Single-lead electrocardiogram Artificial Intelligence model with risk factors detects atrial fibrillation during sinus rhythm
S Dupulthys, K Dujardin, W Anné, P Pollet… - Europace, 2024 - academic.oup.com
Aims Guidelines recommend opportunistic screening for atrial fibrillation (AF), using a 30 s
single-lead electrocardiogram (ECG) recorded by a wearable device. Since many patients …
single-lead electrocardiogram (ECG) recorded by a wearable device. Since many patients …
ECGencode: Compact and computationally efficient deep learning feature encoder for ECG signals
L Bontinck, K Fonteyn, T Dhaene… - Expert Systems with …, 2024 - Elsevier
The visual interpretation of electrocardiogram (ECG) data is driven by human pattern
recognition and requires in-depth medical knowledge. Although state-of-the-art deep …
recognition and requires in-depth medical knowledge. Although state-of-the-art deep …
Artificial intelligence predicts undiagnosed atrial fibrillation in patients with embolic stroke of undetermined source using sinus rhythm electrocardiograms
Background Artificial intelligence (AI)–enabled sinus rhythm (SR) electrocardiogram (ECG)
interpretation can aid in identifying undiagnosed paroxysmal atrial fibrillation (AF) in patients …
interpretation can aid in identifying undiagnosed paroxysmal atrial fibrillation (AF) in patients …
A deep learning method for beat-level risk analysis and interpretation of atrial fibrillation patients during sinus rhythm
J Lei, Y Zhou, X Tian, Q Zhao, Q Zhang, S Geng… - … Signal Processing and …, 2025 - Elsevier
Atrial Fibrillation (AF) is a common cardiac arrhythmia. Many AF patients experience
complications such as stroke and other cardiovascular issues. Early detection of AF is …
complications such as stroke and other cardiovascular issues. Early detection of AF is …
ECG-based machine learning model for AF identification in patients with first ischemic stroke
CC Yu, YQ Peng, C Lin, CH Chiang… - … Journal of Stroke, 2024 - journals.sagepub.com
Background: The recurrence rate of strokes associated with atrial fibrillation (AF) can be
substantially reduced through the administration of oral anticoagulants. However, previous …
substantially reduced through the administration of oral anticoagulants. However, previous …
Risk Prediction for Non-cardiac Surgery Using the 12-Lead Electrocardiogram: An Explainable Deep Learning Approach
Background To improve on existing noncardiac surgery risk scores, we propose a novel
approach which leverages features of the preoperative 12-lead electrocardiogram (ECG) to …
approach which leverages features of the preoperative 12-lead electrocardiogram (ECG) to …
Machine learning based atrial fibrillation detection and onset prediction using QT-dynamicity
JM Gregoire, C Gilon, N Vaneberg… - Physiological …, 2024 - iopscience.iop.org
Objective This study examines the value of ventricular repolarization using QT dynamicity for
two different types of atrial fibrillation (AF) prediction. Approach We studied the importance of …
two different types of atrial fibrillation (AF) prediction. Approach We studied the importance of …
Lead-Specific Performance for Atrial Fibrillation Detection in Convolutional Neural Network Models Using Sinus Rhythm Electrocardiography
S Suzuki, J Motogi, T Umemoto, N Hirota… - Circulation …, 2024 - jstage.jst.go.jp
Background: We developed a convolutional neural network (CNN) model to detect atrial
fibrillation (AF) using the sinus rhythm ECG (SR-ECG). However, the diagnostic performance …
fibrillation (AF) using the sinus rhythm ECG (SR-ECG). However, the diagnostic performance …
AI-Enabled ECG for Paroxysmal Atrial Fibrillation Detection: One Step to Closer to the Finish Line
MM Kalscheur, O Akbilgic - Clinical Electrophysiology, 2023 - jacc.org
With the rapidly increasing global burden of atrial fibrillation(AF) and the increased risk of
stroke, heart failure, hospitalization, cognitive decline, and decreased quality of life …
stroke, heart failure, hospitalization, cognitive decline, and decreased quality of life …