Estimation of high-density activation maps during atrial fibrillation

The study of activation maps using multi-electrode arrays (MEA) can help to understand atrial fibrillation (AF) mechanisms. Activation mapping based on recorded unipolar electrograms (u-EGM) rely on the local activation time (LAT) detector, which has a limited robustness, accuracy, and generally requires manual post-edition. In general, LAT detection ignores spatiotemporal information about activation and conduction conveyed by the relation between signals of the MEA sensor. This work proposes an approach to construct activation maps by simultaneous analysis of u-EGMs from small clusters of MEA electrodes. The algorithm iteratively fits an activation pattern model to the acquired data. Accuracy was evaluated by comparing with audited maps created by expert electrophysiologists from a patient undergoing open-chest surgery during AF. The estimation error was -0.29 ± 6.01 ms (236 maps, 28369 LATs) with high correlation (¿ = 0.93). Therefore, activation maps can be decomposed into local activation patterns derived from fitting an activation model, resulting in smooth and comprehensive high-density activation maps

Spatiotemporal Model-Based Estimation of High-Density Atrial Fibrillation Activation Map

Examination of activation maps using multi-electrode array (MEA) sensors can help to understand the mechanisms underlying atrial fibrillation (AF). Classically, creation of activation maps starts with detection of local activation times (LAT) based on recorded unipolar electrograms. LAT detection has a limited robustness and accuracy, and generally requires manual edition. In general, LAT detection ignores spatiotemporal information of activation embedded in the relation between electrode signals on the MEA mapping sensor. In this work, a unified approach to construct activation maps by simultaneous analysis of activation patterns from overlapping clusters of MEA electrodes is proposed. An activation model fits on the measured data by iterative optimization of the model parameters based on a cost function. The accuracy of the estimated activation maps was evaluated by comparison with audited maps created by expertelectrophysiologists during sinus rhythm (SR) and AF. During SR recordings, 25 activation maps (3100 LATs) were automatically determined resulting in an average LAT estimation error of -0.66 ±2.00msand a correlation of ¿s=0.98compared to the expert reference. During AF recordings (235 maps, 28226 LATs), the estimation error was -0.83 ±6.02mswith only a slightly lower correlation (¿s=0.93). In conclusion, complex spatial activation patterns can be decomposed into local activation patterns derived from fitting an activation model, allowing the creation of smooth and comprehensive high-density activation maps

Noninvasive Estimation of Epicardial Dominant High-Frequency Regions During Atrial Fibrillation

[EN] Introduction Ablation of high dominant frequency (DF) sources in patients with atrial fibrillation (AF) is an effective treatment option for paroxysmal AF. The aim of this study was to evaluate the accuracy of noninvasive estimation of DF and electrical patterns determination by solving the inverse problem of the electrocardiography. Methods Four representative AF patients with left-to-right and right-to-left atrial DF patterns were included in the study. For each patient, intracardiac electrograms from both atria were recorded simultaneously together with 67-lead body surface recordings. In addition to clinical recordings, realistic mathematical models of atria and torso anatomy with different DF patterns of AF were used. For both mathematical models and clinical recordings, inverse-computed electrograms were compared to intracardiac electrograms in terms of voltage, phase, and frequency spectrum relative errors. Results Comparison between intracardiac and inverse computed electrograms for AF patients showed 8.8 ± 4.4% errors for DF, 32 ± 4% for voltage, and 65 ± 4% for phase determination. These results were corroborated by mathematical simulations showing that the inverse problem solution was able to reconstruct the frequency spectrum and the DF maps with relative errors of 5.5 ± 4.1%, whereas the reconstruction of the electrograms or the instantaneous phase presented larger relative errors (i.e., 38 ± 15% and 48 ± 14 % respectively, P < 0.01). Conclusions Noninvasive reconstruction of atrial frequency maps can be achieved by solving the inverse problem of electrocardiography with a higher accuracy than temporal distribution patterns.Pedrón-Torrecilla, J.; Rodrigo Bort, M.; M. Climent, A.; Liberos, A.; Pérez-David E; Bermejo, J.; Arenal, A.... (2016). Noninvasive Estimation of Epicardial Dominant High-Frequency Regions During Atrial Fibrillation. Journal of Cardiovascular Electrophysiology. 27(4):435-442. doi:https://doi.org/10.1111/jce.12931S43544227

