ArticleHealthcare (Basel, Switzerland)2023
An ECG Classification Method Based on Multi-Task Learning and CoT Attention Mechanism.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 38 citations in OpenAlex.
- RT-AFNet: A Hybrid ResNet-Transformer Architecture with Multi-Scale Fusion for Atrial Fibrillation Detection.Biosensors · 2026Article
- A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet.Scientific reports · 2026Article
- Higher-dimensional embedding of time-series data for machine learning.Frontiers in artificial intelligence · 2026Article
- Comparison of Neural Network Structures for Identifying Shockable Rhythm During Cardiopulmonary Resuscitation.Journal of clinical medicine · 2025Article
- Principal component conditional generative adversarial networks for imbalanced ECG classification enhancement.PloS one · 2025Article
- A coordinated adaptive multiscale enhanced spatio-temporal fusion network for multi-lead electrocardiogram arrhythmia detection.Scientific reports · 2024Article
- Visualized Lead Selection for Arrhythmia Classification Based on a Lead Activation Heatmap Using Multi-Lead ECGs.Bioengineering (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
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Abstract
Electrocardiogram (ECG) is an efficient and simple method for the diagnosis of cardiovascular diseases and has been widely used in clinical practice. Because of the shortage of professional cardiologists and the popularity of electrocardiograms, accurate and efficient arrhythmia detection has become a hot research topic. In this paper, we propose a new multi-task deep neural network, which includes a shared low-level feature extraction module (i.e., SE-ResNet) and a task-specific classification module. Contextual Transformer (CoT) block is introduced in the classification module to dynamically model the local and global information of ECG feature sequence. The proposed method was evaluated on public CPSC2018 and PTB-XL datasets and achieved an average F1 score of 0.827 on the CPSC2018 dataset and an average F1 score of 0.833 on the PTB-XL dataset.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.