Machine learning in arrhythmia and electrophysiology
Machine learning (ML), a branch of artificial intelligence, where machines learn from big
data, is at the crest of a technological wave of change sweeping society. Cardiovascular …
data, is at the crest of a technological wave of change sweeping society. Cardiovascular …
Computational models of atrial fibrillation: Achievements, challenges, and perspectives for improving clinical care
Despite significant advances in its detection, understanding and management, atrial
fibrillation (AF) remains a highly prevalent cardiac arrhythmia with a major impact on …
fibrillation (AF) remains a highly prevalent cardiac arrhythmia with a major impact on …
How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management
There has been an exponential growth of artificial intelligence (AI) and machine learning
(ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has …
(ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has …
Detection of atrial fibrillation using a machine learning approach
The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical
practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke …
practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke …
Deep learning in the diagnosis and management of arrhythmias
Recent advancements in analyzing methods for the identification of arrhythmia based on
deep learning have revealed great promise towards improving cardiac care. Probabilistic …
deep learning have revealed great promise towards improving cardiac care. Probabilistic …
Genetic and non-genetic risk factors associated with atrial fibrillation
Atrial fibrillation (AF) is the most common arrhythmic disorder and its prevalence in the
United States is projected to increase to more than twelve million cases in 2030. AF …
United States is projected to increase to more than twelve million cases in 2030. AF …
Artificial intelligence for a personalized diagnosis and treatment of atrial fibrillation
AM Sanchez de la Nava, F Atienza… - American Journal …, 2021 - journals.physiology.org
Although atrial fibrillation (AF) is the most common cardiac arrhythmia, its early identification,
diagnosis, and treatment is still challenging. Due to its heterogeneous mechanisms and risk …
diagnosis, and treatment is still challenging. Due to its heterogeneous mechanisms and risk …
DG-Mapping: a novel software package for the analysis of any type of reentry and focal activation of simulated, experimental or clinical data of cardiac arrhythmia
E Van Nieuwenhuyse, S Hendrickx, RV Abeele… - Medical & Biological …, 2022 - Springer
In this work, we present the release of a novel easy to use software package called DGM or
Directed-Graph-Mapping. DGM can automatically analyze any type of arrhythmia to find …
Directed-Graph-Mapping. DGM can automatically analyze any type of arrhythmia to find …
Deep learning classification of unipolar electrograms in human atrial fibrillation: application in focal source mapping
Focal sources are potential targets for atrial fibrillation (AF) catheter ablation, but they can be
time-consuming and challenging to identify when unipolar electrograms (EGM) are …
time-consuming and challenging to identify when unipolar electrograms (EGM) are …
Advances in cardiac electrophysiology
JP Piccini, AM Russo, PS Sharma, J Kron… - Circulation …, 2022 - Am Heart Assoc
Despite the global COVID-19 pandemic, during the past 2 years, there have been numerous
advances in our understanding of arrhythmia mechanisms and diagnosis and in new …
advances in our understanding of arrhythmia mechanisms and diagnosis and in new …