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题名Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages
作者
发表日期2023
发表期刊Frontiers in Computational Neuroscience
ISSN/eISSN1662-5188
卷号17
摘要

Introduction: The automatic precision detection technology based on electroencephalography (EEG) is essential in epilepsy studies. It can provide objective proof for epilepsy diagnosis, treatment, and evaluation, thus helping doctors improve treatment efficiency. At present, the normal and acute phases of epilepsy can be well identified through EEG analysis, but distinguishing between the normal and chronic phases is still tricky.

Methods: In this paper, five popular complexity indicators of EEG signal, including approximate entropy, sample entropy, permutation entropy, fuzzy entropy and Kolmogorov complexity, are computed from rat hippocampi to characterize the normal, acute, and chronic phases during epileptogenesis. Results of one-way ANOVA and principal component analysis both show that utilizing complexity features, we are able to easily identify differences between normal, acute, and chronic phases. We also propose an innovative framework for epilepsy detection based on graph convolutional neural network (GCNN) using multi-channel EEG complexity as input.

Results: Combining information of five complexity measures at eight channels, our GCNN model demonstrate superior ability in recognizing the normal, acute, and chronic phases. Experiments results show that our GCNN model reached the high prediction accuracy above 98% and F1 score above 97% among these three phases for each individual rat.

Discussion: Our research practice based on real data shows that EEG complexity characteristics are of great significance for recognizing different stages of epilepsy.

关键词chronic stage EEG complexity measures entropy epilepsy diagnosis graph convolutional neural network
DOI10.3389/fncom.2023.1211096
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收录类别SCIE
语种英语English
WOS研究方向Mathematical & Computational Biology ; Neurosciences & Neurology
WOS类目Mathematical & Computational Biology ; Neurosciences
WOS记录号WOS:001094693100001
Scopus入藏号2-s2.0-85174187621
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/12773
专题理工科技学院
通讯作者Peng, Xiaoling
作者单位
1.Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science,BNU-HKBU United International College,Zhuhai,China
2.Department of Neurology,Children's Hospital of Chongqing Medical University,Chongqing,China
第一作者单位北师香港浸会大学
通讯作者单位北师香港浸会大学
推荐引用方式
GB/T 7714
Zheng, Shiming,Zhang, Xiaopei,Song, Panpanet al. Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages[J]. Frontiers in Computational Neuroscience, 2023, 17.
APA Zheng, Shiming, Zhang, Xiaopei, Song, Panpan, Hu, Yue, Gong, Xi, & Peng, Xiaoling. (2023). Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages. Frontiers in Computational Neuroscience, 17.
MLA Zheng, Shiming,et al."Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages". Frontiers in Computational Neuroscience 17(2023).
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