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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
Chenqin Liu1,2, Gaozang Lin1,2, Jingjing Zhou1,2
1School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060.
This study introduces an automated algorithm for detecting atrial fibrillation using BP neural networks and SVM. The algorithm achieves high accuracy, demonstrating its potential for expert-level analysis of electrocardiogram (ECG) data.
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