Time and frequency recurrence analysis of persistent atrial fibrillation after electrical cardioversion

Novel Entropy-Based Metrics for Long-Term Atrial Fibrillation Recurrence Prediction Following Surgical Ablation: Insights from Preoperative Electrocardiographic Analysis

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia often treated concomitantly with other cardiac interventions through the Cox-Maze procedure. This highly invasive intervention is still linked to a long-term recurrence rate of approximately 35% in permanent AF patients. The aim of this study is to preoperatively predict long-term AF recurrence post-surgery through the analysis of atrial activity (AA) organization from non-invasive electrocardiographic (ECG) recordings. A dataset comprising ECGs from 53 patients with permanent AF who had undergone Cox-Maze concomitant surgery was analyzed. The AA was extracted from the lead V1 of these recordings and then characterized using novel predictors, such as the mean and standard deviation of the relative wavelet energy (RWEm and RWEs) across different scales, and an entropy-based metric that computes the stationary wavelet entropy variability (SWEnV). The individual predictors exhibited limited predictive capabilities to anticipate the outcome of the procedure, with the SWEnV yielding a classification accuracy (Acc) of 68.07%. However, the assessment of the RWEs for the seventh scale (RWEs7), which encompassed frequencies associated with the AA, stood out as the most promising individual predictor, with sensitivity (Se) and specificity (Sp) values of 80.83% and 67.09%, respectively, and an Acc of almost 75%. Diverse multivariate decision tree-based models were constructed for prediction, giving priority to simplicity in the interpretation of the forecasting methodology. In fact, the combination of the SWEnV and RWEs7 consistently outperformed the individual predictors and excelled in predicting post-surgery outcomes one year after the Cox-Maze procedure, with Se, Sp, and Acc values of approximately 80%, thus surpassing the results of previous studies based on anatomical predictors associated with atrial function or clinical data. These findings emphasize the crucial role of preoperative patient-specific ECG signal analysis in tailoring post-surgical care, enhancing clinical decision making, and improving long-term clinical outcomes.

Multi-scale Entropy Evaluates the Proarrhythmic Condition of Persistent Atrial Fibrillation Patients Predicting Early Failure of Electrical Cardioversion

Atrial fibrillation (AF) is nowadays the most common cardiac arrhythmia, being associated with an increase in cardiovascular mortality and morbidity. When AF lasts for more than seven days, it is classified as persistent AF and external interventions are required for its termination. A well-established alternative for that purpose is electrical cardioversion (ECV). While ECV is able to initially restore sinus rhythm (SR) in more than 90% of patients, rates of AF recurrence as high as 20-30% have been found after only a few weeks of follow-up. Hence, new methods for evaluating the proarrhythmic condition of a patient before the intervention can serve as efficient predictors about the high risk of early failure of ECV, thus facilitating optimal management of AF patients. Among the wide variety of predictors that have been proposed to date, those based on estimating organization of the fibrillatory (f-) waves from the surface electrocardiogram (ECG) have reported very promising results. However, the existing methods are based on traditional entropy measures, which only assess a single time scale and often are unable to fully characterize the dynamics generated by highly complex systems, such as the heart during AF. The present work then explores whether a multi-scale entropy (MSE) analysis of the f-waves may provide early prediction of AF recurrence after ECV. In addition to the common MSE, two improved versions have also been analyzed, composite MSE (CMSE) and refined MSE (RMSE). When analyzing 70 patients under ECV, of which 31 maintained SR and 39 relapsed to AF after a four week follow-up, the three methods provided similar performance. However, RMSE reported a slightly better discriminant ability of 86%, thus improving the other multi-scale-based outcomes by 3-9% and other previously proposed predictors of ECV by 15-30%. This outcome suggests that investigation of dynamics at large time scales yields novel insights about the underlying complex processes generating f-waves, which could provide individual proarrhythmic condition estimation, thus improving preoperative predictions of ECV early failure.

Surface ECG f Wave Analysis at Initial Onset of Paroxysmal and Persistent Atrial Fibrillation

INTRODUCTION

Dominant frequency (DF) analysis of electrocardiograms (ECGs) from patients with paroxysmal (PAF) and persistent (PeAF) atrial fibrillation has identified higher DFs in PeAF. We therefore hypothesized that among patients initially presenting to the emergency department (ED) with new onset AF, surface ECG features could differentiate PeAF from PAF.

METHODS AND RESULTS

Initial 12-lead ECGs from patients presenting to the ED with a first episode of symptomatic AF were analyzed. Following QRS-T subtraction, fast Fourier transform (FFT) analysis of the AF fibrillatory waves was performed to measure DF and organization index (OI). Median DF of all leads and the DF in the lead with maximum OI were determined. Maximum f wave amplitude and vector magnitudes were measured. One hundred sixty-one patients (age 59 ± 16 years, 68% men) were included in this analysis, of whom 96 (58%) spontaneously converted to sinus rhythm within 7 days (PAF group). The remaining 65 patients underwent cardioversion or remained in AF (PeAF group). ECG features (DF, OI, f wave amplitude, and vector magnitude) did not differ among PAF and PeAF patients.

