive analysis was undertaken to identify the presence of focal activation. Focal activations were identified during persistent AF. RETRO-Mapping was used to show that adjacent activation patterns were not related to focal activations. Lastly, the effect of pulmonary vein isolation (PVI) was studied by mapping atrial activation patterns before and after PVI. RETRO-Mapping showed that PVI leads to increased organisation of AF in most patients, supporting a mechanistic role of the pulmonary veins in persistent AF. In conclusion, a new technique has been developed and validated for automated activation mapping of persistent AF. These techniques could be used to guide additional ablation strategies beyond PVI for patients with persistent AF.Open Acces

Spatial Characterization and Estimation of Intracardiac Propagation Patterns During Atrial Fibrillation

This doctoral thesis is in the field of biomedical signal processing with focus on methods for the analysis of atrial fibrillation (AF). Paper I of the present thesis addresses the challenge of extracting spatial properties of AF from body surface signals. Different parameters are extracted to estimate the preferred direction of atrial activation and the complexity of the atrial activation pattern. In addition, the relation of the spatial properties to AF organization, which is quantified by AF frequency, is evaluated. While no significant correlation between the preferred direction of atrial activation and AF frequency could be observed, the complexity of the atrial activation pattern was found to increase with AF frequency. The remaining three papers deal with the analysis of the propagation of the electrical activity in the atria during AF based on intracardiac signals. In Paper II, a time-domain method to quantify propagation patterns along a linear catheter based on the detected atrial activation times is developed. Taking aspects on intra-atrial signal organization into account, the detected activation times are combined into wavefronts, and parameters related to the consistency of the wavefronts over time and the activation order along the catheter are extracted. Furthermore, the potential relationship of the extracted parameters to established measures from body surface signals is investigated. While the degree of wavefront consistency was not reflected by the applied body surface measures, AF frequency could distinguish between recordings with different degrees of intra-atrial signal organization. This supports the role of AF frequency as an organization measure of AF. In Paper III, a novel method to analyze intracardiac propagation patterns based on causality analysis in the frequency domain is introduced. In particular, the approach is based on the partial directed coherence (PDC), which evaluates directional coupling between multiple signals in the frequency domain. The potential of the method is illustrated with simulation scenarios based on a detailed ionic model of the human atrial cell as well as with real data recordings, selected to present typical propagation mechanisms and recording situations in atrial tachyarrhythmias. For simulated data, the PDC is correctly reflecting the direction of coupling and thus the propagation between all recording sites. For real data, clear propagation patterns are identified which agree with previous clinical observations. Thus, the results illustrate the ability of the novel approach to identify propagation patterns from intracardiac signals during AF which can provide important information about the underlying AF mechanisms, potentially improving the planning and outcome of ablation. However, spurious couplings over long distances can be observed when analyzing real data comprised by a large number of simultaneously recorded signals, which gives room for further improvement of the method. The derivation of the PDC is entirely based on the fit of a multivariate autoregressive (MVAR) model, commonly estimated by the least-squares (LS) method. In Paper IV, the adaptive group least absolute selection and shrinkage operator (LASSO) is introduced in order to avoid overfitting of the MVAR model and to incorporate prior information such as sparsity of the solution. The sparsity can be motivated by the observation that direct couplings over longer distances are likely to be zero during AF; an information which has been further incorporated by proposing distance-adaptive group LASSO. In simulations, adaptive and distance-adaptive group LASSO are found to be superior to LS estimation in terms of both detection and estimation accuracy. In addition, the results of both simulations and real data analysis indicate that further improvements can be achieved when the distance between the recording sites is known or can be estimated. This further promotes the PDC as a method for analysis of AF propagation patterns, which may contribute to a better understanding of AF mechanisms as well as improved AF treatment

System Identification applied to Cardiac Activation

Clarifying the mechanisms maintaining atrial activity during atrial fibrillation (AF), still remains as a relevant topic. The purpose of this master thesis is to apply correlation analysis and system identification methods to study spatial and temporal propagation of atrial activation along coronary sinus (situated in the posterior left part of the heart, in the groove between left atrium and left ventricle) during paroxysmal atrial fibrillation (PAF) using data recorded catheter from 7 different patients. Furthermore, interatrial mechanisms of impulse conduction can be derived due to the position of coronary sinus. This study demonstrated consistency in electrical activity propagation during atrial fibrillation along coronary sinus in five patients out of six included. Nevertheless, direction and speed of propagation resulted dependent on the patient. The method was tried out during sinus rhythm (SR) obtaining the expected high consistency in propagation direction and speed which represented an interesting reference point to compare with atrial fibrillation results. Furthermore, linear relation among endocardial electrograms from coronary sinus and time invariant systems have been presented by computing simple linear models based on least squares method. Secondly, an impulse response method has been applied to reproduce atrial activations during atrial fibrillation and during sinus rhythm in ten endocardial electrograms from coronary sinus simultaneously. In this case, there is no need of measuring the input which is considered to be an impulse generated in the sinus node. This method was only useful in local prediction of atrial activations but not for prediction in the long term. Finally, Subspace Model Identification (SMI) methods have been applied to show an input-output relation among signals from inside the heart (endocardial electrograms from coronary sinus) and signals from outside the heart (V1 Surface-ECG)