Spectral and spatiotemporal variability ECG parameters linked to catheter ablation outcome in persistent atrial fibrillation

Combination of frequency- and time-domain characteristics of the fibrillatory waves for enhanced prediction of persistent atrial fibrillation recurrence after catheter ablation

Catheter ablation (CA) remains the cornerstone alternative to cardioversion for sinus rhythm (SR) restoration in patients with atrial fibrillation (AF). Unfortunately, despite the last methodological and technological advances, this procedure is not consistently effective in treating persistent AF. Beyond introducing new indices to characterize the fibrillatory waves (f-waves) recorded through the preoperative electrocardiogram (ECG), the aim of this study is to combine frequency- and time-domain features to improve CA outcome prediction and optimize patient selection for the procedure, given the absence of any study that jointly analyzes information from both domains. Precisely, the f-waves of 151 persistent AF patients undergoing their first CA procedure were extracted from standard V1 lead. Novel spectral and amplitude features were derived from these waves and combined through a machine learning algorithm to anticipate the intervention mid-term outcome. The power rate index (φ), which estimates the power of the harmonic content regarding the dominant frequency (DF), yielded the maximum individual discriminant ability of 64% to discern between individuals who experienced a recurrence of AF and those who sustained SR after a 9-month follow-up period. The predictive accuracy was improved up to 78.5% when this parameter φ was merged with the amplitude spectrum area in the DF bandwidth (A M S A L F ) and the normalized amplitude of the f-waves into a prediction model based on an ensemble classifier, built by random undersampling boosting of decision trees. This outcome suggests that the synthesis of both spectral and temporal features of the f-waves before CA might enrich the prognostic knowledge of this therapy for persistent AF patients.

A 3D-CNN with temporal-attention block to predict the recurrence of atrial fibrillation based on body-surface potential mapping signals

Catheter ablation has become an important treatment for atrial fibrillation (AF), but its recurrence rate is still high. The aim of this study was to predict AF recurrence using a three-dimensional (3D) network model based on body-surface potential mapping signals (BSPMs). BSPMs were recorded with a 128-lead vest in 14 persistent AF patients before undergoing catheter ablation (Maze-IV). The torso geometry was acquired and meshed by point cloud technology, and the BSPM was interpolated into the torso geometry by the inverse distance weighted (IDW) method to generate the isopotential map. Experiments show that the isopotential map of BSPMs can reflect the propagation of the electrical wavefronts. The 3D isopotential sequence map was established by combining the spatial–temporal information of the isopotential map; a 3D convolutional neural network (3D-CNN) model with temporal attention was established to predict AF recurrence. Our study proposes a novel attention block that focuses the characteristics of atrial activations to improve sampling accuracy. In our experiment, accuracy (ACC) in the intra-patient evaluation for predicting the recurrence of AF was 99.38%. In the inter-patient evaluation, ACC of 3D-CNN was 81.48%, and the area under the curve (AUC) was 0.88. It can be concluded that the dynamic rendering of multiple isopotential maps can not only comprehensively display the conduction of cardiac electrical activity on the body surface but also successfully predict the recurrence of AF after CA by using 3D isopotential sequence maps.

Preoperative Prediction of Catheter Ablation Outcome in Persistent Atrial Fibrillation Patients through Spectral Organization Analysis of the Surface Fibrillatory Waves

Catheter ablation (CA) is a commonly used treatment for persistent atrial fibrillation (AF). Since its medium/long-term success rate remains limited, preoperative prediction of its outcome is gaining clinical interest to optimally select candidates for the procedure. Among predictors based on the surface electrocardiogram, the dominant frequency (DF) and harmonic exponential decay (γ) of the fibrillatory waves (f-waves) have reported promising but clinically insufficient results. Hence, the main goal of this work was to conduct a broader analysis of the f-wave harmonic spectral structure to improve CA outcome prediction through several entropy-based measures computed on different frequency bands. On a database of 151 persistent AF patients under radio-frequency CA and a follow-up of 9 months, the newly introduced parameters discriminated between patients who relapsed to AF and those who maintained SR at about 70%, which was statistically superior to the DF and approximately similar to γ. They also provided complementary information to γ through different combinations in multivariate models based on lineal discriminant analysis and report classification performance improvement of about 5%. These results suggest that the presence of larger harmonics and a proportionally smaller DF peak is associated with a decreased probability of AF recurrence after CA.

Atrial fibrillation frequency tracking in ambulatory ECG signals: The significance of signal quality assessment

An approach to atrial fibrillation (AF) frequency tracking in long-term ambulatory ECG recordings is presented, comprising f-wave extraction, dominant atrial frequency (DAF) tracking, and signal quality assessment. Since poor signal quality is commonly encountered in ambulatory monitoring, a recently proposed index is employed to assess f-wave signal quality in a database containing 38 patients with permanent AF. The index ensures that DAF outliers, typically associated with poor-quality segments, are excluded from further analysis. 40% of all 5-s signal segments were excluded from the database due to poor quality. The exclusion of DAF outliers significantly reduces the standard deviation of the frequency estimates (p≤0.01), allowing more reliable evaluation of the difference between day- and night-time DAF. The results show that signal quality assessment plays a central role in DAF tracking, and therefore should be employed in ambulatory monitoring.

Changes in f-wave characteristics during cryoballoon catheter ablation

OBJECTIVE

Changes in ECG-derived parameters are studied in atrial fibrillation (AF) patients undergoing cryoballoon catheter ablation.

APPROACH

Parameters characterizing f-wave frequency, morphology by phase dispersion, and amplitude are estimated using a model-based statistical approach. These parameters are studied before, during, and after ablation, as well as for AF type (paroxysmal/persistent). Seventy-seven (49/28 paroxysmal/persistent) AF patients undergoing de novo catheter ablation are included in the study, out of which 31 (16/15 paroxysmal/persistent) were in AF during the whole procedure. A signal quality index (SQI) is used to identify analyzable segments.

MAIN RESULTS

f-wave frequency decreased significantly during ablation (p  =  0.001), in particular after ablation of the inferior right pulmonary vein (p  <  0.05). Frequency and phase dispersion differed significantly between paroxysmal and persistent AF (p  =  0.001 and p  <  0.05, respectively).

SIGNIFICANCE

This study demonstrates that a decrease in f-wave frequency can be distinguished during catheter ablation. The use of an SQI ensures reliable analysis and produces results significantly different from those obtained without an SQI.