AIMC Topic: Atrial Fibrillation

Clear Filters Showing 21 to 30 of 318 articles

An Explainable AI Application (AF'fective) to Support Monitoring of Patients With Atrial Fibrillation After Catheter Ablation: Qualitative Focus Group, Design Session, and Interview Study.

JMIR human factors Feb 13, 2025
BACKGROUND: The opaque nature of artificial intelligence (AI) algorithms has led to distrust in medical contexts, particularly in the treatment and monitoring of atrial fibrillation. Although previous studies in explainable AI have demonstrated poten...
Humans Artificial Intelligence Catheter Ablation Qualitative Research Focus Groups Interviews as Topic Atrial Fibrillation
View on PubMed DOI

Causal Machine Learning for Left Atrial Appendage Occlusion in Patients With Atrial Fibrillation.

JACC. Clinical electrophysiology Feb 12, 2025
BACKGROUND: Transcatheter left atrial appendage occlusion (LAAO) is an alternative to lifelong anticoagulation, but optimal patient selection remains challenging.
Retrospective Studies Female Aged Humans Machine Learning Atrial Fibrillation Propensity Score Atrial Appendage Aged, 80 and over Male Anticoagulants
View on PubMed DOI

Deep CNN-based detection of cardiac rhythm disorders using PPG signals from wearable devices.

PloS one Feb 12, 2025
Cardiac rhythm disorders can manifest in various ways, such as the heart rate being too fast (tachycardia) or too slow (bradycardia), irregular heartbeats (like atrial fibrillation-AF, ventricular fibrillation-VF), or the initiation of heartbeats in ...
Signal Processing, Computer-Assisted Wearable Electronic Devices Arrhythmias, Cardiac Heart Rate Photoplethysmography Humans Atrial Fibrillation Electrocardiography Neural Networks, Computer
View on PubMed DOI

Predicting Atrial Fibrillation Relapse Using Bayesian Networks: Explainable AI Approach.

JMIR cardio Feb 11, 2025
BACKGROUND: Atrial fibrillation (AF) is a prevalent arrhythmia associated with significant morbidity and mortality. Despite advancements in ablation techniques, predicting recurrence of AF remains a challenge, necessitating reliable models to identif...
Artificial Intelligence Catheter Ablation Female Bayes Theorem Recurrence Risk Factors Atrial Fibrillation Pulmonary Veins Humans Aged Male Middle Aged
View on PubMed DOI

Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review.

Journal of medical Internet research Feb 10, 2025
BACKGROUND: Although catheter ablation (CA) is currently the most effective clinical treatment for atrial fibrillation, its variable therapeutic effects among different patients present numerous problems. Machine learning (ML) shows promising potenti...
Humans Machine Learning Atrial Fibrillation Catheter Ablation
View on PubMed DOI

Comparing Phenotypes for Acute and Long-Term Response to Atrial Fibrillation Ablation Using Machine Learning.

Circulation. Arrhythmia and electrophysiology Feb 10, 2025
BACKGROUND: It is difficult to identify patients with atrial fibrillation (AF) most likely to respond to ablation. While any arrhythmia patient may recur after acutely successful ablation, AF is unusual in that patients may have long-term arrhythmia ...
Male Middle Aged Time Factors Phenotype Female Aged Predictive Value of Tests Registries Atrial Fibrillation Electrocardiography Treatment Outcome Action Potentials Humans Machine Learning Electrophysiologic Techniques, Cardiac Heart Rate Catheter Ablation
View on PubMed DOI

A machine learning-based model for predicting paroxysmal and persistent atrial fibrillation based on EHR.

BMC medical informatics and decision making Feb 3, 2025
BACKGROUND: There is no effective way to accurately predict paroxysmal and persistent atrial fibrillation (AF) subtypes unless electrocardiogram (ECG) observation is obtained. We aim to develop a predictive model using a machine learning algorithm fo...
Atrial Fibrillation Electronic Health Records Female Aged Male Humans Middle Aged Machine Learning Electrocardiography
View on PubMed DOI

tinyHLS: a novel open source high level synthesis tool targeting hardware accelerators for artificial neural network inference.

Physiological measurement Jan 29, 2025
In recent years, wearable devices such as smartwatches and smart patches have revolutionized biosignal acquisition and analysis, particularly for monitoring electrocardiography (ECG). However, the limited power supply of these devices often precludes...
Humans Software Signal Processing, Computer-Assisted Neural Networks, Computer Computers Atrial Fibrillation Electrocardiography
View on PubMed DOI

A Novel Deep Ensemble Method for Selective Classification of Electrocardiograms.

IEEE transactions on bio-medical engineering Jan 21, 2025
OBJECTIVE: Telehealth paradigms are essential for remotely managing patients with chronic conditions. To assist clinicians in handling the large volumes of data collected through these systems, clinical decision support systems (CDSSs) have been deve...
Electrocardiography Signal Processing, Computer-Assisted Telemedicine Atrial Fibrillation Sensitivity and Specificity Humans Deep Learning Decision Support Systems, Clinical Algorithms Reproducibility of Results
View on PubMed DOI

Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images.

BMC medical informatics and decision making Jan 21, 2025
BACKGROUND: As the incidence and prevalence of Atrial Fibrillation (AF) proliferate worldwide, the condition has become the epicenter of a plethora of ECG diagnostic research. In recent diagnostic methodologies, Morse Continuous Wavelet Transform (Ms...
Wavelet Analysis Electrocardiography Atrial Fibrillation Humans Deep Learning
View on PubMed DOI
Popular Topics
Recent Journals