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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

电子科技学刊2025，Vol.23Issue(3)：71-88,18.

电子科技学刊2025，Vol.23Issue(3)：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)

Key words

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

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].电子科技学刊,2025,23(3):71-88,18.

基金项目

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

电子科技学刊

ISSN：1674-862X

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