Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detectionopen access
- Authors
- Laghari, Asif Ali; Sun, Yanqiu; Alhussein, Musaed; Aurangzeb, Khursheed; Anwar, Muhammad Shahid; Rashid, Mamoon
- Issue Date
- Sep-2023
- Publisher
- NATURE PORTFOLIO
- Citation
- SCIENTIFIC REPORTS, v.13, no.1
- Journal Title
- SCIENTIFIC REPORTS
- Volume
- 13
- Number
- 1
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/89477
- DOI
- 10.1038/s41598-023-40343-x
- ISSN
- 2045-2322
- 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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