Atrial Activity Organization Research Articles

Spectral Analysis of Electrocardiograms in Patients with Inducible Atrial Fibrillation after Catheter Ablation Predicts Sinus Rhythm Maintenance.

We aimed to develop a novel predictive marker for atrial fibrillation (AF) recurrence in patients with inducible AF after catheter ablation, based on power spectral analysis of baseline and postablation electrocardiograms. Twenty-five patients who had undergone their first AF ablation procedure (pulmonary vein isolation and ganglionated plexi ablation) and had inducible AF after ablation were included. A 30-second interval of AF was chosen for each patient before and after ablation, and a periodogram of the atrial activity was computed. A ratio of the power in the dominant frequency to the power in the remainder of the periodogram (DFR) was calculated. Eight (32%) patients had recurrent AF at 1 year. The clinical and echocardiographic characteristics of patients with and without recurrence were similar (P > 0.05). After ablation, there was organization of atrial activity, evidenced by an increase in the DFR (0.28 ± 0.22 vs 0.53 ± 0.29; P = 0.001). The percent change in DFR before and after ablation (median [interquartile range]) was significantly higher in patients without AF recurrence (120% [30% to 344%] vs 3% [-27% to 66%]; P = 0.01). Receiver operating curve (ROC) analysis demonstrated that a less than 16% increase in DFR postablation was able to predict recurrence of AF (area under ROC curve = 0.82; P = 0.03) with 75% sensitivity and 94% specificity. AF ablation leads to variable organization of atrial activity. Organization of atrial activity after AF ablation is associated with lower 1-year recurrence rates and may be used intraprocedurally after as a novel end point for AF ablation. Larger prospective studies are warranted.

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

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

Surface ECG organization analysis to predict paroxysmal atrial fibrillation termination

The aim of this work is to predict non-invasively if an AF episode terminates spontaneously or not by analyzing the increase of atrial activity organization prior to paroxysmal atrial fibrillation (PAF) termination. Sample entropy was selected as non-linear organization index. Synthetic PAF signals were used to evaluate the notable impact of noise in AA organization estimation. Three strategies to reduce noise, ventricular residues and enhance the atrial activity main features were proposed. The best prediction results were obtained through main atrial wave (MAW) organization estimation. The MAW can be considered as the fundamental waveform associated to the AA. The 92% of the terminating and non-terminating analyzed PAF episodes were correctly classified. Thereby, it can be concluded that the MAW non-linear analysis from the surface ECG is a reliable and useful tool to predict spontaneous PAF termination.

Non-invasive organization variation assessment in the onset and termination of paroxysmal atrial fibrillation

Atrial Fibrillation (AF) is the most common supraventricular tachyarrhythmia. Recently, it has been suggested that AF is partially organized on its onset and termination, thus being more suitable for antiarrhythmia and to avoid unnecessary therapy. Although several invasive and non-invasive AF organization estimators have been proposed, the organization time course in the first and last minutes of AF has not been quantified yet. The aim of this work is to study non-invasively the organization variation within the first and last minutes of paroxysmal AF. The organization was evaluated making use of sample entropy, which can robustly estimate electrical atrial activity organization from surface ECG recordings. This work proves an organization decrease in the first minutes of AF onset and an increase within the last minute before spontaneous AF termination. These results are in agreement with the conclusions reported by other authors who made use of invasive recordings.

Non-Invasive Characterization of Atrial Activity Immediately Prior to Termination of Paroxysmal Atrial Fibrillation

Atrial activity characterization with a high degree of detail can be reached by analyzing invasive recordings. Nevertheless, electrocardiographic signal processing tools can nowadays obtain equivalent information in some cases. The aim of this work is to study the electrocardiographic alterations produced during the last instants prior to spontaneous AF termination. Fifty patients in paroxysmal AF with an episode lasting more than two hours were selected. The last minute prior to spontaneous AF termination (group S) and the central minute (group N) from each episode were analyzed. Ventricular response, f waves morphology, and atrial activity spectral and organization were studied through the use of signal processing tools. RR intervals variability was a discriminative parameter (group S 630+/-200 ms vs. group N 740+/-100 ms; P=.034), although a low specificity was obtained (54.2%). Significative differences between both groups were not reported by the f waves morphological study. Group S presented a significative low dominant atrial frequency mean value than group N (group S 5.010+/-0.671 Hz vs. group N 6.514+/-0.804 Hz, P=4.55x10(-9)). Finally, group N showed a considerable low atrial activity organization degree than group S (group S 0.071+/-0.012 vs. group N 0.103+/-0.013, P=6.73x10(-12)). The analysis of the electrocardiographic interval prior to spontaneous AF termination has revealed an atrial activity organization process, which cannot be observed by visual inspection of f waves morphology, but can be successfully quantified through signal processing.

