Electrocardiographic Spectral Features for Long-Term Outcome Prognosis of Atrial Fibrillation Catheter Ablation

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 Novel Signal Restoration Method of Noisy Photoplethysmograms for Uninterrupted Health Monitoring

Health-tracking from photoplethysmography (PPG) signals is significantly hindered by motion artifacts (MAs). Although many algorithms exist to detect MAs, the corrupted signal often remains unexploited. This work introduces a novel method able to reconstruct noisy PPGs and facilitate uninterrupted health monitoring. The algorithm starts with spectral-based MA detection, followed by signal reconstruction by using the morphological and heart-rate variability information from the clean segments adjacent to noise. The algorithm was tested on (a) 30 noisy PPGs of a maximum 20 s noise duration and (b) 28 originally clean PPGs, after noise addition (2-120 s) (1) with and (2) without cancellation of the corresponding clean segment. Sampling frequency was 250 Hz after resampling. Noise detection was evaluated by means of accuracy, sensitivity, and specificity. For the evaluation of signal reconstruction, the heart-rate (HR) was compared via Pearson correlation (PC) and absolute error (a) between ECGs and reconstructed PPGs and (b) between original and reconstructed PPGs. Bland-Altman (BA) analysis for the differences in HR estimation on original and reconstructed segments of (b) was also performed. Noise detection accuracy was 90.91% for (a) and 99.38-100% for (b). For the PPG reconstruction, HR showed 99.31% correlation in (a) and >90% for all noise lengths in (b). Mean absolute error was 1.59 bpm for (a) and 1.26-1.82 bpm for (b). BA analysis indicated that, in most cases, 90% or more of the recordings fall within the confidence interval, regardless of the noise length. Optimal performance is achieved even for signals of noise up to 2 min, allowing for the utilization and further analysis of recordings that would otherwise be discarded. Thereby, the algorithm can be implemented in monitoring devices, assisting in uninterrupted health-tracking.

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.

Surface ECG-based complexity parameters for predicting outcomes of catheter ablation for nonparoxysmal atrial fibrillation: efficacy of fibrillatory wave amplitude

Catheter ablation (CA) is a well-established therapy for rhythm control in atrial fibrillation (AF). However, CA outcomes for persistent AF remain unsatisfactory because of the high recurrence rate despite time-consuming efforts and the latest ablation technology. Therefore, the selection of good responders to CA is necessary. Surface electrocardiography (sECG)-based complexity parameters were tested for the predictive ability of procedural termination failure during CA and late recurrence of atrial arrhythmias (AA) after CA. A total of 130 patients with nonparoxysmal AF who underwent CA for the first time were investigated. A 10-second sECG of 4 leads (leads I, II, V1, and V6) was analyzed to compute the fibrillatory wave amplitude (FWA), dominant frequency (DF), spectral entropy (SE), organization index (OI), and sample entropy (SampEn). The study endpoints were procedural termination failure during CA and late (≥1 year) AA recurrence after CA. In the multivariate analysis, FWA in lead V1 and DF in lead I were independent predictors of successful AF termination during CA (P <.05). The optimal cut-off values for FWA in lead V1 and DF in lead I were 60.38 μV (area under the curve [AUC], 0.672; P = .001) and 5.7 Hz (AUC, 0.630; P = .016), respectively. The combination of FWA of lead V1 and DF of lead I had a more powerful odds ratio for predicting procedural termination failure (OR, 8.542; 95% CI, 2.938-28.834; P < .001). FWA in lead V1 was the only independent predictor of late recurrence after CA. The cut-off value is 65.73 μV which was 0.634 of the AUC (P = .009). These sECG parameters, FWA in lead V1 and DF in lead I, predicted AF termination by CA in patients with nonparoxysmal AF. In particular, FWA in lead V1 was an independent predictor of late recurrence of AA after CA.

Atrial Remodeling in Atrial Fibrillation. Comorbidities and Markers of Disease Progression Predict Catheter Ablation Outcome

Atrial fibrillation is the most common supraventricular arrhythmia affecting an increasing proportion of the population in which mainstream therapy, i.e. catheter ablation, provides freedom from arrhythmia in only a limited number of patients. Understanding the mechanism is key in order to find more effective therapies and to improve patient selection. In this review, the structural and electrophysiological changes of the atrial musculature that constitute atrial remodeling in atrial fibrillaton and how risk factors and markers of disease progression can predict catheter ablation outcome will be discussed in detail.

Characterization of Changes in P-Wave VCG Loops Following Pulmonary-Vein Isolation

Atrial fibrillation is the most common type of cardiac arrhythmia in clinical practice. Currently, catheter ablation for pulmonary-vein isolation is a well-established treatment for maintaining sinus rhythm when antiarrhythmic drugs do not succeed. Unfortunately, arrhythmia recurrence after catheter ablation remains common, with estimated rates of up to 45%. A better understanding of factors leading to atrial-fibrillation recurrence is needed. Hence, the aim of this study is to characterize changes in the atrial propagation pattern following pulmonary-vein isolation, and investigate the relation between such characteristics and atrial-fibrillation recurrence. Fifty patients with paroxysmal atrial fibrillation who had undergone catheter ablation were included in this study. Time-segment and vectorcardiogram-loop-morphology analyses were applied to characterize P waves extracted from 1 min long 12-lead electrocardiogram segments before and after the procedure, respectively. Results showed that P-wave vectorcardiogram loops were significantly less round and more planar, P waves and PR intervals were significantly shorter, and heart rate was significantly higher after the procedure. Differences were larger for patients who did not have arrhythmia recurrences at 2 years of follow-up; for these patients, the pre- and postprocedure P waves could be identified with 84% accuracy.

