High-density mapping of the average complex interval helps localizing atrial fibrillation drivers and predicts catheter ablation outcomes.

Background: Persistent Atrial Fibrillation (PersAF) electrogram-based ablation is complex, and appropriate identification of atrial substrate is critical. Little is known regarding the value of the Average Complex Interval (ACI) feature for PersAF ablation.

Objective: Using the evolution of AF complexity by sequentially computing AF dominant frequency (DF) along the ablation procedure, we sought to evaluate the value of ACI for discriminating active drivers (AD) from bystander zones (BZ), for predicting AF termination during ablation, and for predicting AF recurrence during follow-up.

Fibrillatory Wave Amplitude Evolution during Persistent Atrial Fibrillation Ablation: Implications for Atrial Substrate and Fibrillation Complexity Assessment.

. Fibrillatory Wave Amplitude (FWA) has been described as a non-invasive marker of atrial fibrillation (AF) complexity, and it predicts catheter ablation outcome. However, the actual determinants of FWA remain incompletely understood.

Blind Source Separation in Persistent Atrial Fibrillation Electrocardiograms Using Block-Term Tensor Decomposition With Löwner Constraints.

The estimation of the atrial activity (AA) signal from electrocardiogram (ECG) recordings is an important step in the noninvasive analysis of atrial fibrillation (AF), the most common sustained arrhythmia encountered in clinical practice. This problem admits a blind source separation (BSS) formulation that has been recently posed as a tensor factorization, using the Hankel-based block term decomposition (BTD), which is particularly well-suited to the estimation of exponential models like AA during AF. However, persistent forms of AF are characterized by short R-R intervals and very disorganized (or weak) AA, making it difficult to model AA directly and perform its successful extraction through Hankel-BTD.

Data Augmentation for Automatic Identification of Spatiotemporal Dispersion Electrograms in Persistent Atrial Fibrillation Ablation Using Machine Learning.

Catheter ablation is increasingly used to treat atrial fibrillation (AF), the most common sustained cardiac arrhythmia encountered in clinical practice. A recent breakthrough finding in AF ablation consists in identifying ablation sites based on their spatiotemporal dispersion (STD). STD stands for a delay of the cardiac activation observed in intracardiac electrograms (EGMs) across contiguous leads.

Association between circadian Holter ECG changes and sudden cardiac death in patients with Chagas heart disease.

Objective: Chagas disease (ChD) is a parasitic illness, largely spread over South America. ChD usually causes progressive myocardium damage, either by direct parasite action or through autoimmune response. Sudden cardiac death (SCD) is prevalent in the early disease stages, being associated with a high variety of ectopic cardiac beats.

Unsupervised Common Spatial Patterns.

The common spatial pattern (CSP) method is a dimensionality reduction technique widely used in brain-computer interface (BCI) systems. In the two-class CSP problem, training data are linearly projected onto directions maximizing or minimizing the variance ratio between the two classes. The present contribution proves that kurtosis maximization performs CSP in an unsupervised manner, i.

Respiratory Waveform Estimation From Multiple Accelerometers: An Optimal Sensor Number and Placement Analysis.

Respiratory patterns are commonly measured to monitor and diagnose cardiovascular, metabolic, and sleep disorders. Electronic devices such as masks used to record respiratory waveforms usually require medical staff support and obstruct the patients' breathing, causing discomfort. New techniques are being investigated to overcome such limitations.

Time-varying assessment of heart rate variability parameters using respiratory information.

Analysis of heart rate variability (HRV) is commonly used for characterization of autonomic nervous system. As high frequency (HF, known as the respiratory-related) component of HR, overlaps with the typical low frequency (LF) band when the respiratory rate is low, a reference signal for HF variations would help in better discriminating the LF and HF components of HR. The present study proposes a model for time-varying separation of HRV components as well as estimation of HRV parameters using respiration information.

