Bat: Beat-aligned transformer for electrocardiogram classification
X Li, C Li, Y Wei, Y Sun, J Wei, X Li… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
Electrocardiogram (ECG) is one of the critical diagnostic tools in healthcare. Various deep
learning models, except Transformers, have been explored and applied to map ECG …
learning models, except Transformers, have been explored and applied to map ECG …
MSW-Transformer: Multi-scale shifted windows transformer networks for 12-lead ECG classification
R Cheng, Z Zhuang, S Zhuang, L Xie, J Guo - arXiv preprint arXiv …, 2023 - arxiv.org
Automatic classification of electrocardiogram (ECG) signals plays a crucial role in the early
prevention and diagnosis of cardiovascular diseases. While ECG signals can be used for …
prevention and diagnosis of cardiovascular diseases. While ECG signals can be used for …
Transforming ECG diagnosis: An in-depth review of transformer-based deeplearning models in cardiovascular disease detection
Z Zhao - arXiv preprint arXiv:2306.01249, 2023 - arxiv.org
The emergence of deep learning has significantly enhanced the analysis of
electrocardiograms (ECGs), a non-invasive method that is essential for assessing heart …
electrocardiograms (ECGs), a non-invasive method that is essential for assessing heart …
A token selection-based multi-scale dual-branch CNN-transformer network for 12-lead ECG signal classification
The timely identification of cardiovascular diseases is critical for effective intervention, with
the electrocardiogram (ECG) serving as a pivotal diagnostic tool. Recent advancements in …
the electrocardiogram (ECG) serving as a pivotal diagnostic tool. Recent advancements in …
Arrhythmia classification with heartbeat-aware transformer
B Wang, C Liu, C Hu, X Liu… - ICASSP 2021-2021 IEEE …, 2021 - ieeexplore.ieee.org
Electrocardiography (ECG) is a conventional method in arrhythmia diagnosis. In this paper,
we proposed a novel neural network model which treats typical heartbeat classification task …
we proposed a novel neural network model which treats typical heartbeat classification task …
HeartBEiT: Vision Transformer for Electrocardiogram Data Improves Diagnostic Performance at Low Sample Sizes
The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural
networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer …
networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer …
[HTML][HTML] Constrained transformer network for ECG signal processing and arrhythmia classification
Background Heart disease diagnosis is a challenging task and it is important to explore
useful information from the massive amount of electrocardiogram (ECG) records of patients …
useful information from the massive amount of electrocardiogram (ECG) records of patients …
Fusing transformer model with temporal features for ECG heartbeat classification
ECG heartbeat classification plays a vital role in diagnosis of cardiac arrhythmia. Traditional
heartbeat classification methods rely on handcrafted features and often fail to learn …
heartbeat classification methods rely on handcrafted features and often fail to learn …
[HTML][HTML] A foundational vision transformer improves diagnostic performance for electrocardiograms
The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural
networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer …
networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer …
ECGformer: Leveraging transformer for ECG heartbeat arrhythmia classification
An arrhythmia, also known as a dysrhythmia, refers to an irregular heartbeat. There are
various types of arrhythmias that can originate from different areas of the heart, resulting in …
various types of arrhythmias that can originate from different areas of the heart, resulting in …