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V2IED: Dual-view learning framework for detecting events of interictal epileptiform discharges

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成果类型:
期刊论文
作者:
Ming, Zhekai;Chen, Dan;Gao, Tengfei;Tang, Yunbo;Tu, Weiping;...
通讯作者:
Chen, D
作者机构:
[Chen, Dan; Gao, Tengfei; Chen, D; Ming, Zhekai; Tu, Weiping] Wuhan Univ, Natl Engn Res Ctr Multimedia Software, Sch Comp Sci, Hubei Key Lab Multimedia & Network Commun Engn, Wuhan 430072, Peoples R China.
[Tang, Yunbo] Fuzhou Univ, Coll Comp & Data Sci, Fuzhou 350108, Peoples R China.
[Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.
通讯机构:
[Chen, D ] W
Wuhan Univ, Natl Engn Res Ctr Multimedia Software, Sch Comp Sci, Hubei Key Lab Multimedia & Network Commun Engn, Wuhan 430072, Peoples R China.
语种:
英文
关键词:
Convolutional neural network;Dual-view learning;Electroencephalography;Interictal epileptiform discharge;Long short-term memories
期刊:
Neural Networks
ISSN:
0893-6080
年:
2024
卷:
172
页码:
106136
基金类别:
STI2030-Major Projects+ [2021ZD0204300]; National Natural Science Foundation of China [62172304, 61977027]; Science & Technology Major Project Hubei Province (Next-Generation AI Technologies) [2021BEA159]; National Defense Basic Scientific Research Program of China [JCKY2020110B026]; National Key Research and Development Program of China [2019YFB1600700, 2019YFB1600701]; Wuhan Maritime Communication Research Institute [2020MG001/050-22- CF]
机构署名:
本校为其他机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
Interictal epileptiform discharges (IED) as large intermittent electrophysiological events are associated with various severe brain disorders. Automated IED detection has long been a challenging task, and mainstream methods largely focus on singling out IEDs from backgrounds from the perspective of waveform, leaving normal sharp transients/artifacts with similar waveforms almost unattended. An open issue still remains to accurately detect IED events that directly reflect the abnormalities in brain electrophysiological activities, minimizing the interference from irrelevant sharp transients wit...

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