ArticleFrontiers in physiology2021
HADLN: Hybrid Attention-Based Deep Learning Network for Automated Arrhythmia Classification.
Article in Frontiers in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017-2023.Frontiers in physiology · 2023Pooled it
- Intelligent Real-Time Healthcare and Biomedical Monitoring Systems: A Narrative Review of AI, IoT, and Emerging Technologies.Bioengineering (Basel, Switzerland) · 2026Review
- Review of Non-Invasive Fetal Electrocardiography Monitoring Techniques.Sensors (Basel, Switzerland) · 2025Review
- Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images.BMC medical informatics and decision making · 2025Article
- Research on atrial fibrillation diagnosis in electrocardiograms based on CLA-AF model.European heart journal. Digital health · 2025Article
- ECG-Based Multiclass Arrhythmia Classification Using Beat-Level Fusion Network.Journal of healthcare engineering · 2023Article
- Hemodynamic functional connectivity optimization of frequency EEG microstates enables attention LSTM framework to classify distinct temporal cortical communications of different cognitive tasks.Brain informatics · 2022Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- Application of Dense Neural Networks for Detection of Atrial Fibrillation and Ranking of Augmented ECG Feature Set.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, with the development of artificial intelligence, deep learning model has achieved initial success in ECG data analysis, especially the detection of atrial fibrillation. In order to solve the problems of ignoring the correlation between contexts and gradient dispersion in traditional deep convolution neural network model, the hybrid attention-based deep learning network (HADLN) method is proposed to implement arrhythmia classification. The HADLN can make full use of the advantages of residual network (ResNet) and bidirectional long-short-term memory (Bi-LSTM) architecture to obtain fusion features containing local and global information and improve the interpretability of the model through the attention mechanism. The method is trained and verified by using the PhysioNet 2017 challenge dataset. Without loss of generality, the ECG signal is classified into four categories, including atrial fibrillation, noise, other, and normal signals. By combining the fusion features and the attention mechanism, the learned model has a great improvement in classification performance and certain interpretability. The experimental results show that the proposed HADLN method can achieve precision of 0.866, recall of 0.859, accuracy of 0.867, and F1-score of 0.880 on 10-fold cross-validation.
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