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Atrial Fibrillation

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Deep Learning Model for Predicting Rhythm Outcomes after Radiofrequency Catheter Ablation in Patients with Atrial Fibrillation.

Journal of healthcare engineering
Current guidelines on atrial fibrillation (AF) emphasized that radiofrequency catheter ablation (RFCA) should be decided after fully considering its prognosis. However, a robust prediction model reflecting the complex interactions between the feature...

Natural Language Processing to Improve Prediction of Incident Atrial Fibrillation Using Electronic Health Records.

Journal of the American Heart Association
Background Models predicting atrial fibrillation (AF) risk, such as Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF), have not performed as well in electronic health records. Natural language processing (NLP) may improve mo...

Deepaware: A hybrid deep learning and context-aware heuristics-based model for atrial fibrillation detection.

Computer methods and programs in biomedicine
BACKGROUND: State-of-the-art automatic atrial fibrillation (AF) detection models trained on RR-interval (RRI) features generally produce high performance on standard benchmark electrocardiogram (ECG) AF datasets. These models, however, result in a si...

Exploiting exercise electrocardiography to improve early diagnosis of atrial fibrillation with deep learning neural networks.

Computers in biology and medicine
Atrial fibrillation (AF) is the most common type of sustained arrhythmia. It results from abnormal irregularities in the electrical performance of the atria, and may cause heart thrombosis, stroke, arterial disease, thromboembolism, and heart failure...

Automatic Time-Resolved Cardiovascular Segmentation of 4D Flow MRI Using Deep Learning.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Segmenting the whole heart over the cardiac cycle in 4D flow MRI is a challenging and time-consuming process, as there is considerable motion and limited contrast between blood and tissue.

Electrocardiogram Biometrics Using Transformer's Self-Attention Mechanism for Sequence Pair Feature Extractor and Flexible Enrollment Scope Identification.

Sensors (Basel, Switzerland)
The existing electrocardiogram (ECG) biometrics do not perform well when ECG changes after the enrollment phase because the feature extraction is not able to relate ECG collected during enrollment and ECG collected during classification. In this rese...

Machine learning based deconvolution of microarray atrial samples from atrial fibrillation patients reveals increased fractions of follicular CD4+ T lymphocytes and gamma-delta T cells.

Journal of physiology and pharmacology : an official journal of the Polish Physiological Society
A potential relationship between T cell immunity and development of atrial fibrillation (AF) has been proposed. Historically in AF patients it has been reported that peripheral blood had elevated CD4+ T cells. However few studies have explored whethe...

Effect of an artificial intelligence-assisted tool on non-valvular atrial fibrillation anticoagulation management in primary care: protocol for a cluster randomized controlled trial.

Trials
BACKGROUND: Atrial fibrillation (AF) is one of the most common cardiac arrhythmia diseases. Thromboembolic prophylaxis plays an essential role in AF therapy, but at present, general practitioners (GPs) are presumed to lack the knowledge and enthusias...

Atrial fibrillation signatures on intracardiac electrograms identified by deep learning.

Computers in biology and medicine
BACKGROUND: Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as 'high rate events'. This may delay or misdirect therapy.

Artificial intelligence-based detection of atrial fibrillation from chest radiographs.

European radiology
OBJECTIVE: The purpose of this study was to develop an artificial intelligence (AI)-based model to detect features of atrial fibrillation (AF) on chest radiographs.