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XABH-CNN-GRU:Explainable attention-based hybrid CNN-GRU model for accurate identification of common arrhythmias

Abduljabbar S.Ba Mahel Fahad Mushabbab G.Alotaibi Zenebe Markos Lonseko Ni-Ni Rao

Journal of Electronic Science and Technology2025，Vol.23Issue(3)：P.71-88,18.

Journal of Electronic Science and Technology2025，Vol.23Issue(3)：P.71-88,18.DOI:10.1016/j.jnlest.2025.100322

XABH-CNN-GRU:Explainable attention-based hybrid CNN-GRU model for accurate identification of common arrhythmias

Abduljabbar S.Ba Mahel ¹Fahad Mushabbab G.Alotaibi ²Zenebe Markos Lonseko ¹Ni-Ni Rao¹

作者信息

1. School of Life Science and Technology,University of Electronic Science and Technology of China,Chengdu,610054,China
2. School of Life Science and Technology,University of Electronic Science and Technology of China,Chengdu,610054,China Department of Biomedical Technology,College of Applied Medical Sciences,King Saud University,Riyadh,11433,Saudi Arabia
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摘要

关键词

Arrhythmias/Cardiac/Classification/Deep learning/Electrocardiogram(ECG)

分类

医药卫生

引用本文复制引用

Abduljabbar S.Ba Mahel,Fahad Mushabbab G.Alotaibi,Zenebe Markos Lonseko,Ni-Ni Rao..XABH-CNN-GRU:Explainable attention-based hybrid CNN-GRU model for accurate identification of common arrhythmias[J].Journal of Electronic Science and Technology,2025,23(3):P.71-88,18.

基金项目

supported by the National Natural Science Foundation of China under Grant No.62271127 （）

the Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China and the West China Hospital of Sichuan University under Grants No.ZYGX2022YGRH011 and No.HXDZ22005 （）

the Natural Science Foundation of Sichuan,China under Grant No.23NSFSC0627 （）

Sichuan Provincial Key Laboratory Fund for Ultra Sound Cardioelectrophysiology and Biomechanics,China under Grant No.2023KFKT01. （）

Journal of Electronic Science and Technology

ISSN：1674-862X

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