Results 51 to 60 of about 397,934 (398)

Characterizing Recurrence Following Hybrid Ablation in Patients With Persistent Atrial Fibrillation

open access: yesJournal of Patient-Centered Research and Reviews, 2020
Purpose: It is It is widely accepted that atrial fibrillation (AF) accounts for half of arrhythmia recurrences following endocardial catheter ablation of AF.
David C. Kress   +4 more
doaj   +1 more source

Rapidly detecting disorder in rhythmic biological signals: A spectral entropy measure to identify cardiac arrhythmias [PDF]

open access: yesPhys. Rev. E 79, 011915 (2009), 2008
We consider the use of a running measure of power spectrum disorder to distinguish between the normal sinus rhythm of the heart and two forms of cardiac arrhythmia: atrial fibrillation and atrial flutter. This spectral entropy measure is motivated by characteristic differences in the spectra of beat timings during the three rhythms.
arxiv   +1 more source

Patient-Specific Identification of Atrial Flutter Vulnerability–A Computational Approach to Reveal Latent Reentry Pathways

open access: yesFrontiers in Physiology, 2019
Atypical atrial flutter (AFlut) is a reentrant arrhythmia which patients frequently develop after ablation for atrial fibrillation (AF). Indeed, substrate modifications during AF ablation can increase the likelihood to develop AFlut and it is clinically ...
A. Loewe   +6 more
semanticscholar   +1 more source

Extraction and analysis of T waves in electrocardiograms during atrial flutter [PDF]

open access: yes, 2010
Analysis of T waves in the electrocardiogram (ECG) is an essential clinical tool for diagnosis, monitoring and followup of patients with heart dysfunction.
Dubé, Bruno   +7 more
core   +1 more source

What Is the Optimal Digoxin Level? Challenging Case of Fetal Atrial Flutter Treatment in a Monochorionic Diamniotic Twin

open access: yesMedicina, 2023
Background: Atrial flutter is an infrequent yet potentially fatal arrhythmia. Digoxin is the preferred first-line treatment for fetal atrial flutter due to its efficacy and favorable safety profile.
Soo Jung Kim   +5 more
doaj   +1 more source

Selecting Classifiers by Pooling over Cross-Validation Results in More Consistency in Small-Sample Classification of Atrial Flutter Localization [PDF]

open access: yesSIE, 2022, Langkawi, Malaysia, 2022
Selecting learning machines such as classifiers is an important task when using AI in the clinic. K-fold crossvalidation is a practical technique that allows simple inference of such machines. However, the recipe generates many models and does not provide a means to determine the best one.
arxiv  

Clinical workflow and applicability of electrophysiological cardiovascular magnetic resonance-guided radiofrequency ablation of isthmus-dependent atrial flutter

open access: yesEuropean Heart Journal-Cardiovascular Imaging, 2018
Aims To determine safety and efficacy of electrophysiological cardiovascular magnetic resonance (EP-CMR)-guided radiofrequency (RF) ablation in patients with typical right atrial flutter in a routine clinical setting.
I. Paetsch   +11 more
semanticscholar   +1 more source

Digoxin for atrial fibrillation and atrial flutter: A systematic review with meta-analysis and trial sequential analysis of randomised clinical trials

open access: yesPLoS ONE, 2018
Background During recent years, systematic reviews of observational studies have compared digoxin to no digoxin in patients with atrial fibrillation or atrial flutter, and the results of these reviews suggested that digoxin seems to increase the risk of ...
N. Sethi   +5 more
semanticscholar   +1 more source

Virtualizing clinical cases of atrial flutter in a fast marching simulation including conduction velocity and ablation scars

open access: yesCurrent Directions in Biomedical Engineering, 2015
Diagnosis of atrial flutter caused by ablation of atrial fibrillation is complex due to ablation scars. This paper presents an approach to replicate the clinically measured flutter circuit in a dynamic computer model.
Trächtler J.   +6 more
doaj   +1 more source

RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECG [PDF]

open access: yesarXiv, 2023
Introduction: Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term, ambulatory ECG recordings have shown high performance. However, the rhythm-based approach does not take advantage of the morphological information conveyed by the different ECG waveforms, particularly the f-waves.
arxiv  

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