A novel framework for noninvasive analysis of short-term atrial activity dynamics during persistent atrial fibrillation

ECG-based representation of atrial fibrillation (AF) progression is currently limited. We propose a novel framework for a more sensitive noninvasive characterization of the AF substrate during persistent AF. An atrial activity (AA) recurrence signal is computed from body surface potential map (BSPM) recordings, and a set of characteristic indices is derived from it which captures the short- and long-term recurrent behaviour in the AA patterns. A novel measure of short- and long-term spatial variability of AA propagation is introduced, to provide an interpretation of the above indices, and to test the hypothesis that the variability in the oscillatory content of AA is due mainly to a spatially uncoordinated propagation of the AF waveforms. A simple model of atrial signal dynamics is proposed to confirm this hypothesis, and to investigate a possible influence of the AF substrate on the short-term recurrent behaviour of AA propagation. Results confirm the hypothesis, with the model also revealing the above influence. Once the characteristic indices are normalized to remove this influence, they show to be significantly associated with AF recurrence 4 to 6 weeks after electrical cardioversion. Therefore, the proposed framework improves noninvasive AF substrate characterization in patients with a very similar substrate. Graphical Abstract Schematic representation of the proposed framework for the noninvasive characterization of short-term atrial signal dynamics during persistent AF. The proposed framework shows that the faster the AA is propagating, the more stable its propagation paths are in the short-term (larger values of Speed in the bottom right plot should be interpreted as lower speed of propagation of the corresponding AA propagation patters).

Anti-arrhythmic strategies for atrial fibrillation: The role of computational modeling in discovery, development, and optimization

Atrial fibrillation (AF), the most common cardiac arrhythmia, is associated with increased risk of cerebrovascular stroke, and with several other pathologies, including heart failure. Current therapies for AF are targeted at reducing risk of stroke (anticoagulation) and tachycardia-induced cardiomyopathy (rate or rhythm control). Rate control, typically achieved by atrioventricular nodal blocking drugs, is often insufficient to alleviate symptoms. Rhythm control approaches include antiarrhythmic drugs, electrical cardioversion, and ablation strategies. Here, we offer several examples of how computational modeling can provide a quantitative framework for integrating multiscale data to: (a) gain insight into multiscale mechanisms of AF; (b) identify and test pharmacological and electrical therapy and interventions; and (c) support clinical decisions. We review how modeling approaches have evolved and contributed to the research pipeline and preclinical development and discuss future directions and challenges in the field.

Spectral Analysis of Baseline Electrocardiogram During Atrial Fibrillation Predicts Response to Antiarrhythmic Drug Therapy in Patients With Persistent Atrial Fibrillation

BACKGROUND

We evaluated the ability of spectral analysis of the baseline ECG during atrial fibrillation (AF) to predict the response of persistent AF to antiarrhythmic drug therapy.

METHODS

Patients with persistent AF who were admitted for dofetilide loading were prospectively enrolled in the study. Atrial activity was extracted from the ECG using an Independent Component Analysis method and then subjected to a Modified Periodogram. The regularity index was computed as the ratio of the power in the dominant frequency and all its harmonics to the total power in the spectrum. Patients were followed at 1 month, 3 months and every 3 months thereafter.

RESULTS

Of 28 patients enrolled in the study, 14 (50%) converted acutely to sinus rhythm during the 3-day hospital loading period. The clinical and echocardiographic characteristics of patients with and without acute pharmacologic conversion were similar. The regularity index was significantly higher in those who converted to sinus rhythm compared to those who did not (0.71 ± 0.20 vs. 0.38 ± 0.13, respectively; P < 0.0001). A regularity index ≥0.44 had a 79% sensitivity and 93% specificity to predict acute conversion and was associated with a nearly 5-fold increase in the acute conversion rate (odds ratio = 4.89; 95% confidence interval 1.74-13.75; P = 0.003). The regularity index was the only independent predictor of acute conversion. Neither acute conversion, nor the regularity index predicted sinus rhythm maintenance, after a median follow-up of 10 months.

CONCLUSION

Increased regularity index predicts acute conversion of persistent AF during dofetilide loading, but does not predict long-term sinus rhythm maintenance.

Spectral Analysis of Electrocardiograms in Patients with Inducible Atrial Fibrillation after Catheter Ablation Predicts Sinus Rhythm Maintenance

BACKGROUND

We aimed to develop a novel predictive marker for atrial fibrillation (AF) recurrence in patients with inducible AF after catheter ablation, based on power spectral analysis of baseline and postablation electrocardiograms.

METHODS

Twenty-five patients who had undergone their first AF ablation procedure (pulmonary vein isolation and ganglionated plexi ablation) and had inducible AF after ablation were included. A 30-second interval of AF was chosen for each patient before and after ablation, and a periodogram of the atrial activity was computed. A ratio of the power in the dominant frequency to the power in the remainder of the periodogram (DFR) was calculated.

RESULTS

Eight (32%) patients had recurrent AF at 1 year. The clinical and echocardiographic characteristics of patients with and without recurrence were similar (P > 0.05). After ablation, there was organization of atrial activity, evidenced by an increase in the DFR (0.28 ± 0.22 vs 0.53 ± 0.29; P = 0.001). The percent change in DFR before and after ablation (median [interquartile range]) was significantly higher in patients without AF recurrence (120% [30% to 344%] vs 3% [-27% to 66%]; P = 0.01). Receiver operating curve (ROC) analysis demonstrated that a less than 16% increase in DFR postablation was able to predict recurrence of AF (area under ROC curve = 0.82; P = 0.03) with 75% sensitivity and 94% specificity.

