发表状态 | 已发表Published |
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
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ISSN/eISSN | 1662-5188 |
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 |
DOI | 10.3389/fncom.2023.1211096 |
URL | 查看来源 |
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 | |
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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