ArticleFrontiers in physiology2021
Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid
Article in Frontiers in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Atrial fibrosis in atrial fibrillation: Mechanisms, mapping techniques and clinical applications.The Journal of physiology · 2026Review
- Artificial Intelligence in Cardiac Electrophysiology: A Comprehensive Review.Journal of personalized medicine · 2025Review
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Bioengineering (Basel, Switzerland) · 2025Review
- Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Review
- Analyzing the performance of biomedical time-series segmentation with electrophysiology data.Scientific reports · 2025Article
- Clinical usefulness of digital twin guided virtual amiodarone test in patients with atrial fibrillation ablation.NPJ digital medicine · 2024Article
- Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.Physiological reviews · 2024Review
- Directed Graph Mapping exceeds Phase Mapping for the detection of simulated 2D meandering rotors in fibrotic tissue with added noise.Computers in biology and medicine · 2024Article
- Impact of data synthesis strategies for the classification of craniosynostosis.Frontiers in medical technology · 2023Article
- Machine learning of adipose tissue in atrial fibrillation.Heart rhythm · 2022Article
- Atrial fibrosis identification with unipolar electrogram eigenvalue distribution analysis in multi-electrode arrays.Medical & biological engineering & computing · 2022Article
- Confocal microscopy-based estimation of intracellular conductivities in myocardium for modeling of the normal and infarcted heart.Computers in biology and medicine · 2022Article
- A Review of Healthy and Fibrotic Myocardium Microstructure Modeling and Corresponding Intracardiac Electrograms.Frontiers in physiology · 2022Review
- How synergy between mechanistic and statistical models is impacting research in atrial fibrillation.Frontiers in physiology · 2022Review
- Multiparametric analysis of geometric features of fibrotic textures leading to cardiac arrhythmias.Scientific reports · 2021Article
- Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a HybridFrontiers in physiology · 2021Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. However, it remains an open challenge to find fibrotic areas and to differentiate their density and transmurality. This study aims to identify the volume fraction and transmurality of fibrosis in the atrial substrate. Simulated cardiac electrograms, combined with a generalized model of clinical noise, reproduce clinically measured signals. Our hybrid dataset approach combines
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