System Development & Application
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 7
2098-2103

Epileptic EEG identification with cross layer fully connected neural network

Wang Fengqin1
Lu Guanming2
Ke Hengjin3
Xiao Xinfeng4
1. College of Physics & Electronic Science, Hubei Normal University, Huangshi Hubei 435102, China
2. College of Telecommunications & Information Engineering, Nanjing University of Posts & Telecommunications, Nanjing 210003, China
3. School of Computer Science, Wuhan University, Wuhan 430072, China
4. Guangdong Polytechnic of Environmental Protection Engineering, Guangzhou 528216, China
DOI: 10.19734/j.issn.1001-3695.2018.01.0017

Abstract

Under the circumstance of insufficient prior knowledge, it becomes even more important to adaptively classify the synchronization dynamics to accurately characterize the intrinsic nature of seizure activities represented by the EEG. This paper first measured the global synchronization by calculating clustering partition mutual information(MI) of all EEG data channels. Then it designed a cross layer fully connected net to adaptively characterize the synchronization dynamics captured correlation matrices and automatically identify the seizure states of the EEG. It also performed experiments over the CHB-MIT scalp EEG dataset to evaluate the proposed approach. It identified seizure states with an accuracy, sensitivity and specificity of [98.19%±0.24%], [98.27%±0.51%], and[98.11%±0.36%], respectively. The resulted performance was superior to those of most existing methods over the same dataset. The approach alleviated the need for strictly denoising and artifact removing based on the EEG prior knowledge that is mandatory for existing methods. Only one hyper-parameter need be set manually to avoid getting worse performance because of complex parameter setting.

Foundation Support

国家自然科学基金资助项目(61071167,61501249)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.01.0017
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 7
Section: System Development & Application
Pages: 2098-2103
Serial Number: 1001-3695(2019)07-039-2098-06

Publish History

[2019-07-05] Printed Article

Cite This Article

王凤琴, 卢官明, 柯亨进, 等. 基于跨层全连接神经网络的癫痫发作期识别 [J]. 计算机应用研究, 2019, 36 (7): 2098-2103. (Wang Fengqin, Lu Guanming, Ke Hengjin, et al. Epileptic EEG identification with cross layer fully connected neural network [J]. Application Research of Computers, 2019, 36 (7): 2098-2103. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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