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A Novel Method for Automatic Detection of Arrhythmias Using the Unsupervised Convolutional Neural Network Cover

A Novel Method for Automatic Detection of Arrhythmias Using the Unsupervised Convolutional Neural Network

Journal of Artificial Intelligence and Soft Computing Research
Volume 13 (2023): Issue 3 (June 2023)
By: Junming Zhang,  Ruxian Yao,  Jinfeng Gao,  Gangqiang Li and  Haitao Wu  
Open Access
|Jun 2023

Authors

Junming Zhang

2013zhangjm@tongji.edu.cn

College of Information Engineering, Huanghuai University, Henan, China
Henan Key Laboratory of Smart Lighting, Henan, China
Henan Joint International Research Laboratory of Behavior Optimization Control for Smart Robots, Henan, China
Zhumadian Artificial Intelligence & Medical Engineering Technical Research Centre, Henan, China

Ruxian Yao

College of Information Engineering, Huanghuai University, Henan, China
Henan Key Laboratory of Smart Lighting, Henan, China

Jinfeng Gao

College of Information Engineering, Huanghuai University, Henan, China
Henan Key Laboratory of Smart Lighting, Henan, China

Gangqiang Li

College of Information Engineering, Huanghuai University, Henan, China
Henan Key Laboratory of Smart Lighting, Henan, China

Haitao Wu

College of Information Engineering, Huanghuai University, Henan, China
Henan Key Laboratory of Smart Lighting, Henan, China
DOI: https://doi.org/10.2478/jaiscr-2023-0014
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Language: English
Page range: 181 - 196
Submitted on: Jan 28, 2023
|
Accepted on: May 17, 2023
|
Published on: Jun 23, 2023
Published by: SAN University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
convolutional neural network,
arrhythmia detection,
unsupervised learning,
ECG classification
Related subjects:
Computer sciences,
Databases and data mining,
Artificial intelligence

© 2023 Junming Zhang, Ruxian Yao, Jinfeng Gao, Gangqiang Li, Haitao Wu, published by SAN University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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