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).

Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest.

Optimal defibrillation timing guided by ventricular fibrillation (VF) waveform analysis would contribute to improved survival of out-of-hospital cardiac arrest (OHCA) patients by minimizing myocardial damage caused by futile defibrillation shocks and minimizing interruptions to cardiopulmonary resuscitation. Recently, fuzzy entropy (FuzzyEn) tailored to jointly measure VF amplitude and regularity has been shown to be an efficient defibrillation success predictor. In this study, 734 shocks from 296 OHCA patients (50 survivors) were analyzed, and the embedding dimension () and matching tolerance () for FuzzyEn and sample entropy (SampEn) were adjusted to predict defibrillation success and patient survival.

Gaussian modeling of the P-wave morphology time course applied to anticipate paroxysmal atrial fibrillation.

This paper introduces a new algorithm to quantify the P-wave morphology time course with the aim of anticipating as much as possible the onset of paroxysmal atrial fibrillation (PAF). The method is based on modeling each P-wave with a single Gaussian function and analyzing the extracted parameters variability over time. The selected Gaussian approaches are associated with the amplitude, peak timing, and width of the P-wave.

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).

Dynamic time warping applied to estimate atrial fibrillation temporal organization from the surface electrocardiogram.

Atrial fibrillation (AF) is the most commonly diagnosed arrhythmia in clinical practice. However, the mechanisms responsible for its induction and maintenance still are not fully understood. To this respect, analysis of the electrical activity organization within the atria could play an important role in their proper interpretation.

Radial basis function neural networks applied to efficient QRST cancellation in atrial fibrillation.

The most extended noninvasive technique for medical diagnosis and analysis of atrial fibrillation (AF) relies on the surface elctrocardiogram (ECG). In order to take optimal profit of the ECG in the study of AF, it is mandatory to separate the atrial activity (AA) from other cardioelectric signals. Traditionally, template matching and subtraction (TMS) has been the most widely used technique for single-lead ECGs, whereas multi-lead ECGs have been addressed through statistical signal processing techniques, like independent component analysis.

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.

Classification of paroxysmal and persistent atrial fibrillation in ambulatory ECG recordings.

The problem of classifying short atrial fibrillatory segments in ambulatory ECG recordings as being either paroxysmal or persistent is addressed by investigating a robust approach to signal characterization. The method comprises preprocessing estimation of the dominant atrial frequency for the purpose of controlling the subbands of a filter bank, computation of the relative subband (harmonics) energy, and the subband sample entropy. Using minimum-error-rate classification of different feature vectors, a data set consisting of 24-h ambulatory recordings from 50 subjects with either paroxysmal (26) or persistent (24) atrial fibrillation (AF) was analyzed on a 10-s segment basis; a total of 212,196 segments were classified.

Surface ECG organization time course analysis along onward episodes of paroxysmal atrial fibrillation.

The complete understanding of the mechanisms leading to the initiation, maintenance and self-termination of atrial fibrillation (AF) still is an unsolved challenge for cardiac electrophysiology. Studies in which AF has been induced have shown that electrophysiological and structural remodeling of the atria during the arrhythmia could play an important role in the transition from paroxysmal to persistent AF. However, to this day, the time course of the atrial remodeling along onward episodes of non-induced paroxysmal AF has not been investigated yet.

Application of the phasor transform for automatic delineation of single-lead ECG fiducial points.

This work introduces a new single-lead ECG delineator based on phasor transform. The method is characterized by its robustness, low computational cost and mathematical simplicity. It converts each instantaneous ECG sample into a phasor, and can precisely manage P and T waves, which are of notably lower amplitude than the QRS complex.

The application of nonlinear metrics to assess organization differences in short recordings of paroxysmal and persistent atrial fibrillation.

Atrial fibrillation (AF) is the most common arrhythmia in clinical practice. In the first stages of the disease, AF may terminate spontaneously and it is referred to as paroxysmal AF. The arrhythmia is called persistent AF when external intervention is required to its termination.

Non-invasive atrial fibrillation organization follow-up under successive attempts of electrical cardioversion.

The development of non-invasive tools able to provide valuable information about the effectiveness of a shock in external electrical cardioversion (ECV) is clinically relevant to enhance these protocols in the treatment of atrial fibrillation (AF). The present contribution analyzes the ability of a non-linear regularity index, such as sample entropy (SampEn), to follow-up non-invasively AF organization under successive attempts of ECV and to predict the effectiveness of every single shock. To this respect, the atrial activity (AA) preceding each delivered shock was extracted by using a QRST cancellation method.