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

With the increasing prevalence of atrial fibrillation (AF), there is a strong clinical interest in determining whether a patient suffering from persistent AF will benefit from catheter ablation (CA) therapy at long term. This work presents several regression models based on noninvasive measures automatically computed from the standard 12-lead electrocardiogram (ECG) such as AF dominant frequency (DF), spectral concentration and spatiotemporal variability (STV). Sixty-two AF patients referred to CA were enrolled in this study.

Non-invasive prediction of catheter ablation outcome in persistent atrial fibrillation by fibrillatory wave amplitude computation in multiple electrocardiogram leads.

Background: Catheter ablation (CA) of persistent atrial fibrillation (AF) is challenging, and reported results are capable of improvement. A better patient selection for the procedure could enhance its success rate while avoiding the risks associated with ablation, especially for patients with low odds of favorable outcome. CA outcome can be predicted non-invasively by atrial fibrillatory wave (f-wave) amplitude, but previous works focused mostly on manual measures in single electrocardiogram (ECG) leads only.

On the Link Between L1-PCA and ICA.

Principal component analysis (PCA) based on L1-norm maximization is an emerging technique that has drawn growing interest in the signal processing and machine learning research communities, especially due to its robustness to outliers. The present work proves that L1-norm PCA can perform independent component analysis (ICA) under the whitening assumption. However, when the source probability distributions fulfil certain conditions, the L1-norm criterion needs to be minimized rather than maximized, which can be accomplished by simple modifications on existing optimal algorithms for L1-PCA.

Atrial signal extraction in atrial fibrillation electrocardiograms using a tensor decomposition approach.

Atrial fibrillation (AF) is the most common cardiac arrhythmia encountered in clinical practice and remains a major challenge in cardiology. The noninvasive analysis of AF usually requires the estimation of the atrial activity (AA) signal in surface electrocardiogram (ECG) recordings. The present contribution puts forward a tensor decomposition approach for noninvasive AA extraction in AF ECG recordings.

Empirical mode decomposition of multiple ECG leads for catheter ablation long-term outcome prediction in persistent atrial fibrillation.

Predictive models arouse increasing interest in clinical practice, not only to improve successful intervention rates but also to extract information of diverse physiological disorders. This is the case of persistent atrial fibrillation (AF), the most common cardiac arrhythmia in adults. Currently, catheter ablation (CA) is one of the preferred therapies to face this disease.

Exploiting intracardiac and surface recording modalities for atrial signal extraction in atrial fibrillation.

During atrial fibrillation (AF), atrial activity (AA) on the surface ECG consists of a pattern of quasi-periodic oscillations (f-waves), which are related to the electrical activation of the atrial substrate. However, to date no direct comparison between the extracted f-wave pattern in surface recordings and specific activation sites within the atria has been carried out. In the present study, one reference intracardiac modality consisting of a bipolar electrogram (EGM) recorded from the left atrial appendage (LAA) is exploited for the first time to guide the extraction of LAA electrical activity from standard 12-lead ECG recordings.

Noninvasive prediction of catheter ablation acute outcome in persistent atrial fibrillation based on logistic regression of ECG fibrillatory wave amplitude and spatio-temporal variability.

Catheter ablation (CA) is increasingly employed to treat persistent atrial fibrillation (AF), yet assessment of procedural AF termination is still a subject of debate in the medical community. This has motivated the development of different criteria based on the standard electrocardiogram (ECG) to characterize ablation immediate effectiveness. However, most of conventional descriptors are merely computed in one ECG lead, thus neglecting significant information provided by the other leads.

Multidimensional characterization of fibrillatory wave amplitude on surface ECG to describe catheter ablation impact on persistent atrial fibrillation.

Radiofrequency catheter ablation (CA) is increasingly employed to treat persistent atrial fibrillation (AF). Nevertheless, its success is not always guaranteed, as selection of patients who could positively respond to this therapy does not rely on systematic criteria and still remains an open issue. Moreover, very little is known about the quantitative effects of this treatment over AF electrophysiology, so their quantitative evaluation is not a trivial task.

