ArticleScientific reports2023
Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Leveraging ECG foundation models in critical care for sinus rhythm and atrial fibrillation classification.Intensive care medicine experimental · 2026Article
- Computed Tomography Findings of Pulmonary Lymphoma in a Dog and Two Cats.Veterinary medicine and science · 2026Article
- Nonreciprocal surface plasmonic neural network for decoupled bidirectional analogue computing.Nature communications · 2025Article
- Adaptive deep SVM for detecting early heart disease among cardiac patients.Scientific reports · 2025Article
- State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- [A lightweight classification network for single-lead atrial fibrillation based on depthwise separable convolution and attention mechanism].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Recent advances in the tools and techniques for AI-aided diagnosis of atrial fibrillation.Biophysics reviews · 2025Review
- Deep learning for cardiovascular management: optimizing pathways and cost control under diagnosis-related group models.Frontiers in artificial intelligence · 2025Review
- Research on network security vulnerability risk contagion in software supply chain based on system dynamics.PloS one · 2025Article
- A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI.Frontiers in oncology · 2023Article
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6 authors.
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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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