Understanding Atrial Fibrillation: The Signal Processing Contribution, Part II

The book presents recent advances in signal processing techniques for modeling, analysis, and understanding of the heart's electrical activity during atrial fibrillation. This arrhythmia is the most commonly encountered in clinical practice and its complex and metamorphic nature represents a challenging problem for clinicians, engineers, and scientists. Research on atrial fibrillation has stimulated the development of a wide range of signal processing tools to better understand the mechanisms ruling its initiation, maintenance, and termination. This book provides undergraduate and graduate students, as well as researchers and practicing engineers, with an overview of techniques, including time domain techniques for atrial wave extraction, time-frequency analysis for exploring wave dynamics, and nonlinear techniques to characterize the ventricular response and the organization of atrial activity. The book includes an introductory chapter about atrial fibrillation and its mechanisms, treatment, and management. The successive chapters are dedicated to the analysis of atrial signals recorded on the body surface and to the quantification of ventricular response. The rest of the book explores techniques to characterize endo- and epicardial recordings and to model atrial conduction. Under the appearance of being a monothematic book on atrial fibrillation, the reader will not only recognize common problems of biomedical signal processing but also discover that analysis of atrial fibrillation is a unique challenge for developing and testing novel signal processing tools.

Understanding Atrial Fibrillation: The Signal Processing Contribution, Part I

The book presents recent advances in signal processing techniques for modeling, analysis, and understanding of the heart's electrical activity during atrial fibrillation. This arrhythmia is the most commonly encountered in clinical practice and its complex and metamorphic nature represents a challenging problem for clinicians, engineers, and scientists. Research on atrial fibrillation has stimulated the development of a wide range of signal processing tools to better understand the mechanisms ruling its initiation, maintenance, and termination. This book provides undergraduate and graduate students, as well as researchers and practicing engineers, with an overview of techniques, including time domain techniques for atrial wave extraction, time-frequency analysis for exploring wave dynamics, and nonlinear techniques to characterize the ventricular response and the organization of atrial activity. The book includes an introductory chapter about atrial fibrillation and its mechanisms, treatment, and management. The successive chapters are dedicated to the analysis of atrial signals recorded on the body surface and to the quantification of ventricular response. The rest of the book explores techniques to characterize endo- and epicardial recordings and to model atrial conduction. Under the appearance of being a monothematic book on atrial fibrillation, the reader will not only recognize common problems of biomedical signal processing but also discover that analysis of atrial fibrillation is a unique challenge for developing and testing novel signal processing tools.

Electrophysiological Differences of the Spontaneous Onset of Paroxysmal and Persistent Atrial Fibrillation

Information about the spatiotemporal organization of atrial activity at the onset of atrial fibrillation (AF) is still limited. AF mapping was performed in 30 patients with AF (mean age 53 +/- 9 years, 26 males) by deploying a noncontact mapping balloon in the left atrium (LA). Twenty-four patients had paroxysmal AF and six patients had persistent AF. Three types of AF episodes were analyzed: nonsustained AF (lasting <or= 30 seconds), sustained AF (lasting > 30 seconds, with spontaneous conversion or requiring internal cardioversion and subsequent stable sinus rhythm), and persistent AF episodes (stable sinus rhythm lasting <or= 1 minute after cardioversion). A total of 101 spontaneous AF onset episodes were analyzed. Analysis of AF onset showed that there was a progressive shortening of the initial cycle lengths from nonsustained episodes to sustained episodes and to persistent AF episodes. There was an earlier and more rapid reduction in the cycle lengths from persistent episodes to sustained episodes and to nonsustained episodes of AF (P < 0.05 for persistent vs sustained and for sustained vs nonsustained episodes). The development of multiwavelet activity and disorganization of conduction occurred earlier in persistent and sustained episodes than in nonsustained AF episodes. LA size was greater in patients with persistent AF episodes compared with patients with sustained or nonsustained AF episodes. Electrophysiological events that develop at the onset of AF seem to be different in different types of AF. A more rapid degeneration into the fibrillatory activity was observed in persistent and sustained AF than in nonsustained AF episodes.

