ArticleComputational and mathematical methods in medicine2022
Interpatient ECG Arrhythmia Detection by Residual Attention CNN.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Survey of Data Augmentation of ECG Signals for AI Applications.Sensors (Basel, Switzerland) · 2023Pooled it
- CaReS-BiNet: A multi-scale deep learning framework for ECG arrhythmia classification.Scientific reports · 2026Article
- Automated ECG Arrhythmia Classification Using Feature Images with Common Matrix Approach-Based Classifier.Sensors (Basel, Switzerland) · 2025Article
- Arrhythmia classification based on multi-input convolutional neural network with attention mechanism.PloS one · 2025Article
- Research on atrial fibrillation diagnosis in electrocardiograms based on CLA-AF model.European heart journal. Digital health · 2025Article
- An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The precise identification of arrhythmia is critical in electrocardiogram (ECG) research. Many automatic classification methods have been suggested so far. However, efficient and accurate classification is still a challenge due to the limited feature extraction and model generalization ability. We integrate attention mechanism and residual skip connection into the U-Net (RA-UNET); besides, a skip connection between the RA-UNET and a residual block is executed as a residual attention convolutional neural network (RA-CNN) for accurate classification. The model was evaluated using the MIT-BIH arrhythmia database and achieved an accuracy of 98.5% and
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