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Abstract
Atrial fibrillation easily leads to stroke, cerebral infarction and other complications, which will seriously harm the life and health of patients. Traditional deep learning methods have weak anti-interference and generalization ability. Therefore, we propose a new-fashioned deep residual-dense network via bidirectional recurrent neural network (RNN) model for atrial fibrillation detection. The combination of one-dimensional dense residual network and bidirectional RNN for atrial fibrillation detection simplifies the tedious feature extraction steps, and constructs the end-to-end neural network to achieve atrial fibrillation detection through data feature learning. Meanwhile, the attention mechanism is utilized to fuse the different features and extract the high-value information. The accuracy of the experimental results is 97.72%, the sensitivity and specificity are 93.09% and 98.71%, respectively compared with other methods.
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Details
1 Shenyang Normal University, Software College, Shenyang, China (GRID:grid.263484.f) (ISNI:0000 0004 1759 8467)
2 Liaoning University of Traditional Chinese Medicine, Shenyang, China (GRID:grid.411464.2) (ISNI:0000 0001 0009 6522)
3 King Saud University, Department of Computer Engineering, College of Computer and Information Sciences, Riyadh, Kingdom of Saudi Arabia (GRID:grid.56302.32) (ISNI:0000 0004 1773 5396)
4 Gachon University, Department of AI and Software, Seongnam-si, South Korea (GRID:grid.256155.0) (ISNI:0000 0004 0647 2973)
5 Vishwakarma University, Department of Computer Engineering, Faculty of Science and Technology, Pune, India (GRID:grid.256155.0) (ISNI:0000 0005 0599 7193)