Severe and uniform bi-atrial remodeling measured by dominant frequency analysis in persistent atrial fibrillation unresponsive to ablation

The atrial electrocardiogram in patients with persistent atrial fibrillation

BACKGROUND

The electrocardiogram (ECG) in persistent atrial fibrillation (AF) is underused. Signal processing techniques can extract the f-waves from the QRS-T complex to generate the atrial ECG (A-ECG).

OBJECTIVE

This study aimed to explore A-ECG technical and clinical issues.

METHODS

Thirty patients (age, 62.6 ± 9.4 years; 80% male; CHA2DS2-VASc score, 2.2 ± 1.7; ejection fraction, 47.9% ± 10.4%) with persistent AF undergoing ablation were enrolled. ECGs were downloaded for offline analysis. A-ECG was derived by QRS-T subtraction and independent component analysis filtering. A-ECG short-term (DF10s, OI10s) and long-term (DF_Global, OI_Global) spectral features were derived. Optimal recording duration, temporal reproducibility, differences between leads, comparison to intracardiac dominant frequency (DF), acute ablation effect, and A-ECG differences based on ablation outcomes were evaluated.

RESULTS

Time-frequency A-ECG analysis demonstrated dynamic spectral features. Repeated 10-second A-ECG recordings had higher coefficient of variation (7.5%-10.0%) compared with repeated 4- to 5-minute recordings (1.1%-3.4%). Short-term reproducibility of 2 recordings ∼30 minutes apart showed high correlation (r = 0.91-0.99; all P values < .0001). Regional lead analyses showed no significant differences except for the comparison of posterior leads V7-V9 vs V1-V3. A-ECG DF parameters correlated with right atrial DF (r = 0.64-0.72; P < .0001) and not left atrial DF. A-ECG DF declined after ablation (median-DF10s-1min, 5.94 ± 0.63 Hz vs 5.77 ± 0.63 Hz; P = .0003). Patients with successful ablation at 1 year had higher A-ECG DF vs those with recurrences (median-DF10s-5min, 6.04 ± 0.42 Hz vs 5.13 ± 0.93 Hz; P = .003).

CONCLUSION

This study provides the practical basis for further use of the A-ECG as a novel tool for evaluating persistent AF and demonstrates its clinical relevance. Further phenotypic classification of AF based on the A-ECG and assessment of treatment response should be explored.

Evaluation and optimization of novel extraction algorithms for the automatic detection of atrial activations recorded within the pulmonary veins during atrial fibrillation

Background and objective

The automated detection of atrial activations (AAs) recorded from intracardiac electrograms (IEGMs) during atrial fibrillation (AF) is challenging considering their various amplitudes, morphologies and cycle length. Activation time estimation is further complicated by the constant changes in the IEGM active zones in complex and/or fractionated signals. We propose a new method which provides reliable automatic extraction of intracardiac AAs recorded within the pulmonary veins during AF and an accurate estimation of their local activation times.

Methods

First, two recently developed algorithms were evaluated and optimized on 118 recordings of pulmonary vein IEGM taken from 35 patients undergoing ablation of persistent AF. The adaptive mathematical morphology algorithm (AMM) uses an adaptive structuring element to extract AAs based on their morphological features. The relative-energy algorithm (Rel-En) uses short- and long-term energies to enhance and detect the AAs in the IEGM signals. Second, following the AA extraction, the signal amplitude was weighted using statistics of the AA sequences in order to reduce over- and undersensing of the algorithms. The detection capacity of our algorithms was compared with manually annotated activations and with two previously developed algorithms based on the Teager–Kaiser energy operator and the AF cycle length iteration, respectively. Finally, a method based on the barycenter was developed to reduce artificial variations in the activation annotations of complex IEGM signals.

Results

The best detection was achieved using Rel-En, yielding a false negative rate of 0.76% and a false positive rate of only 0.12% (total error rate 0.88%) against expert annotation. The post-processing further reduced the total error rate of the Rel-En algorithm by 70% (yielding to a final total error rate of 0.28%).

Conclusion

The proposed method shows reliable detection and robust temporal annotation of AAs recorded within pulmonary veins in AF. The method has low computational cost and high robustness for automatic detection of AAs, which makes it a suitable approach for online use in a procedural context.

Dynamics of Intraprocedural Dominant Frequency Identifies Ablation Outcome in Persistent Atrial Fibrillation

Background: The role of dominant frequency (DF) in tracking the efficiency of a stepwise catheter ablation (step-CA) in persistent atrial fibrillation (peAF) remains poorly studied. We hypothesized that the DF time-course during step-CA displays divergent patterns between patients in whom a step-CA successfully restores long-term sinus rhythm (SR) and those with recurrence. Methods: This study involved 40 consecutive patients who underwent a step-CA for peAF (sustained duration 19 ± 11 months). Dominant frequency was computed on electrograms recorded from the right and left atrial appendages (RAA; LAA) and the coronary sinus before and during the step-CA synchronously to the 12-lead ECG. Dominant frequency was defined as the highest peak within the power spectrum. Results: Persistent atrial fibrillation was terminated by a step-CA in 28 patients [left-terminated (LT)], whereas 12 patients remaining in AF after ablation [not left-terminated (NLT)] were cardioverted. Over a mean follow-up of 34 ± 14 months, all NLT patients had a recurrence. Among the 28 LT patients, 20 had a recurrence, while 8 remained in SR throughout follow-up. The RAA and V₁ DF had the best predictive values of the procedural failure to terminate AF (area under the curve; AUC 0.84, p < 0.05). A decision tree model including a decrease in LAA DF ≥ 6.61% during the first 20 min following pulmonary vein isolation (PVI) and a baseline RAA DF <5.6 Hz predicted long-term SR restoration with a sensitivity of 83% and a specificity of 93% (p < 0.05). Conclusion: This study found that high baseline DF values are predictive of unfavorable ablation outcomes. The reduction of the LAA DF at early ablation steps following PVI is associated with procedural AF termination and long-term SR maintenance.