Predicting spontaneous termination of atrial fibrillation using the surface ECG

By recognizing and characterizing conditions under which atrial fibrillation (AF) is likely to terminate spontaneously or be sustained, improved treatment of sustained AF may result and unnecessary treatment of self-terminating AF avoided. Time–frequency measures that characterize AF, such as fibrillatory frequency, amplitude, and waveform shape (exponential decay), are extracted from the residual ECG following QRST cancellation. Three complexity measures are also studied, characterizing the degree of organization of atrial activity. All measures are analysed using a training set, consisting of 20 recordings of AF with known termination properties, and a test set of 30 recordings. Spontaneous termination was best predicted by a low and stable fibrillatory frequency and a low exponential decay. Using these predictors, 90% of the test set was correctly classified into terminating and sustained AF. Neither fibrillation amplitude nor the complexity measures differed significantly between the two sets.

Temporal organization of atrial activity and irregular ventricular rhythm during spontaneous atrial fibrillation: an in vivo study in the horse.

Atrial fibrillation (AF) is common in healthy horses. We studied the temporal organization of AF to test the hypothesis that the arrhythmia is governed by a high degree of periodicity and therefore is not random in the horse. Further, we surmised that concealed conduction of AF impulses in the AV node results in an inverse relationship between AF frequency and ventricular frequency. Fast Fourier transform (FFT) analysis of atrial activity was done on signal-averaged ECGs (n = 11) and atrial electrograms (n = 3) of horses with AF at control (C), after quinidine sulfate (22 mg/kg by mouth every 2 hours) at 50% time to conversion (T50), and immediately before conversion (T90) to sinus rhythm. FFT always revealed a single dominant frequency peak. The mean dominant frequency decreased until conversion (C = 6.84 +/- 0.85 Hz, T50 = 4.87 +/- 1.5 Hz, T90 = 3.41 +/- 1.18 Hz; P < 0.001). Mean AA intervals (n = 500) gradually increased after quinidine. Mean RR intervals (n = 500), standard deviation of the mean (SDM), Poincaré plots, and serial autocorrelograms (SACs) of 500 RR intervals were measured at C and T90 to determine the ventricular response to AF and quinidine-induced changes in the variability of the ventricular response. Mean RR interval and SDM were reduced after quinidine (C = 1431 +/- 266 msec and 695 +/- 23 msec; T90 = 974 +/- 116 msec and 273 +/- 158 msec, respectively; P < 0.01). Poincaré plots and SAC at C and at T90 revealed a significant correlation of consecutive RR intervals typical of a system with a deterministic behavior. At T90, the variability of RR intervals was reduced and the overall periodicity of RR intervals was increased after quinidine administration. In the horse, AF is a complex arrhythmia characterized by a high degree of underlying periodicity. The inverse AA-to-RR interval relationship and reduced variability of RR intervals after quinidine suggest that the ventricular response during AF results from rate-dependent concealment of AF wavelets bombarding the AV node, which nevertheless results in a significant degree of short-term predictability of beat-to-beat changes in RR intervals.

Organization of atrial activity during atrial fibrillation

In this paper, I respond to comments by Henggeler, Schoenwald, Borduin, and Swenson [Henngeler, S. W., Schoenwald, S. K., Borduin, C. M., & Swenson, C. C. (this issue). The Littell paper: Methodological critique and meta-analysis as Trojan horse. Children and Youth Services Review, doi: 10.1016/j.childyouth.2005.07.001] on my recent article, “Lessons from a systematic review of Multisystemic Therapy”. I identify factual and logical errors in their response, show how relevant research has been misinterpreted and misrepresented, and suggest constructive new directions for Multisystemic Therapy and the evidence-based practice movement.