Non-invasive Spatial Mapping of Frequencies in Atrial Fibrillation: Correlation With Contact Mapping

Electrocardiographic imaging in the atria

The inverse problem of electrocardiography or electrocardiographic imaging (ECGI) is a technique for reconstructing electrical information about cardiac surfaces from noninvasive or non-contact recordings. ECGI has been used to characterize atrial and ventricular arrhythmias. Although it is a technology with years of progress, its development to characterize atrial arrhythmias is challenging. Complications can arise when trying to describe the atrial mechanisms that lead to abnormal propagation patterns, premature or tachycardic beats, and reentrant arrhythmias. This review addresses the various ECGI methodologies, regularization methods, and post-processing techniques used in the atria, as well as the context in which they are used. The current advantages and limitations of ECGI in the fields of research and clinical diagnosis of atrial arrhythmias are outlined. In addition, areas where ECGI efforts should be concentrated to address the associated unsatisfied needs from the atrial perspective are discussed.

Combined atrial fibrillation ablation and balloon mitral commissurotomy in patients with rheumatic mitral stenosis

INTRODUCTION

Ablation of atrial fibrillation (AF) is usually not considered in patients with rheumatic mitral stenosis (RMS). We analyzed the results of a combined procedure of AF ablation and percutaneous balloon mitral commissurotomy (PBMC).

METHODS

We prospectively included 22 patients with severe RMS to undergo a combined PBMC + AF ablation procedure. Noninvasive mapping of the atria was also performed. A historical sample of propensity-scored matched patients who underwent PBMC alone was used as controls. The primary endpoint was freedom from AF/AT at 1-year. Multivariate analysis evaluated sinus rhythm (SR) predictors.

RESULTS

Successful pulmonary vein isolation and electrocardiographic imaging-based drivers ablation was performed in 20 patients following PBMC. At 1-year, 75% of the patients in the combined group were in SR compared to 40% in the propensity-score matched group (p = 0.004). The composite of AF recurrence, need for mitral surgery and all-cause mortality was also more frequent in the control group (65% vs. 30%; p = 0.005). Catheter ablation (odds ratio [OR] 1.58; 95% confidence interval [CI] [1.17-17.37]; p = 0.04) and AF type (OR 1.46; 95% CI [1.05-82.64]; p < 0.001) were the only independent predictors of SR at 1-year. Noninvasive mapping in the combined group showed that the number of simultaneous rotors (OR 2.10; 95% CI [1.41-10.2]; p = 0.04) was the only independent predictor of AF.

CONCLUSION

A combined procedure of AF ablation and PBMC significantly increased the proportion of patients in sinus rhythm at 1-year. Noninvasive mapping may help to improve AF characterization and guide personalized AF treatment.

Predicting acute termination and non-termination during ablation of human atrial fibrillation using quantitative indices

Background: Termination of atrial fibrillation (AF), the most common arrhythmia in the United States, during catheter ablation is an attractive procedural endpoint, which has been associated with improved long-term outcome in some studies. It is not clear, however, whether it is possible to predict termination using clinical data. We developed and applied three quantitative indices in global multielectrode recordings of AF prior to ablation: average dominant frequency (ADF), spectral power index (SPI), and electrogram quality index (EQI). Methods: In N = 42 persistent AF patients (65 ± 9 years, 14% female) we collected unipolar electrograms from 64-pole baskets (Abbott, CA). We studied N = 17 patients in whom AF terminated during ablation ("Term") and N = 25 in whom it did not ("Non-term"). For each index, we determined its ability to predict ablation by computing receiver operating characteristic (ROC) and calculated the area under the curve (AUC). Results: The ADF did not differ for Term and Non-term patients at 5.28 ± 0.82 Hz and 5.51 ± 0.81 Hz, respectively (p = 0.34). Conversely, the SPI for these two groups was. 0.85 (0.80-0.92) and 0.97 (0.93-0.98) and the EQI was 0.61 (0.58-0.64) and 0.56 (0.55-0.59) (p < 0.0001). The AUC for predicting AF termination for the SPI was 0.85 ([0.68, 0.95] 95% CI), and for the EQI, 0.86 ([0.72, 0.95] 95% CI). Conclusion: Both the EQI and the SPI may provide a useful clinical tool to predict procedural ablation outcome in persistent AF patients. Future studies are required to identify which physiological features of AF are revealed by these indices and hence linked to AF termination or non-termination.

Atrial fibrillation signatures on intracardiac electrograms identified by deep learning

BACKGROUND

Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as 'high rate events'. This may delay or misdirect therapy.

OBJECTIVE

We hypothesized that deep learning (DL) can accurately classify AF from AT by revealing electrogram (EGM) signatures.

METHODS

We studied 86 patients in whom the diagnosis of AF or AT was established at electrophysiological study (25 female, 65 ± 11 years). Custom DL architectures were trained to identify AF using N = 29,340 unipolar and N = 23,760 bipolar EGM segments. We compared DL to traditional classifiers based on rate or regularity. We explained DL using computer models to assess the impact of controlled variations in shape, rate and timing on AF/AT classification in 246,067 EGMs reconstructed from clinical data.

RESULTS

DL identified AF with AUC of 0.97 ± 0.04 (unipolar) and 0.92 ± 0.09 (bipolar). Rule-based classifiers misclassified ∼10-12% of cases. DL classification was explained by regularity in EGM shape (13%) or timing (26%), and rate (60%; p < 0.001), and also by a set of unipolar EGM shapes that classified as AF independent of rate or regularity. Overall, the optimal AF 'fingerprint' comprised these specific EGM shapes, >15% timing variation, <0.48 correlation in beat-to-beat EGM shapes and CL < 190 ms (p < 0.001).

CONCLUSIONS

Deep learning of intracardiac EGMs can identify AF or AT via signatures of rate, regularity in timing or shape, and specific EGM shapes. Future work should examine if these signatures differ between different clinical subpopulations with AF.