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Multiscale feature enhanced gating network for atrial fibrillation detection.

Comput Methods Programs Biomed

January 2025

College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, PR China. Electronic address:

Background And Objective: Atrial fibrillation (AF) is a significant cause of life-threatening heart disease due to its potential to lead to stroke and heart failure. Although deep learning-assisted diagnosis of AF based on ECG holds significance in clinical settings, it remains unsatisfactory due to insufficient consideration of noise and redundant features. In this work, we propose a novel multiscale feature-enhanced gating network (MFEG Net) for AF diagnosis.

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Atrial fibrillation (AF) is a prevalent arrhythmia closely associated with atrial fibrosis, posing significant challenges to cardiovascular health. Recent studies have identified the sodium-glucose co-transporter 1 (SGLT1) as a potential key player in the pathophysiology of heart diseases, particularly in the context of AF and atrial fibrosis. This review aims to synthesize current knowledge regarding the mechanisms by which SGLT1 influences the development of AF and atrial fibrosis, with a specific focus on its relationship with endothelial-mesenchymal transition (EMT).

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