CONCLUSIONS

ECG features (DF, OI, amplitude, vector magnitude) do not differ among patients with PAF versus PeAF when the ECGs are obtained at the initial onset of symptoms. Thus, prior data showing higher DF in PeAF likely reflect electrophysiologic remodeling rather than a marker for any specific type of AF or extent of underlying substrate.

Intracardiac impedance response during acute AF internal cardioversion using novel rectilinear and capacitor-discharge waveforms

Intracardiac impedance (ICI) is a major determinant of success during internal cardioversion of atrial fibrillation (AF). However, there have been few studies that have examined the dynamic behaviour of atrial impedance during internal cardioversion in relation to clinical outcome. In this study, voltage and current waveforms captured during internal cardioversion of acute AF in ovine models using novel radiofrequency (RF) generated low-tilt rectilinear and conventional capacitor-discharge based shock waveforms were retrospectively analysed using a digital signal processing algorithm to investigate the dynamic behaviour of atrial impedance during cardioversion. The algorithm was specifically designed to facilitate the simultaneous analysis of multiple impedance parameters, including: mean intracardiac impedance (Z M), intracardiac impedance variance (ICIV) and impedance amplitude spectrum area (IAMSA) for each cardioversion event. A significant reduction in ICI was observed when comparing two successive shocks of increasing energy where cardioversion outcome was successful. In addition, ICIV and IAMSA variables were found to inversely correlate to the magnitude of energy delivered; with a stronger correlation found to the former parameter. In conclusion, ICIV and IAMSA have been evidenced as two key dynamic intracardiac impedance variables that may prove useful in better understanding of the cardioversion process and that could potentially act as prognostic markers with respect to clinical outcome.

Preoperative study of the surface ECG for the prognosis of atrial fibrillation maze surgery outcome at discharge

The Cox-maze surgery is an effective procedure for terminating atrial fibrillation (AF) in patients requiring open-heart surgery associated with another heart disease. After the intervention, regardless of the patient's rhythm, all are treated with oral anticoagulants and antiarrhythmic drugs prior to discharge. Furthermore, patients maintaining AF before discharge could also be treated with electrical cardioversion (ECV). In view of this, a preoperative prognosis of the patient's rhythm at discharge would be helpful for optimizing drug therapy planning as well as for advancing ECV therapy. This work analyzes 30 preoperative electrocardiograms (ECGs) from patients suffering from AF in order to predict the Cox-maze surgery outcome at discharge. Two different characteristics of the AF pattern have been studied. On the one hand, the atrial activity (AA) organization, which provides information about the number of propagating wavelets in the atria, was investigated. AA organization has been successfully used in previous studies related to spontaneous reversion of paroxysmal AF and to the outcome of ECV. To assess organization, the dominant atrial frequency (DAF) and sample entropy (SampEn) have been computed. On the other hand, the second characteristic studied was the fibrillatory wave (f-wave) amplitude, which has been demonstrated to be a valuable indicator of the Cox-maze surgery outcome in previous studies. Moreover, this parameter has been obtained through a new methodology, based on computing the f-wave average power (fWP). Finally, all the computed indices were combined in a decision tree in order to improve prediction capability. Results for the DAF yielded a sensitivity (Se), a specificity (Sp) and an accuracy (Acc) of 61.54%, 82.35% and 73.33%, respectively. For SampEn the values were 69.23%, 76.00% and 73.33%, respectively, and for fWP they were 92.31%, 82.35% and 86.67%, respectively. Finally, the decision tree combining the three parameters analyzed improved the preoperative prognosis of the Cox-maze outcome with values of Se, Sp and Acc of 100%, 82.35% and 90%, respectively. As a consequence, the analysis of parameters related to the f-wave pattern, extracted from the preoperative ECG, has provided a considerable ability to predict the outcome of AF Cox-maze surgery at discharge.

Application of Wavelet Entropy to predict atrial fibrillation progression from the surface ECG

Atrial fibrillation (AF) is the most common supraventricular arrhythmia in clinical practice, thus, being the subject of intensive research both in medicine and engineering. Wavelet Entropy (WE) is a measure of the disorder degree of a specific phenomena in both time and frequency domains, allowing to reveal underlying dynamical processes out of sight for other methods. The present work introduces two different WE applications to the electrocardiogram (ECG) of patients in AF. The first application predicts the spontaneous termination of paroxysmal AF (PAF), whereas the second one deals with the electrical cardioversion (ECV) outcome in persistent AF patients. In both applications, WE was used with the objective of assessing the atrial fibrillatory (f) waves organization. Structural changes into the f waves reflect the atrial activity organization variation, and this fact can be used to predict AF progression. To this respect, results in the prediction of PAF termination regarding sensitivity, specificity, and accuracy were 95.38%, 91.67%, and 93.60%, respectively. On the other hand, for ECV outcome prediction, 85.24% sensitivity, 81.82% specificity, and 84.05% accuracy were obtained. These results turn WE as the highest single predictor of spontaneous PAF termination and ECV outcome, thus being a promising tool to characterize non-invasive AF signals.