Time and frequency series combination for non-invasive regularity analysis of atrial fibrillation.

In this work, we present a new method based on electrocardiogram signal processing to distinguish between the atrial fibrillation (AF) episodes that terminate immediately and those that sustain. The spectrogram of the atrial activity is computed and 12 numerical series of spectral parameters are constructed. The sample entropy (SampEn) of six series are relevant in the characterization of AF termination (p < 0.

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

Electrical cardioversion (ECV) has become a mainstay of therapy for the treatment of persistent atrial fibrillation (AF), which is an arrhythmia that affects up to 1% of the general population. The procedure is initially effective, but it is also characterized by a high rate of AF recurrence. As a consequence, it would be clinically useful to predict normal sinus rhythm (NSR) maintenance after ECV before it is attempted.

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.

Adaptive singular value cancelation of ventricular activity in single-lead atrial fibrillation electrocardiograms.

The proper analysis and characterization of atrial fibrillation (AF) from surface electrocardiographic (ECG) recordings requires to cancel out the ventricular activity (VA), which is composed of the QRS complex and the T wave. Historically, for single-lead ECGs, the averaged beat subtraction (ABS) has been the most widely used technique. However, this method is very sensitive to QRST wave variations and, moreover, high-quality cancelation templates may be difficult to obtain when only short length and single-lead recordings are available.

A non-invasive method to predict electrical cardioversion outcome of persistent atrial fibrillation.

Atrial fibrillation (AF) is the most common cardiac arrhythmia with episodes that may terminate spontaneously in the first stages of the disease. On the other hand, when the arrhythmia is not self-terminating, normal sinus rhythm (NSR) restoration use to be required to reduce the risk of stroke and improve cardiac output. Electrical cardioversion (ECV) is the most effective alternative to revert AF back to sinus rhythm.

Wavelet bidomain sample entropy analysis to predict spontaneous termination of atrial fibrillation.

The ability to predict if an atrial fibrillation (AF) episode terminates spontaneously or not through non-invasive techniques is a challenging problem of great clinical interest. This fact could avoid useless therapeutic interventions and minimize the risks for the patient. The present work introduces a robust AF prediction methodology carried out by estimating, through sample entropy (SampEn), the atrial activity (AA) organization increase prior to AF termination from the surface electrocardiogram (ECG).

Comparative study of methods for ventricular activity cancellation in atrial electrograms of atrial fibrillation.

Atrial fibrillation is a very common cardiovascular disease in clinical practice. One relevant issue to understand its pathophysiological mechanisms is the analysis and interpretation of atrial electrograms (AEG). To study these signals properly, ventricular activity has to be removed from the AEG.

Automatic segmentation and 3D reconstruction of intravascular ultrasound images for a fast preliminar evaluation of vessel pathologies.

Intravascular ultrasound (IVUS) imaging is used along with X-ray coronary angiography to detect vessel pathologies. Manual analysis of IVUS images is slow and time-consuming and it is not feasible for clinical purposes. A semi-automated method is proposed to generate 3D reconstructions from IVUS video sequences, so that a fast diagnose can be easily done, quantifying plaque length and severity as well as plaque volume of the vessels under study.

Analysis of surface electrocardiograms in atrial fibrillation: techniques, research, and clinical applications.

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Neither the natural history of AF nor its response to therapy is sufficiently predictable by clinical and echocardiographic parameters. The purpose of this article is to describe technical aspects of novel electrocardiogram (ECG) analysis techniques and to present research and clinical applications of these methods for characterization of both the fibrillatory process and the ventricular response during AF.

Comparison of atrial signal extraction algorithms in 12-lead ECGs with atrial fibrillation.

Analysis of atrial rhythm is important in the treatment and management of patients with atrial fibrillation. Several algorithms exist for extracting the atrial signal from the electrocardiogram (ECG) in atrial fibrillation, but there are few reports on how well these techniques are able to recover the atrial signal. We assessed and compared three algorithms for extracting the atrial signal from the 12-lead ECG.

Atrial activity extraction for atrial fibrillation analysis using blind source separation.

This contribution addresses the extraction of atrial activity (AA) from real electrocardiogram (ECG) recordings of atrial fibrillation (AF). We show the appropriateness of independent component analysis (ICA) to tackle this biomedical challenge when regarded as a blind source separation (BSS) problem. ICA is a statistical tool able to reconstruct the unobservable independent sources of bioelectric activity which generate, through instantaneous linear mixing, a measurable set of signals.