RIHANet: A Residual-based Inception with Hybrid-Attention Network for Seizure Detection using EEG signals-Reference-Cited by-同舟云学术

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RIHANet: A Residual-based Inception with Hybrid-Attention Network for Seizure Detection using EEG signals

Published:2024-03 Issue: Volume:171 Page:108086
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Zhou Qiaoli^ORCID,Zhang Shun^ORCID,Du Qiang,Ke Li

Funder

National Natural Science Foundation of China

Department of Education of Liaoning Province

Publisher

Elsevier BV

Reference99 articles.

1. Epilepsy;World Health Organization,2017

2. Types of seizures;The Johns Hopkins University,2023

3. Sudden religious conversions in temporal lobe epilepsy;Dewhurst;Br. J. Psychiatry,1970

4. Early identification of refractory epilepsy;Kwan;N. Engl. J. Med.,2000

5. Epileptic seizure detection via EEG using tree-based pipeline optimization tool;Javel,2019

Cited by 2 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. ASP-DSRN: Accurate Seizure Prediction Using Dual Self-Attention Residual Networking Model;2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI);2024-05-09

2. An Epileptic EEG Classification Approach with Spike Train Encoding Using Spiking Neural Networks;Communications in Computer and Information Science;2024

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