AIMC Topic: Atrial Fibrillation
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SeqAFNet: A Beat-Wise Sequential Neural Network for Atrial Fibrillation Classification in Adhesive Patch-Type Electrocardiographs.
IEEE journal of biomedical and health informatics
Sep 5, 2024
Due to their convenience, adhesive patch-type electrocardiographs are commonly used for arrhythmia screening. This study aimed to develop a reliable method that can improve the classification performance of atrial fibrillation (AF) using these device...
RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection From the Raw ECG.
IEEE journal of biomedical and health informatics
Sep 5, 2024
INTRODUCTION: Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term ambulatory ECG recordings have shown high performance. However, the rhythm-based approach does not take advantage of the morph...
Electrocardiogram-Based Artificial Intelligence to Discriminate Cardioembolic Stroke and Stratify Risk of Atrial Fibrillation After Stroke.
Circulation. Arrhythmia and electrophysiology
Aug 28, 2024
DECNet: Left Atrial Pulmonary Vein Class Imbalance Classification Network.
Journal of imaging informatics in medicine
Aug 20, 2024
In clinical practice, the anatomical classification of pulmonary veins plays a crucial role in the preoperative assessment of atrial fibrillation radiofrequency ablation surgery. Accurate classification of pulmonary vein anatomy assists physicians in...
AI-based derivation of atrial fibrillation phenotypes in the general and critical care populations.
EBioMedicine
Aug 16, 2024
BACKGROUND: Atrial fibrillation (AF) is the most common heart arrhythmia worldwide and is linked to a higher risk of mortality and morbidity. To predict AF and AF-related complications, clinical risk scores are commonly employed, but their predictive...
Machine learning-based model to predict composite thromboembolic events among Chinese elderly patients with atrial fibrillation.
BMC cardiovascular disorders
Aug 13, 2024
OBJECTIVE: Accurate prediction of survival prognosis is helpful to guide clinical decision-making. The aim of this study was to develop a model using machine learning techniques to predict the occurrence of composite thromboembolic events (CTEs) in e...
Deep learning-based multimodal fusion of the surface ECG and clinical features in prediction of atrial fibrillation recurrence following catheter ablation.
BMC medical informatics and decision making
Aug 8, 2024
BACKGROUND: Despite improvement in treatment strategies for atrial fibrillation (AF), a significant proportion of patients still experience recurrence after ablation. This study aims to propose a novel algorithm based on Transformer using surface ele...
Identification of potential biomarkers for atrial fibrillation and stable coronary artery disease based on WGCNA and machine algorithms.
BMC cardiovascular disorders
Aug 2, 2024
BACKGROUND: Patients with atrial fibrillation (AF) often have coronary artery disease (CAD), but the biological link between them remains unclear. This study aims to explore the common pathogenesis of AF and CAD and identify common biomarkers.
Letter to the Editor: 'Machine learning to develop a novel model for predicting new-onset postoperative atrial fibrillation after coronary artery bypass grafting'.
International journal of surgery (London, England)
Aug 1, 2024
A machine learning-based predictive model for the in-hospital mortality of critically ill patients with atrial fibrillation.
International journal of medical informatics
Jul 31, 2024
BACKGROUND: Atrial fibrillation (AF) is common among intensive care unit (ICU) patients and significantly raises the in-hospital mortality rate. Existing scoring systems or models have limited predictive capabilities for AF patients in ICU. Our study...
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