AIMC Topic: Atrial Fibrillation
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Compressed Deep Learning Models for Wearable Atrial Fibrillation Detection through Attention.
Sensors (Basel, Switzerland)
Jul 24, 2024
Deep learning (DL) models have shown promise for the accurate detection of atrial fibrillation (AF) from electrocardiogram/photoplethysmography (ECG/PPG) data, yet deploying these on resource-constrained wearable devices remains challenging. This stu...
Detection of atrial fibrillation using a nonlinear Lorenz Scattergram and deep learning in primary care.
BMC primary care
Jul 20, 2024
BACKGROUND: Atrial fibrillation (AF) is highly correlated with heart failure, stroke and death. Screening increases AF detection and facilitates the early adoption of comprehensive intervention. Long-term wearable devices have become increasingly pop...
An assessment of the value of deep neural networks in genetic risk prediction for surgically relevant outcomes.
PloS one
Jul 15, 2024
INTRODUCTION: Postoperative complications affect up to 15% of surgical patients constituting a major part of the overall disease burden in a modern healthcare system. While several surgical risk calculators have been developed, none have so far been ...
Predictors of left atrial appendage thrombus in atrial fibrillation patients undergoing cardioversion.
Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing
Jul 12, 2024
BACKGROUND: Atrial fibrillation and atrial flutter represent the most prevalent clinically significant cardiac arrhythmias. While the CHA2DS2-VASc score is commonly used to inform anticoagulation therapy decisions for patients with these conditions, ...
Machine learning-based atrial fibrillation detection and onset prediction using QT-dynamicity.
Physiological measurement
Jul 1, 2024
. This study examines the value of ventricular repolarization using QT dynamicity for two different types of atrial fibrillation (AF) prediction.. We studied the importance of QT-dynamicity (1) in the detection and (2) the onset prediction (i.e. fore...
Higher-Order Spectral Analysis Combined with a Convolution Neural Network for Atrial Fibrillation Detection-Preliminary Study.
Sensors (Basel, Switzerland)
Jun 27, 2024
The global burden of atrial fibrillation (AFIB) is constantly increasing, and its early detection is still a challenge for public health and motivates researchers to improve methods for automatic AFIB prediction and management. This work proposes hig...
Artificial Intelligence-Based Atrial Fibrillation Recognition Method for Motion Artifact-Contaminated Electrocardiogram Signals Preprocessed by Adaptive Filtering Algorithm.
Sensors (Basel, Switzerland)
Jun 11, 2024
Atrial fibrillation (AF) is a common arrhythmia, and out-of-hospital, wearable, long-term electrocardiogram (ECG) monitoring can help with the early detection of AF. The presence of a motion artifact (MA) in ECG can significantly affect the character...
Precise risk-prediction model including arterial stiffness for new-onset atrial fibrillation using machine learning techniques.
Journal of clinical hypertension (Greenwich, Conn.)
Jun 8, 2024
Atrial fibrillation (AF) is the most common clinically significant cardiac arrhythmia and is an important risk factor for ischemic cerebrovascular events. This study used machine learning techniques to develop and validate a new risk prediction model...
xECGArch: a trustworthy deep learning architecture for interpretable ECG analysis considering short-term and long-term features.
Scientific reports
Jun 7, 2024
Deep learning-based methods have demonstrated high classification performance in the detection of cardiovascular diseases from electrocardiograms (ECGs). However, their blackbox character and the associated lack of interpretability limit their clinic...
Machine Learning of Cardiac Anatomy and the Risk of New-Onset Atrial Fibrillation After TAVR.
JACC. Clinical electrophysiology
Jun 5, 2024
BACKGROUND: New-onset atrial fibrillation (NOAF) occurs in 5% to 15% of patients who undergo transfemoral transcatheter aortic valve replacement (TAVR). Cardiac imaging has been underutilized to predict NOAF following TAVR.
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