ReviewFrontiers in physiology2018
Information Theory and Atrial Fibrillation (AF): A Review.
Review in Frontiers in physiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Predicting Spontaneous Termination of Atrial Fibrillation Based on Analysis of Standard Electrocardiograms: A Systematic Review.Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2024Pooled it
- Sinus rhythm maintenance in persistent atrial fibrillation: 12-lead ECG multiscale entropy characterization.Medical & biological engineering & computing · 2026Article
- Statistics and behavior of clinically significant extra-pulmonary vein atrial fibrillation sources: machine-learning-enhanced electrographic flow mapping in persistent atrial fibrillation.Frontiers in cardiovascular medicine · 2025Article
- mRMEBP: a unified framework for online detection of atrial fibrillation utilizing deep learning.npj biomedical innovations · 2024Article
- Observable Atrial and Ventricular Fibrillation Episode Durations Are Conformant With a Power Law Based on System Size and Spatial Synchronization.Circulation. Arrhythmia and electrophysiology · 2024Article
- Information theory-based direct causality measure to assess cardiac fibrillation dynamics.Journal of the Royal Society, Interface · 2023Article
- A Study of R-R Interval Transition Matrix Features for Machine Learning Algorithms in AFib Detection.Sensors (Basel, Switzerland) · 2023Article
- Atrial fibrillation classification based on the 2D representation of minimal subset ECG and a non-deep neural network.Frontiers in physiology · 2023Article
- Nonlinear Heart Rate Dynamics Before and After Paroxysmal Atrial Fibrillation Events.Acta Cardiologica Sinica · 2022Article
- An Efficient Hybrid Methodology for Local Activation Waves Detection under Complex Fractionated Atrial Electrograms of Atrial Fibrillation.Sensors (Basel, Switzerland) · 2022Article
- Premature Beats Rejection Strategy on Paroxysmal Atrial Fibrillation Detection.Frontiers in physiology · 2022Article
- Determination of Parameters for an Entropy-Based Atrial Fibrillation Detector.Entropy (Basel, Switzerland) · 2021Article
- Functional electrographic flow patterns in patients with persistent atrial fibrillation predict outcome of catheter ablation.Journal of cardiovascular electrophysiology · 2021Article
- Reconceptualising Atrial Fibrillation Using Renewal Theory: A Novel Approach to the Assessment of Atrial Fibrillation Dynamics.Arrhythmia & electrophysiology review · 2021Review
- Over-fitting suppression training strategies for deep learning-based atrial fibrillation detection.Medical & biological engineering & computing · 2021Article
- Multi-scale Entropy Evaluates the Proarrhythmic Condition of Persistent Atrial Fibrillation Patients Predicting Early Failure of Electrical Cardioversion.Entropy (Basel, Switzerland) · 2020Article
- The application of Lempel-Ziv and Titchener complexity analysis for equine telemetric electrocardiographic recordings.Scientific reports · 2019Article
- A New Entropy-Based Atrial Fibrillation Detection Method for Scanning Wearable ECG Recordings.Entropy (Basel, Switzerland) · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atrial Fibrillation (AF) is the most common cardiac rhythm disorder seen in hospitals and in general practice, accounting for up to a third of arrhythmia related hospitalizations. Unfortunately, AF treatment is in practice complicated by the lack of understanding of the fundamental mechanisms underlying the arrhythmia, which makes detection of effective ablation targets particularly difficult. Various approaches to AF mapping have been explored in the hopes of better pinpointing these effective targets, such as Dominant Frequency (DF) analysis, complex fractionated electrograms (CFAE) and unipolar reconstruction (FIRM), but many of these methods have produced conflicting results or require further investigation. Exploration of AF using information theoretic-based approaches may have the potential to provide new insights into the complex system dynamics of AF, whilst also providing the benefit of being less reliant on empirically derived definitions in comparison to alternate mapping approaches. This work provides an overview of information theory and reviews its applications in AF analysis, with particular focus on AF mapping. The works discussed in this review demonstrate how understanding AF from a signal property perspective can provide new insights into the arrhythmic phenomena, which may have valuable clinical implications for AF mapping and ablation in the future.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.