Central tendency measure and wavelet transform combined in the non-invasive analysis of atrial fibrillation recordings

Background

Atrial fibrillation (AF) is the most common supraventricular arrhythmia in the clinical practice, being the subject of intensive research.

Methods

The present work introduces two different Wavelet Transform (WT) applications to electrocardiogram (ECG) recordings of patients in AF. The first one predicts spontaneous termination of paroxysmal AF (PAF), whereas the second one deals with the prediction of electrical cardioversion (ECV) outcome in persistent AF patients. In both cases, the central tendency measure (CTM) from the first differences scatter plot was applied to the AF wavelet decomposition. In this way, the wavelet coefficients vector CTM associated to the AF frequency scale was used to assess how atrial fibrillatory (f) waves variability can be related to AF events.

Results

Structural changes into the f waves can be assessed by combining WT and CTM to reflect atrial activity organization variation. This fact can be used to predict organization-related events in AF. To this respect, results in the prediction of PAF termination regarding sensitivity, specificity and accuracy were 100%, 91.67% and 96%, respectively. On the other hand, for ECV outcome prediction, 82.93% sensitivity, 90.91% specificity and 85.71% accuracy were obtained. Hence, CTM has reached the highest diagnostic ability as a single predictor published to date.

Conclusions

Results suggest that CTM can be considered as a promising tool to characterize non-invasive AF signals. In this sense, therapeutic interventions for the treatment of paroxysmal and persistent AF patients could be improved, thus, avoiding useless procedures and minimizing risks.

Noninvasive time and frequency predictors of long-standing atrial fibrillation early recurrence after electrical cardioversion

BACKGROUND

Several clinical factors have been studied to predict atrial fibrillation (AF) recurrence after electrical cardioversion (ECV) with limited predictive value.

METHODS

 A method able to predict robustly long-standing AF early recurrence by characterizing noninvasively the electrical atrial activity (AA) with parameters related to its time course and spectral features is presented. To this respect, 63 patients (20 men and 43 women; mean age 73.4 ± 9.0 years; under antiarrhythmic drug treatment with amiodarone) who were referred for ECV of persistent AF were studied. During a 4-week follow-up, AF recurrence was observed in 41 patients (65.1%).

RESULTS

 RR variability and the studied AA spectral features, including dominant atrial frequency (DAF), its first harmonic and their amplitude, provided poor statistical differences between groups. On the contrary, f waves power (fWP) and Sample Entropy (SampEn) of the AA behaved as very good predictors. Patients who relapsed to AF presented lower fWP (0.036 ± 0.019 vs 0.081 ± 0.029 n.u.(2) , P < 0.001) and higher SampEn (0.107 ± 0.022 vs 0.086 ± 0.033, P < 0.01). Furthermore, fWP presented the highest predictive accuracy of 82.5%, whereas SampEn provided a 79.4%. The remaining features revealed accuracies lower than 70%. A stepwise discriminant analysis (SDA) provided a model based on fWP and SampEn with 90.5% of accuracy.

CONCLUSIONS

 The fWP has proved to predict long-standing AF early recurrence after ECV and can be combined with SampEn to improve its diagnostic ability. Furthermore, a thorough analysis of the results allowed outlining possible associations between these two features and the concomitant status of atrial remodeling.

Validation of surface atrial fibrillation organization indicators through invasive recordings

Studies related to atrial fibrillation (AF) have shown that surface lead V(1) reflects mainly the dominant atrial frequency (DAF) of the right atrium (RA), which has been widely used to analyze this arrhythmia. AF organization and fibrillatory (f) waves amplitude are two recently proposed non-invasive AF markers, which have not been yet validated with invasive recordings. In this work, these two non-invasive metrics have been compared with similar measures recorded from two unipolar atrial electrograms (AEGs). Results obtained from 38 patients showed statistically significant correlations between the values measured from surface and invasive recordings, thus corroborating the usefulness of the aforesaid markers in the non-invasive study of AF. Precisely, for AF organization, the correlation coefficients between surface and both AEGs were R = 0.926 (p < 0.001) and R = 0.932 (p < 0.001), respectively. For f waves amplitude, slightly lower significant relationships were noticed, the correlation coefficients being R = 0.765 (p < 0.001) and R = 0.842 (p < 0.001), respectively. These outcomes together with interesting linear relationships found among the parameters, suggest that AF organization and f waves amplitude can characterize non-invasively the epicardial activity related to AF.