Non-invasive estimation of left atrial dominant frequency in atrial fibrillation from different electrode sites: Insight from body surface potential mapping

© 2014, CardioFront LLC. All rights reserved. The dominant driving sources of atrial fibrillation are often found in the left atrium, but the expression of left atrial activation on the body surface is poorly understood. Using body surface potential mapping and simultaneous invasive measurements of left atrial activation our aim was to describe the expression of the left atrial dominant fibrillation frequency across the body surface. 20 patients in atrial fibrillation were studied. The spatial distributions of the dominant atrial fibrillation frequency across anterior and posterior sites on the body surface were quantified. Their relationship with invasive left atrial dominant fibrillation frequency was assessed by linear regression analysis, and the coefficient of determination was calculated for each body surface site. The correlation between intracardiac and body surface dominant frequency was significantly higher with posterior compared with anterior sites (coefficient of determination 67±8% vs 48±2%,

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

A multi-variate predictability framework to assess invasive cardiac activity and interactions during atrial fibrillation

Objective: This study introduces a predictability framework based on the concept of Granger causality (GC), in order to analyze the activity and interactions between different intracardiac sites during atrial fibrillation (AF). Methods: GC-based interactions were studied using a three-electrode analysis scheme with multi-variate autoregressive models of the involved preprocessed intracardiac signals. The method was evaluated in different scenarios covering simulations of complex atrial activity as well as endocardial signals acquired from patients. Results: The results illustrate the ability of the method to determine atrial rhythm complexity and to track and map propagation during AF. Conclusion: The proposed framework provides information on the underlying activation and regularity, does not require activation detection or postprocessing algorithms and is applicable for the analysis of any multielectrode catheter. Significance: The proposed framework can potentially help to guide catheter ablation interventions of AF

Instantaneous Amplitude and Frequency Modulations Detect the Footprint of Rotational Activity and Reveal Stable Driver Regions as Targets for Persistent Atrial Fibrillation Ablation

RATIONALE: Costly proprietary panoramic multielectrode (64-256) acquisition systems are being increasingly used together with conventional electroanatomical mapping systems for persistent atrial fibrillation (PersAF) ablation. However, such approaches target alleged drivers (rotational/focal) regardless of their activation frequency dynamics. OBJECTIVES: To test the hypothesis that stable regions of higher than surrounding instantaneous frequency modulation (iFM) drive PersAF and determine whether rotational activity is specific for such regions. METHODS AND RESULTS: First, novel single-signal algorithms based on instantaneous amplitude modulation (iAM) and iFM to detect rotational-footprints without panoramic multielectrode acquisition systems were tested in 125 optical movies from 5 ex vivo Langendorff-perfused PersAF sheep hearts (sensitivity/specificity, 92.6/97.5%; accuracy, 2.5-mm) and in computer simulations. Then, 16 pigs underwent high-rate atrial pacing to develop PersAF. After a median (interquartile range [IQR]) of 4.4 (IQR, 2.5-9.9) months of high-rate atrial pacing followed by 4.1 (IQR, 2.7-5.4) months of self-sustained PersAF, pigs underwent in vivo high-density electroanatomical atrial mapping (4920 [IQR, 4435-5855] 8-second unipolar signals per map). The first 4 out of 16 pigs were used to adapt ex vivo optical proccessing of iFM/iAM to in vivo electrical signals. In the remaining 12 out of 16 pigs, regions of higher than surrounding average iFM were considered leading-drivers. Two leading-driver + rotational-footprint maps were generated 2.6 (IQR, 2.4-2.9) hours apart to test leading-driver spatiotemporal stability and guide ablation. Leading-driver regions (2.5 [IQR, 2.0-4.0] regions/map) exactly colocalized (95.7%) in the 2 maps, and their ablation terminated PersAF in 92.3% of procedures (radiofrequency until termination, 16.9 [IQR, 9.2-35.8] minutes; until nonsustainability, 20.4 [IQR, 12.8-44.0] minutes). Rotational-footprints were found at every leading-driver region, albeit most (76.8% [IQR, 70.5%-83.6%]) were located outside. Finally, the translational ability of this approach was tested in 3 PersAF redo patients. CONCLUSIONS: Both rotational-footprints and spatiotemporally stable leading-driver regions can be located using iFM/iAM algorithms without panoramic multielectrode acquisition systems. In pigs, ablation of leading-driver regions usually terminates PersAF and prevents its sustainability. Rotational activations are sensitive but not specific to such regions. Single-signal iFM/iAM algorithms could be integrated into conventional electroanatomical mapping systems to improve driver detection accuracy and reduce the cost of patient-tailored/mechanistic approaches.This study was supported by the European Regional Development Fund and the Spanish Ministry of Science, Innovation and Universities (SAF2016-80324-R). The CNIC is supported by the Spanish Ministry of Science, Innovation and Universities and the Pro-CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).S