Predicting catheter ablation outcome in persistent atrial fibrillation using atrial dominant frequency and related spectral features.

Pre-procedural atrial fibrillation dominant frequency (AFDF) has been reported to play a role as a predictor of catheter ablation (CA) outcome for the treatment of persistent atrial fibrillation (AF). The present study analyzes some spectral features of the atrial signal aimed at evaluating the quality of surface AFDF estimation and discusses their predictive power. First, automated extraction of surface atrial activity (AA) on pre-procedural 12-lead ECG recordings is performed by means of an independent component analysis (ICA) method.

Spatial variability of the 12-lead surface ECG as a tool for noninvasive prediction of catheter ablation outcome in persistent atrial fibrillation.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia encountered in clinical practice. Radiofrequency catheter ablation (CA) is increasingly employed to treat this disease, yet the selection of persistent AF patients who will benefit from this treatment remains a challenging task. Several parameters of the surface electrocardiogram (ECG) have been analyzed in previous works to predict AF termination by CA, such as fibrillatory wave (f-wave) amplitude.

Non-invasive prediction of catheter ablation outcome in persistent atrial fibrillation by exploiting the spatial diversity of surface ECG.

Atrial fibrillation (AF) is the most common cardiac arrhythmia encountered in clinical practice. Radiofrequency catheter ablation (CA) is becoming one of the most widely employed therapies. Yet selection of patients who will benefit from this treatment remains a challenging task.

Topographic imaging of the atrial electrical activity during atrial fibrillation for the analysis of uniform distributions of the surface electrical potentials.

Atrial fibrillation (AF) is a progressive arrhythmia which causes time dependent impairing of the cardiac muscle. This makes that proper therapeutic interventions depend on the degree of AF progression, i.e.

Atrial fibrillation disorganization is reduced by catheter ablation: a standard ECG study.

Selection of candidates to catheter ablation (CA) of long-lasting persistent atrial fibrillation (AF) is challenging, since success is not guaranteed. In this study, we put forward an automated method for noninvasively evaluating the reduction of the complexity of the AF organization following CA. Complexity is meant as the amount of disorganization observed on the ECG, supposed to be directly correlated to the number and interactions of atrial wavefronts.

Noninvasive assessment of the complexity and stationarity of the atrial wavefront patterns during atrial fibrillation.

A novel automated approach to quantitatively evaluate the degree of spatio-temporal organization in the atrial activity (AA) during atrial fibrillation (AF) from surface recordings, obtained from body surface potential maps (BSPM), is presented. AA organization is assessed by measuring the reflection of the spatial complexity and temporal stationarity of the wavefront patterns propagating inside the atria on the surface ECG, by means of principal component analysis (PCA). Complexity and stationarity are quantified through novel parameters describing the structure of the mixing matrices derived by the PCA of the different AA segments across the BSPM recording.

A contrast function for independent component analysis without permutation ambiguity.

This brief deals with the problem of blind source separation (BSS) via independent component analysis (ICA). We prove that a linear combination of the separator output fourth-order marginal cumulants (kurtoses) is a valid contrast function for ICA under prewhitening if the weights have the same sign as the source kurtoses. If, in addition, the source kurtoses are different and so are the linear combination weights, the contrast eliminates the permutation ambiguity typical to ICA, as the estimated sources are sorted at the separator output according to their kurtosis values in the same order as the weights.

Atrial activity estimation from atrial fibrillation ECGs by blind source extraction based on a conditional maximum likelihood approach.

This work presents a spatial filtering method for the estimation of atrial fibrillation activity in the cutaneous electrocardiogram. A linear extraction filter is obtained by maximising the extractor output power on the significant spectral support of the signal of interest. An iterative procedure based on a quasi-maximum likelihood estimator is proposed to jointly estimate the significant spectral support and the extraction filter.