ArticleComputers in biology and medicine2019
A technique for measuring anisotropy in atrial conduction to estimate conduction velocity and atrial fibre direction.
Article in Computers in biology and medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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
32 citing papers in PubMed, 68 citations in OpenAlex.
- Association between left atrial slow conduction velocity and recurrence of atrial fibrillation: a prospective study based on high-density mapping.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2025Article
- High-density evaluation of the arrhythmogenic substrate in persistent atrial fibrillation.Heart rhythm O2 · 2025Article
- Mitochondrial dysfunction as a central hub linking NaFrontiers in cardiovascular medicine · 2025Review
- Overcoming Uncertainties in Electrogram-Based Atrial Fibrillation Mapping: A Review.Cardiovascular engineering and technology · 2024Review
- A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data.Arrhythmia & electrophysiology review · 2024Review
- Vector Field Heterogeneity for the Assessment of Locally Disorganised Cardiac Electrical Propagation Wavefronts From High-Density Multielectrodes.IEEE open journal of engineering in medicine and biology · 2024Article
- Constructing bilayer and volumetric atrial models at scale.Interface focus · 2023Article
- Explainable Machine Learning to Predict Anchored Reentry Substrate Created by Persistent Atrial Fibrillation Ablation in Computational Models.Journal of the American Heart Association · 2023Article
- Fiber Organization Has Little Effect on Electrical Activation Patterns During Focal Arrhythmias in the Left Atrium.IEEE transactions on bio-medical engineering · 2023Article
- Article
- Automated conduction velocity estimation based on isochronal activation of heart chambers.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2023Article
- Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds.Scientific reports · 2022Article
- Manifold Approximating Graph Interpolation of Cardiac Local Activation Time.IEEE transactions on bio-medical engineering · 2022Article
- Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate.Medical & biological engineering & computing · 2022Review
- Circle Method for Robust Estimation of Local Conduction Velocity High-Density Maps From Optical Mapping Data: Characterization of Radiofrequency Ablation Sites.Frontiers in physiology · 2022Article
- Fast Characterization of Inducible Regions of Atrial Fibrillation Models With Multi-Fidelity Gaussian Process Classification.Frontiers in physiology · 2022Article
- Review
- Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks.Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH (Conference) · 2021Article
- Article
- Electrocardiographic Imaging for Atrial Fibrillation: A Perspective From Computer Models and Animal Experiments to Clinical Value.Frontiers in physiology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundCardiac conduction properties exhibit large variability, and affect patient-specific arrhythmia mechanisms. However, it is challenging to clinically measure conduction velocity (CV), anisotropy and fibre direction. Our aim is to develop a technique to estimate conduction anisotropy and fibre direction from clinically available electrical recordings.
methodsWe developed and validated automated algorithms for estimating cardiac CV anisotropy, from any distribution of recording locations on the atrial surface. The first algorithm is for elliptical wavefront fitting to a single activation map (method 1), which works well close to the pacing location, but decreases in accuracy further from the pacing location (due to spatial heterogeneity in the conductivity and fibre fields). As such, we developed a second methodology for measuring local conduction anisotropy, using data from two or three activation maps (method 2: ellipse fitting to wavefront propagation velocity vectors from multiple activation maps).
resultsEllipse fitting to CV vectors from two activation maps (method 2) leads to an improved estimation of longitudinal and transverse CV compared to method 1, but fibre direction estimation is still relatively poor. Using three activation maps with method 2 provides accurate estimation, with approximately 70% of atrial fibres estimated within 20
conclusionsWe have developed novel algorithms for calculating CV and measuring the direction dependency of atrial activation to estimate atrial fibre direction, without the need for specialised pacing protocols, using clinically available electrical recordings.
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.