Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph

doi:10.1088/1674-1056/acdfbf

中国物理B ›› 2023, Vol. 32 ›› Issue (11): 110506-110506.doi: 10.1088/1674-1056/acdfbf

Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph

Lu Ma(马璐)^1,2, Yan-Lin Ren(任彦霖)³, Ai-Jun He(何爱军)⁴, De-Qiang Cheng(程德强)¹, and Xiao-Dong Yang(杨小冬)^3,†

1 School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China;
2 Suzhou Vocational and Technical College, Suzhou 234000, China;
3 School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;
4 School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China

收稿日期:2023-01-17 修回日期:2023-06-07 接受日期:2023-06-20 出版日期:2023-10-16 发布日期:2023-10-24
通讯作者: Xiao-Dong Yang E-mail:xyang@cumt.edu.cn
基金资助:
Project supported by the Xuzhou Key Research and Development Program (Social Development) (Grant No. KC21304) and the National Natural Science Foundation of China (Grant No. 61876186).

Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph

Lu Ma(马璐)^1,2, Yan-Lin Ren(任彦霖)³, Ai-Jun He(何爱军)⁴, De-Qiang Cheng(程德强)¹, and Xiao-Dong Yang(杨小冬)^3,†

1 School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China;
2 Suzhou Vocational and Technical College, Suzhou 234000, China;
3 School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;
4 School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China

Received:2023-01-17 Revised:2023-06-07 Accepted:2023-06-20 Online:2023-10-16 Published:2023-10-24
Contact: Xiao-Dong Yang E-mail:xyang@cumt.edu.cn
Supported by:
Project supported by the Xuzhou Key Research and Development Program (Social Development) (Grant No. KC21304) and the National Natural Science Foundation of China (Grant No. 61876186).

摘要/Abstract

摘要： Electroencephalogram (EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the horizontal visibility graph (HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the weighted horizontal visibility graph (WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations, they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis (MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free intervals. Furthermore, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase, and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of the WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.

关键词: epilepsy, EEG signal, horizontal visibility graph, complex network

Abstract: Electroencephalogram (EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the horizontal visibility graph (HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the weighted horizontal visibility graph (WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations, they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis (MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free intervals. Furthermore, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase, and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of the WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.

Key words: epilepsy, EEG signal, horizontal visibility graph, complex network

中图分类号: (Computational methods in statistical physics and nonlinear dynamics)

05.10.-a

87.18.-h (Biological complexity) 87.85.Ng (Biological signal processing) 87.19.X- (Diseases)

Lu Ma(马璐), Yan-Lin Ren(任彦霖), Ai-Jun He(何爱军), De-Qiang Cheng(程德强), and Xiao-Dong Yang(杨小冬). Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph[J]. 中国物理B, 2023, 32(11): 110506-110506.

参考文献

[1] Geneva:World Health Organization 2019 Global Epilepsy Report Licence CC BY-NC-SA 3.0 IGO
[2] Mei Z N, Zhao X, Chen H Y and Chen W 2018 Sensors 18 1720
[3] Samiee K, Kovacs P and Gabbouj M 2015 IEEE Trans. Biomed Eng. 62 541
[4] Kiymik M K 2005 Comput. Biol. Med. 35 603
[5] Kumar Y, Dewal M L and Anand R S 2014 Neurocomputing 133 271
[6] Sanchez M A, Castanon-Puga M, Aguilar L and Rodriguez-Diaz A 2018 Computer Science and Engineering-Theory and Applications (Springer) p. 167
[7] Azami H A, Fernandez and Escudero J 2017 Med. Biol. Eng. Comput. 55 2037
[8] Wang S, Chaovalitwongse W A and Wong S 2013 IEEE Transactions on Knowledge and Data Engineering 25 2854
[9] Shayegh F 2014 Computer Methods and Programs in Biomedicine 113 323
[10] Sikdar D, Roy R and Mahadevappa M 2018 Biomedical Signal Processing and Contro 41 264
[11] Lacasa L 2008 Proc. Natl. Acad. Sci. USA 105 4972
[12] Luque B 2009 Phys. Rev. E 80 046103
[13] Zou Y 2019 Phys. Rep. 787 1
[14] Zhu G, Li Y and Wen P 2014 Computer Methods and Programs in Biomedicine 115 64
[15] Bhaduri S and Ghosh D 2015 Clin. EEG Neurosci 46 218
[16] Supriya S 2016 IEEE Access 4 6554
[17] Mohammadpoory Z, Nasrolahzadeh M and Haddadnia J 2017 Seizure 50 202
[18] Song C, Havlin S and Makse H A 2005 Nature 433 392
[19] Yu Z G, Zhang H, Huang D W, Lin Y and Anh V 2016 J. Stat. Mech-Theorey E 3 033206
[20] Furuya S and Yakubo K 2011 Phys. Rev. E 84 036118
[21] Wang D L, Yu Z G and Anh V 2012 Chin. Phys. B 21 080504
[22] Liu J L, Yu Z G and Anh V 2015 Chaos 25 023103
[23] Tél T, Fülöp Á and Vicsek T 1989 Physica A 159 155
[24] Pavón-Domínguez P and Moreno-Pulido S 2020 Physica A 541 123670
[25] Pavon-Dominguez P and Moreno-Pulido S Chaos, Solitons and Fractals 156 111836
[26] Gieral towski J, Żebrowski J J and Baranowski R 2012 Phys. Rev. E 85 021915
[27] Andrzejak R.G 2001 Phys. Rev. E 64 061907
[28] Sidorov S, Faizliev A and Balash V 2018 IAENG Int. J. Appl. Math. 48 90
[29] Diykh M, Li Y and Wen P 2017 Expert Systems with Applications 90 87
[30] Song Y and Lió P 2010 Journal of Biomedical Science and Engineering 03 556
[31] Orhan U, Hekim M and Ozer M 2011 Expert Systems with Applications 38 13475
[32] Guo L 2011 Expert Systems with Applications 38 10425
[33] Martis R J 2012 Int. J. Neural Syst. 22 1250027
[34] Martis R J 2013 Int. J. Neural Syst. 23 1350023
[35] Kaya Y 2014 Appl. Math. Comput. 243 209
[36] Abdulhay E 2020 Pattern Recognition Letters 139 174
[37] Liu Y, Jiang B, Feng J, Hu J Z and Zhang H B 2021 Multimed Tools Appl. 80 30261
[38] Liu H 2022 Mathematical Biosciences and Engineering 19 624

Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph

Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	Dan Chen(陈单), Defu Cai(蔡德福), and Housheng Su(苏厚胜). Self-similarity of complex networks under centrality-based node removal strategy[J]. 中国物理B, 2023, 32(9): 98903-098903.
[2]	Jia-Hui Song(宋家辉). Important edge identification in complex networks based on local and global features[J]. 中国物理B, 2023, 32(9): 98901-098901.
[3]	Huang-Jing Ni(倪黄晶), Ruo-Yu Du(杜若瑜), Lei Liang(梁磊), Ling-Ling Hua(花玲玲), Li-Hua Zhu(朱丽华), and Jiao-Long Qin(秦姣龙). Two-dimensional horizontal visibility graph analysis of human brain aging on gray matter[J]. 中国物理B, 2023, 32(7): 78501-078501.
[4]	Duolan(朵兰), Linying Xiang(项林英), and Guanrong Chen(陈关荣). Synchronization of stochastic complex networks with time-delayed coupling[J]. 中国物理B, 2023, 32(6): 60502-060502.
[5]	Yun Zhai(翟云), Xuan Wang(王璇), Jinghua Xiao(肖井华), and Zhigang Zheng(郑志刚). Stability and multistability of synchronization in networks of coupled phase oscillators[J]. 中国物理B, 2023, 32(6): 60503-060503.
[6]	Zijian Yan(严子健), Yongxiang Xia(夏永祥), Lijun Guo(郭丽君), Lingzhe Zhu(祝令哲), Yuanyuan Liang(梁圆圆), and Haicheng Tu(涂海程). Identification of key recovering node for spatial networks[J]. 中国物理B, 2023, 32(6): 68901-068901.
[7]	Peishi Yang(杨培实), Pengli Lu(卢鹏丽), and Teng Zhang(张腾). AG-GATCN: A novel method for predicting essential proteins[J]. 中国物理B, 2023, 32(5): 58902-058902.
[8]	Jie Zhou(周洁), Cheng Yuan(袁诚), Zu-Yu Qian(钱祖燏), Bing-Hong Wang(汪秉宏), and Sen Nie(聂森). Analysis of cut vertex in the control of complex networks[J]. 中国物理B, 2023, 32(2): 28902-028902.
[9]	Zhixuan Yuan(袁治轩), Mengmeng Du(独盟盟), Yangyang Yu(于羊羊), and Ying Wu(吴莹). Epilepsy dynamics of an astrocyte-neuron model with ammonia intoxication[J]. 中国物理B, 2023, 32(2): 20502-020502.
[10]	Da Ai(艾达), Xin-Long Liu(刘鑫龙), Wen-Zhe Kang(康文哲), Lin-Na Li(李琳娜), Shao-Qing Lü(吕少卿), and Ying Liu(刘颖). SLGC: Identifying influential nodes in complex networks from the perspectives of self-centrality, local centrality, and global centrality[J]. 中国物理B, 2023, 32(11): 118902-118902.
[11]	Hongwei Zhang(张红伟), Ran Cheng(程然), and Dawei Ding(丁大为). Quasi-synchronization of fractional-order complex networks with random coupling via quantized control[J]. 中国物理B, 2023, 32(11): 110501-110501.
[12]	Xianjia Wang(王先甲) and Tao Wang(王饕). Effect of conformity on evolution of cooperation in a coordination game[J]. 中国物理B, 2023, 32(10): 100202-100202.
[13]	Pengli Lu(卢鹏丽) and Wei Chen(陈玮). Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding[J]. 中国物理B, 2023, 32(1): 18903-018903.
[14]	Chaoyi Shi(史朝义), Qi Zhang(张琦), and Tianguang Chu(楚天广). Effect of observation time on source identification of diffusion in complex networks[J]. 中国物理B, 2022, 31(7): 70203-070203.
[15]	Jing-Cheng Zhu(朱敬成) and Lun-Wen Wang(王伦文). An extended improved global structure model for influential node identification in complex networks[J]. 中国物理B, 2022, 31(6): 68904-068904.