[1]于永强,刘健,孙仁诚,等.基于多特征多关系图卷积神经网络的癫痫脑电分类[J].中国医学物理学杂志,2023,40(5):595-601.[doi:DOI:10.3969/j.issn.1005-202X.2023.05.012]
　YU Yongqiang,LIU Jian,SUN Rencheng,et al.Classification of epileptic electroencephalogram signal using graph convolutional neural network with multiple features and multiple relations[J].Chinese Journal of Medical Physics,2023,40(5):595-601.[doi:DOI:10.3969/j.issn.1005-202X.2023.05.012]
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基于多特征多关系图卷积神经网络的癫痫脑电分类()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第5期
页码:
595-601
栏目:
医学信号处理与医学仪器
出版日期:
2023-05-26

文章信息/Info

Title:
Classification of epileptic electroencephalogram signal using graph convolutional neural network with multiple features and multiple relations
文章编号:
1005-202X（2023）05-0595-07
作者:
于永强1; 刘健2; 孙仁诚1; 隋毅1
1.青岛大学计算机科学技术学院， 山东 青岛 266071； 2.青岛市口腔医院， 山东 青岛 266000
Author(s):
YU Yongqiang1;  LIU Jian2;  SUN Rencheng 1;  SUI Yi 1
1. College of Computer Science and Technology, Qingdao University, Qingdao 266071, China 2. Qingdao Stomatological Hospital, Qingdao 266000, China
关键词:
脑电图; 癫痫; 图卷积神经网络; 多关系图
Keywords:
Keywords: electroencephalogram epilepsy graph convolutional neural network multiple-relation graph
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2023.05.012
文献标志码:
A
摘要:
目的：提出一种基于多特征多关系图卷积神经网络的癫痫脑电分类方法，改进图卷积神经网络在癫痫脑电分类领域的应用，提升分类准确率。方法：分别提取癫痫脑电信号的1个频域特征、3个时频域特征和2个非线性动力学特征作为模型节点的特征。提取脑电通道之间的空间相似性和频谱相似性，融合两种通道相似性作为整体图节点之间的边关系矩阵。结果：在TUEP数据集上进行实验，准确率、精确率、召回率、F1分数、AUC结果分别为：0.87±0.02、0.91±0.04、0.82±0.04、0.86±0.02、0.90±0.03。结论：提出的模型与单特征和单关系的图卷积神经网络相比，对于癫痫脑电分类的提升效果明显。
Abstract:
Abstract: Objective To present a graph convolutional neural network (GCNN) based on multi-feature and multi-relations for the electroencephalogram (EEG) signal classification in epilepsy for promoting the application of GCNN in epileptic EEG signal classification and improving the classification accuracy. Methods One frequency domain feature, 3 time frequency domain features and 2 nonlinear dynamical features of epileptic EEG signals were taken as features of model nodes. The spatial similarity and spectral similarity between EEG channels were extracted and fused as the edge relationship matrix between the overall graph nodes. Results Experiments were conducted on the TUEP dataset to evaluate the classification performance. The proposed model achieved an accuracy, precision, recall, F1-score, and AUC of 0.87±0.02, 0.91±0.04, 0.82±0.04, 0.86±0.02, and 0.90±0.03, respectively. Conclusion The proposed model is advantageous over the single-feature and single-relation GCNN in the classification of epileptic EEG signal.

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备注/Memo

备注/Memo:
【收稿日期】2022-12-10 【基金项目】国家自然科学青年基金项目（41706198）；青岛市自主创新重大专项子课题（21-1-2-1hy） 【作者简介】于永强，硕士研究生，研究方向：机器学习和深度学习，E-mail: yongqiang169@foxmail.com 【通信作者】隋毅，博士，副教授，研究方向：大数据分析、机器学习，E-mail: suiyi@qdu.edu.cn
更新日期/Last Update: 2023-05-26