A novel IRBF-RVM model for diagnosis of atrial fibrillation

BACKGROUND AND OBJECTIVE

Atrial fibrillation (AF) is one of the common cardiovascular diseases, and electrocardiography (ECG) is a key indicator for the detection and diagnosis of AF and other heart diseases. In this study, an improved machine learning method is proposed for rapid modeling and accurate diagnosis of AF.

METHODS

This paper presents a novel IRBF-RVM model that combines the integrated radial basis function (IRBF) and relevance vector machine (RVM), which is utilized for the diagnosis of AF. The synchronous 12-lead ECG signals are collected from the human body surface so as to fully reflect the electrical activity of the whole heart. RR intervals of the QRS-waves in ECG signals are obtained by means of the classical Pan-Tompkins algorithm. The RR-features extracted from RR intervals are adopted as the diagnostic features for AF patients. In addition, the conventional RBF-RVM model, support vector machine (SVM) and other machine learning methods are also investigated for the diagnosis of AF so as to reflect the advantage of the proposed IRBF-RVM model. The open MIT-BIH arrhythmia database (MITDB) is also used to evaluate the predictive performance of these state-of-the-art methods.

RESULTS

Altogether 1056 AF patients and 904 healthy people are participated in this study and validate the effectiveness of each channel of the 12-lead ECG signals. Experimental results show that the classification rate of IRBF-RVM can reach up to 98.16% by recurring to Channel II of the 12-lead ECG signals.

CONCLUSIONS

IRBF-RVM absorbs the advantages of IRBF, which makes the kernel parameter of IRBF-RVM have a much larger selectable region than RBF-RVM. In addition, RVM has faster modeling and recognition speed in comparison with SVM. This work lays the foundation for the application of RVM to accurate diagnosis of AF.

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.

Surface ECG and intracardiac spectral measures predict atrial fibrillation recurrence after catheter ablation

INTRODUCTION

Outcome of patients undergoing catheter ablation of atrial fibrillation (AF) varies widely. We sought to investigate whether parameters derived from the spectral analysis of surface ECG and intracardiac AF electrograms can predict outcome in patients referred for pulmonary vein isolation (PVI).

METHODS

We performed spectral analysis on the surface ECG and intracardiac electrograms from patients referred for AF ablation. After filtering and QRST subtraction, we measured the dominant frequency (DF), regularity index (RI) and the organizational index (OI) of fibrillatory electrograms and determined their value for predicting AF recurrence after ablation. A subjective, blinded prediction based on the surface ECG was also performed.

RESULTS

We analyzed data from 153 PVI procedures in 140 patients (67.1% with persistent or longstanding AF). In a multivariable model, DF in the right atrium (RA) and distal coronary sinus (CSd)-to-RA DF gradient predicted AF recurrence (OR, 3.52, P = 0.023 and OR, 0.2, P = 0.034, respectively). DF in RA and CSd to RA DF gradient had a good predictive value for PVI outcome (area under the curve [AUC] of 0.73, P = 0.007 and 0.74, P = 0.007, respectively). These performed better than the subjective predictions of experienced electrophysiologists ( P = 0.2).

CONCLUSIONS

Higher RA DF, lower CSd to RA DF gradient predicted recurrence after AF ablation. These spectral measures suggest a more remodeled atrial substrate and may provide simple tools for risk stratification or predict the need for additional substrate modification in patients referred for AF ablation.

Clinical, electrophysiological and imaging predictors of atrial fibrillation ablation outcome

INTRODUCTION

Significant technological advances for catheter ablation of atrial fibrillation (AF) have occurred over the last decade, with a consequent increase in numbers of patients referred for AF ablation worldwide. Despite this, long-term success rates, particularly in those with persistent AF, remain modest. The patient population presenting for AF ablation are heterogeneous with regard to age, type of AF and presence of associated cardiovascular disease. Improved understanding of factors predicting response to AF ablation may therefore help to improve patient selection for ablation procedures. Areas covered: This review outlines the clinical, electrophysiological and imaging predictors of response to radiofrequency ablation for AF in contemporary practice. Recently developed scoring systems incorporating these parameters are examined, as are factors identified thus far which may predict the outcome of cryoballoon ablation. Expert commentary: Traditional clinical factors associated with ablation outcomes serve as surrogates rather than direct measures of the underlying arrhythmia substrate. An improved understanding of this substrate could improve the prediction of response to radiofrequency ablation. Continued development of methods for characterising the arrhythmia substrate, including atrial cardiac magnetic resonance imaging and invasive voltage mapping, may inform patient risk assessment and help guide selection for catheter ablation on an increasingly individualistic basis.