Systematic differences of non-invasive dominant frequency estimation compared to invasive dominant frequency estimation in atrial fibrillation

An expert review of the inverse problem in electrocardiographic imaging for the non-invasive identification of atrial fibrillation drivers

BACKGROUND AND OBJECTIVE

Electrocardiographic imaging (ECGI) has emerged as a non-invasive approach to identify atrial fibrillation (AF) driver sources. This paper aims to collect and review the current research literature on the ECGI inverse problem, summarize the research progress, and propose potential research directions for the future.

METHODS AND RESULTS

The effectiveness and feasibility of using ECGI to map AF driver sources may be influenced by several factors, such as inaccuracies in the atrial model due to heart movement or deformation, noise interference in high-density body surface potential (BSP), inconvenient and time-consuming BSP acquisition, errors in solving the inverse problem, and incomplete interpretation of the AF driving source information derived from the reconstructed epicardial potential. We review the current research progress on these factors and discuss possible improvement directions. Additionally, we highlight the limitations of ECGI itself, including the lack of a gold standard to validate the accuracy of ECGI technology in locating AF drivers and the challenges associated with guiding AF ablation based on post-processed epicardial potentials due to the intrinsic difference between epicardial and endocardial potentials.

CONCLUSIONS

Before performing ablation, ECGI can provide operators with predictive information about the underlying locations of AF driver by non-invasively and globally mapping the biatrial electrical activity. In the future, endocardial catheter mapping technology may benefit from the use of ECGI to enhance the diagnosis and ablation of AF.

Recurrence quantification analysis for fine-scale characterisation of arrhythmic patterns in cardiac tissue

This paper uses recurrence quantification analysis (RQA) combined with entropy measures and organization indices to characterize arrhythmic patterns and dynamics in computer simulations of cardiac tissue. We performed different simulations of cardiac tissues of sizes comparable to the human heart atrium. In these simulations, we observed four classic arrhythmic patterns: a spiral wave anchored to a highly fibrotic region resulting in sustained re-entry, a meandering spiral wave, fibrillation, and a spiral wave anchored to a scar region that breaks up into wavelets away from the main rotor. A detailed analysis revealed that, within the same simulation, maps of RQA metrics could differentiate regions with regular AP propagation from ones with chaotic activity. In particular, the combination of two RQA metrics, the length of the longest diagonal string of recurrence points and the mean length of diagonal lines, was able to identify the location of rotor tips, which are the active elements that maintain spiral waves and fibrillation. By proposing low-dimensional models based on the mean value and spatial correlation of metrics calculated from membrane potential time series, we identify RQA-based metrics that successfully separate the four different types of cardiac arrhythmia into distinct regions of the feature space, and thus might be used for automatic classification, in particular distinguishing between fibrillation driven by self-sustaining chaos and that created by a persistent rotor and wavebreak. We also discuss the practical applicability of such an approach.

Identification of atrial fibrillation drivers by means of concentric ring electrodes

BACKGROUND AND OBJECTIVE

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

METHODS

31 AF episodes were simulated using realistic tridimensional models of the atria electrical activity and torso. Periodogram and autoregressive (AR) spectral estimators were computed and the percentile (P90^th, P95^th and P98^th) to impose on the dominant frequencies (DFs) across whole atria to define the best DFdriver estimator evaluated. The identification of DFdriver on DFs from BC-ECG and unipolar surface signals with conventional disc electrodes was compared.

RESULTS

The best DFdriver estimator was P95^th and AR order 100. BC-ECG signals allowed better detection of AF activity than unipolar signals, with a significantly greater percentage of electrode locations in which DFdriver was identified (p-value 0.0095).

CONCLUSIONS

The use of BC-ECG signals for body surface Laplacian potential mapping with CRE could be helpful for better AF diagnosis, prognosis and ablation procedures than those with conventional disk electrodes.

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

Electrocardiographic imaging (ECGI) is a technique to reconstruct non-invasively the electrical activity on the heart surface from body-surface potential recordings and geometric information of the torso and the heart. ECGI has shown scientific and clinical value when used to characterize and treat both atrial and ventricular arrhythmias. Regarding atrial fibrillation (AF), the characterization of the electrical propagation and the underlying substrate favoring AF is inherently more challenging than for ventricular arrhythmias, due to the progressive and heterogeneous nature of the disease and its manifestation, the small volume and wall thickness of the atria, and the relatively large role of microstructural abnormalities in AF. At the same time, ECGI has the advantage over other mapping technologies of allowing a global characterization of atrial electrical activity at every atrial beat and non-invasively. However, since ECGI is time-consuming and costly and the use of electrical mapping to guide AF ablation is still not fully established, the clinical value of ECGI for AF is still under assessment. Nonetheless, AF is known to be the manifestation of a complex interaction between electrical and structural abnormalities and therefore, true electro-anatomical-structural imaging may elucidate important key factors of AF development, progression, and treatment. Therefore, it is paramount to identify which clinical questions could be successfully addressed by ECGI when it comes to AF characterization and treatment, and which questions may be beyond its technical limitations. In this manuscript we review the questions that researchers have tried to address on the use of ECGI for AF characterization and treatment guidance (for example, localization of AF triggers and sustaining mechanisms), and we discuss the technological requirements and validation. We address experimental and clinical results, limitations, and future challenges for fruitful application of ECGI for AF understanding and management. We pay attention to existing techniques and clinical application, to computer models and (animal or human) experiments, to challenges of methodological and clinical validation. The overall objective of the study is to provide a consensus on valuable directions that ECGI research may take to provide future improvements in AF characterization and treatment guidance.

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

OBJECTIVE

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

APPROACH

Continuous wavelet transform along 40 scales in the pseudo-frequency range of 3-30 Hz is performed in each BSPM signal using a Gaussian mother wavelet. 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 DF _BSPM (HDF _BSPM ) 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 [Formula: see text] 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.

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

PURPOSE

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

METHODS

19 simulations of 567-lead BSPMs were used to obtain dominant frequency (DF) maps and estimate the atrial driving frequencies using the highest DF (HDF). Regions with |DF-HDF|≤1Hz were segmented and characterized (size, area); the spatial distribution of the differences |DF-atrialHDFestimate| was qualitatively analyzed. Phase singularity points (SPs) were detected on maps generated with Hilbert transform after band-pass filtering around the HDF (±1Hz). Connected SPs along time (filaments) and their histogram (heatmaps) were used for rotational activity characterization (duration, spatiotemporal stability). Results were reproduced in clinical layouts (252 to 12 leads) and with different rotations and translations of the atria within the torso, and compared with the original 567-lead outcomes using structural similarity index (SSIM) between maps, sensitivity and precision in SP detection and direct feature comparison. Random forest and least-square based algorithms were used to classify the arrhythmias and their mechanisms' location, respectively, based on the obtained features.

RESULTS

Frequency and phase analyses revealed distinct behavior between arrhythmias. AT and AFL presented uniform DF maps with low variance, while AF maps were more heterogeneous. Lower differences from the atrial HDF regions correlated with the driver location. Rotational activity was most stable in AFL, followed by AT and AF. Features were robust to lower spatial resolution layouts and modifications in the atrial geometry; DF and heatmaps presented decreasing SSIM along the layouts. The classification of the arrhythmias and their mechanisms' location achieved balanced accuracy of 72.0% and 73.9%, respectively.

CONCLUSION

Non-invasive characterization of AT, AFL and AF based on realistic models highlights intrinsic differences between the arrhythmias, enhancing the BSPM utility as an auxiliary clinical tool.