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

Background: Ablation is an effective therapy in atrial fibrillation (AF) patients in which an electrical driver can be identified. Objective: The aim of this study is to present and discuss a novel and strictly non-invasive approach to map and identify atrial regions responsible for AF perpetuation. Methods: Surface potential recordings of 14 patients with AF were recorded using a 67-lead recording system. Singularity points (SPs) were identified in surface phase maps after band-pass filtering at the highest dominant frequency (HDF). Mathematical models of combined atria and torso were constructed and used to investigate the ability of surface phase maps to estimate rotor activity in the atrial wall. Results: The simulations show that surface SPs originate at atrial SPs, but not all atrial SPs are reflected at the surface. Stable SPs were found in AF signals during 8.3±5.7% vs. 73.1±16.8% of the time in unfiltered vs. HDF-filtered patient data respectively (p<0.01). The average duration of each rotational pattern was also lower in unfiltered than in HDF-filtered AF signals (160±43 vs. 342±138 ms, p<0.01) resulting in 2.8±0.7 rotations per rotor. Band-pass filtering reduced the apparent meandering of surface HDF rotors by reducing the effect of the atrial electrical activity taking place at different frequencies. Torso surface SPs representing HDF rotors during AF were reflected at specific areas corresponding to the fastest atrial location. Conclusion: Phase analysis of surface potential signals after HDF-filtering during AF shows reentrant drivers localized to either the LA or RA, helping in localizing ablation targetsThis work was supported in part by the Spanish Society of Cardiology (Becas Investigacion Clinica 2009); the Universitat Politecnica de Valencia through its research initiative program; the Generalitat Valenciana grant (ACIF/2013/021); the Ministerio de Economia y Competitividad, Rod RIC; the Centro Nacional de Investigaciones Cardiovasculares (proyecto CNIC-13); the Coulter Foundation from the Biomedical Engineering Department, University of Michigan; the Gelman Award from the Cardiovascular Division, University of Michigan; the National Heart, Lung, and Blood Institute grants (P01411.039707, P01-1111187226, and R01-11L118304); and the Leducq Foundation. Dr Femandez-Aviles served on the advisory board of Medtronic and has received research funding from St Jude Medical Spain. Dr Berenfeld has received research support from Medtronic and St Jude Medical; he is a colbunder and scientific officer of Rhythm Solutions. None of the companies disclosed financed the research described in this article.Rodrigo Bort, M.; Guillem Sánchez, MS.; Climent, AM.; Pedrón Torrecilla, J.; Liberos Mascarell, A.; Millet Roig, J.; Fernandez-Aviles, F.... (2014). Body surface localization of left and right atrial high-frequency rotors in atrial fibrillation patients: A clinical-computational study. Heart Rhythm. 11(9):1584-1591. https://doi.org/10.1016/j.hrthm.2014.05.013S1584159111

Identification of atrial fibrillation drivers by means of concentric ring electrodes

The prevalence of atrial fibrillation (AF) has tripled in the last 50 years due to population aging. High-frequency (DFdriver) activated atrial regions lead the activation of the rest of the atria, disrupting the propagation wavefront. Fourier based spectral analysis of body surface potential maps have been proposed for DFdriver identification, although these approaches present serious drawbacks due to their limited spectral resolution for short AF epochs and the blurring effect of the volume conductor. Laplacian signals (BC-ECG) from bipolar concentric ring electrodes (CRE) have been shown to outperform the spatial resolution achieved with conventional unipolar recordings. Our aimed was to determine the best DFdriver estimator in endocardial electrograms and to assess the BC-ECG capacity of CRE to quantify AF activity non-invasively