ArticleSensors (Basel, Switzerland)2020
Hybrid Network with Attention Mechanism for Detection and Location of Myocardial Infarction Based on 12-Lead Electrocardiogram Signals.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.Biomedical engineering online · 2025Pooled it
- Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017-2023.Frontiers in physiology · 2023Pooled it
- Artificial intelligence-enhanced electrocardiography for acute myocardial infarction detection: a systematic review.Cardiovascular diagnosis and therapy · 2026Review
- Optimizing myocardial infarction detection: a hybrid CNN-GRU deep learning approach.BMC medical informatics and decision making · 2025Article
- Generalizable Hybrid Wavelet-Deep Learning Architecture for Robust Arrhythmia Detection in Wearable ECG Monitoring.Sensors (Basel, Switzerland) · 2025Article
- Data Augmentation-Enhanced Myocardial Infarction Classification and Localization Using a ResNet-Transformer Cascaded Network.Biology · 2025Article
- A Multi-Domain Feature Fusion CNN for Myocardial Infarction Detection and Localization.Biosensors · 2025Article
- Comparison of Neural Network Structures for Identifying Shockable Rhythm During Cardiopulmonary Resuscitation.Journal of clinical medicine · 2025Article
- A hybrid deep learning network for automatic diagnosis of cardiac arrhythmia based on 12-lead ECG.Scientific reports · 2024Article
- Quantifying attention in children with intellectual and developmental disabilities through multicenter electrooculogram signal analysis.Scientific reports · 2024Article
- Prototype Learning for Medical Time Series Classification via Human-Machine Collaboration.Sensors (Basel, Switzerland) · 2024Article
- [A review on intelligent auxiliary diagnosis methods based on electrocardiograms for myocardial infarction].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Review
- Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease.BMC cardiovascular disorders · 2023Article
- Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease Diagnostics.Bioengineering (Basel, Switzerland) · 2023Article
- Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- A Novel Preoperative Prediction Model Based on Deep Learning to Predict Neoplasm T Staging and Grading in Patients with Upper Tract Urothelial Carcinoma.Journal of clinical medicine · 2022Article
- End-to-End Convolutional Neural Network Model to Detect and Localize Myocardial Infarction Using 12-Lead ECG Images without Preprocessing.Bioengineering (Basel, Switzerland) · 2022Article
- A Robustness Evaluation of Machine Learning Algorithms for ECG Myocardial Infarction Detection.Journal of clinical medicine · 2022Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- Detection and Localization of Myocardial Infarction Based on Multi-Scale ResNet and Attention Mechanism.Frontiers in physiology · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The electrocardiogram (ECG) is a non-invasive, inexpensive, and effective tool for myocardial infarction (MI) diagnosis. Conventional detection algorithms require solid domain expertise and rely heavily on handcrafted features. Although previous works have studied deep learning methods for extracting features, these methods still neglect the relationships between different leads and the temporal characteristics of ECG signals. To handle the issues, a novel multi-lead attention (MLA) mechanism integrated with convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) framework (MLA-CNN-BiGRU) is therefore proposed to detect and locate MI via 12-lead ECG records. Specifically, the MLA mechanism automatically measures and assigns the weights to different leads according to their contribution. The two-dimensional CNN module exploits the interrelated characteristics between leads and extracts discriminative spatial features. Moreover, the BiGRU module extracts essential temporal features inside each lead. The spatial and temporal features from these two modules are fused together as global features for classification. In experiments, MI location and detection were performed under both intra-patient scheme and inter-patient scheme to test the robustness of the proposed framework. Experimental results indicate that our intelligent framework achieved satisfactory performance and demonstrated vital clinical significance.
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Registered trials
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