CONCLUSION

AF ablation leads to variable organization of atrial activity. Organization of atrial activity after AF ablation is associated with lower 1-year recurrence rates and may be used intraprocedurally after as a novel end point for AF ablation. Larger prospective studies are warranted.

Entropy at the right atrium as a predictor of atrial fibrillation recurrence outcome after pulmonary vein ablation

Atrial fibrillation (AF) recurrence rates after successful ablation procedures are still high and difficult to predict. This work studies the capability of entropy measured from intracardiac recordings as an indicator for recurrence outcome. Intra-atrial recordings from 31 AF patients were registered previously to an ablation procedure. Four electrodes were located at the right atrium (RA) and four more at the left atrium (LA). Sample entropy measurements were applied to these signals, in order to characterize different non-linear AF dynamics at the RA and LA independently. In a 3 months follow-up, 19 of them remained in sinus rhythm, whereas the other 12 turned back to AF. Entropy values can be associated to a proarrhythmic indicator as they were higher in patients with AF recurrence (1.11±0.15 vs. 0.91±0.13), in persistent patients (1.03±0.19 vs. 0.96±0.15), and at the LA with respect to the RA (1.03±0.23 vs. 0.89±0.15 for paroxysmal AF patients). Furthermore, entropy values at the RA arose as a more reliable predictor for recurrence outcome than at the LA. Results suggest that high entropy values, especially at the RA, are associated with high risk of AF recurrence. These findings show the potential of the proposed method to predict recurrences post-ablation, providing additional insights to the understanding of arrhythmia.

A new look at atrial fibrillation: lessons learned from drugs, pacing, and ablation therapies

Atrial fibrillation (AF) is the most common arrhythmia and among the leading causes of stroke and heart failure in Western populations. Despite the increasing size of clinical trials assessing the efficacy and safety of AF therapies, achieved outcomes have not always matched expectations. Considering that AF is a symptom of many possible underlying diseases, clinical research for this arrhythmia should take into account their respective pathophysiology. Accordingly, the definition of the study populations to be included should rely on the established as well as on the new classifications of AF and take advantage from a differentiated look at the AF-electrocardiogram and from increasingly large spectrum of biomarkers. Such an integrated approach could bring researchers and treating physicians one step closer to the ultimate vision of personalized therapy, which, in this case, means an AF therapy based on refined diagnostic elements in accordance with scientific evidence gathered from clinical trials. By applying clear-cut patient inclusion criteria, future studies will be of smaller size and thus of lower cost. In addition, the findings from such studies will be of greater predictive value at the individual patient level, allowing for pinpointed therapeutic decisions in daily practice.

Low atrial fibrillatory rate is associated with spontaneous conversion of recent-onset atrial fibrillation

AIMS

Atrial fibrillatory rate (AFR) is considered a non-invasive index of atrial remodelling. Low AFR has been associated with favourable outcome of interventions in patients with persistent atrial fibrillation (AF). However, AFR has never been studied in unselected patients with short duration of AF, prone to regain sinus rhythm (SR) spontaneously. The aim of the study was to assess if AFR can predict spontaneous conversion in patients with recent-onset AF.

METHODS AND RESULTS

Files of consecutive patients with AF < 48 h seeking emergency room care during a 12-month period were screened (n = 225). Patients with thyroid illness, acute ischaemic heart disease (IHD) or acute congestive heart failure, significant valvular heart disease, congenital heart disease, history of cardiac surgery or catheter ablation, or on class I/III antiarrhythmics were excluded. Atrial fibrillatory rate was obtained by QRST cancellation and time frequency analysis of electrocardiogram at admission. The study population comprised 148 patients (age 64 ± 13 years, 52 men), of whom 48 converted to SR within 18 h. Those converting spontaneously comprised more women, had a higher prevalence of first-ever AF episode, IHD, and a lower AFR. The multivariate analysis revealed: AFR < 350 fibrillations per minute [odds ratio (OR) 3.7, 95% confidence interval (CI) 1.3-10.5, P = 0.016], IHD (OR 5.7, 95% CI 1.5-22.4, P = 0.012) and first-ever AF episode (OR 4.1, 95% CI 1.3-13.0, P = 0.015) as independent predictors of spontaneous conversion.

CONCLUSION

A low AFR was predictive of spontaneous conversion in patients with recent-onset AF. Along with first-ever AF episode and IHD, AFR can be used in assessing likelihood of spontaneous conversion, if proven in prospective studies.

An echo state neural network for QRST cancellation during atrial fibrillation

A novel method for QRST cancellation during atrial fibrillation (AF) is introduced for use in recordings with two or more leads. The method is based on an echo state neural network which estimates the time-varying, nonlinear transfer function between two leads, one lead with atrial activity and another lead without, for the purpose of canceling ventricular activity. The network has different sets of weights that define the input, hidden, and output layers, of which only the output set is adapted for every new sample to be processed. The performance is evaluated on ECG signals, with simulated f-waves added, by determining the root mean square error between the true f-wave signal and the estimated signal, as well as by evaluating the dominant AF frequency. When compared to average beat subtraction (ABS), being the most widely used method for QRST cancellation, the performance is found to be significantly better with an error reduction factor of 0.24-0.43, depending on f-wave amplitude. The estimates of dominant AF frequency are considerably more accurate for all f-wave amplitudes than the AF estimates based on ABS. The novel method is particularly well suited for implementation in mobile health systems where monitoring of AF during extended time periods is of interest.