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Record #47001

An Effective and Lightweight Deep Electrocardiography Arrhythmia Recognition Model Using Novel Special and Native Structural Regularization Techniques on Cardiac Signal

Publisher
Hindawi
Original paper date
Apr 12, 2022
Institution
School of Materials and Energy, State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, China · IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, Guangdong, China · International Institute of Information Technology, Hyderabad, Telangana 500032, India · Department of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia · Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia · Department of Orthopedics Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China · School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China · Computer Science Department, Faculty of Applied Sciences, Taiz University, Taiz 6803, Yemen · Faculty of Medicine, Perm State Medical University, Perm 614000, Russia · Department of Studies in Computer Science, University of Mysore, Mysore, Karnataka, India · IT Department, Sana'a Community College, Sana'a 5695, Yemen · College of Computer Science, Data Intelligence and Computing Art Lab, Sichuan University, Chengdu 610065, China · Department of Computer Science, University of Science and Technology, Bannu, Pakistan · Biomedical Imaging and Electrophysiology Laboratory, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, China
Open access
No
Article type
Research Article
Broad categories
(B/T) Data Science(HSC) Medicine - Cardiovascular
Citation lifecycle

How this paper is being cited

Pre-retraction
14
Same day
1
Post-retraction
6
% post-retraction
28.6%
~12.6 citations/yr before retraction · ~1.8 citations/yr after
— citing papers

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