Multiscale convolutional neural network for detecting paroxysmal atrial fibrillation from single lead ECG signals-Reference-Cited by-同舟云学术

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Multiscale convolutional neural network for detecting paroxysmal atrial fibrillation from single lead ECG signals

Published:2020-10-07 Issue: Volume: Page:
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Container-title:2020 IEEE Applied Signal Processing Conference (ASPCON)
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Author:

Prabhakararao Eedara,Dandapat Samarendra

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9276206/9276653/09276690.pdf?arnumber=9276690

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. MSGformer: A multi-scale grid transformer network for 12-lead ECG arrhythmia detection;Biomedical Signal Processing and Control;2024-01

2. BioU-Net: Diagnosis Network Based on Spectral Feature Enhancement for Myocardial Infarction;Lecture Notes in Computer Science;2024

3. MSGNet: Multi-Scale Grid Based Automatic Cardiac Arrhythmia Detection in 12-Lead ECG;2023 IEEE Smart World Congress (SWC);2023-08-28

4. Multiscale Encoding of Electrocardiogram Signals with a Residual Network for the Detection of Atrial Fibrillation;Bioengineering;2022-09-16

5. On the use of a residual convolutional neural networks and images derived from Poincaré-plots for the detection of atrial fibrillation;2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON);2021-12-06

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