基于复杂网络的癫痫脑电分类与分析

doi:10.6040/j.issn.1672-3961.0.2016.279

山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (3): 8-15.doi: 10.6040/j.issn.1672-3961.0.2016.279

基于复杂网络的癫痫脑电分类与分析

郝崇清^1,2,王志宏¹

1. 河北科技大学电气工程学院, 河北石家庄 050018;2. 天津大学电气与自动化工程学院, 天津 300072

收稿日期:2016-07-27 出版日期:2017-06-20 发布日期:2016-07-27
作者简介:郝崇清(1981— ),男,山东枣庄人,讲师,博士,主要研究方向为生物电信息处理与复杂网络.E-mail: haochongqing@hebust.edu.cn
基金资助:
河北省自然科学基金资助项目(F2014208013)

Classification and analysis of epileptic EEG based on complex networks

HAO Chongqing^1,2, WANG Zhihong¹

1.School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, Hebei, China;
2. School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China

Received:2016-07-27 Online:2017-06-20 Published:2016-07-27

摘要/Abstract

摘要： 为提取癫痫发作与间歇期脑电信号的特征,提出利用构建癫痫EEG(electroencephalogram)网络的方法来刻画脑电信号。研究各变量均可测情况下的Lorenz和Rössler混沌系统,利用其各变量的输出混沌时间序列构建复杂网络,发现构建的复杂网络拓扑图与其混沌吸引子存在形态相似性,说明由时间序列构建的复杂网络能刻画其原信号特征。对于多维系统中仅有一维可测时,多维时间序列由相空间重构得到。利用相空间重构方法对癫痫发作和间歇期脑电信号构建复杂网络进行分析。研究结果表明,癫痫发作时其网络拓扑较间歇期存在明显不同,且其平均路径长度显著增加,而递归率及其波动范围都显著降低,这些网络特性可以用来刻画脑电信号的特征,从而为癫痫疾病的自动辨识与预测提供基础。

关键词: 平均路径长度, 网络拓扑, 递归率, 癫痫脑电, 复杂网络, 形态相似性

Abstract: To extract epileptic EEG features in the ictal and interictal period, a method of depicting epileptic EEG was proposed by transforming epileptic EEG time series to epileptic networks. Chaotic multi-dimensional time series coming from the Lorenz system and Rössler system were used to construct a complex network,in which all the variables could be measured. It was found that there was morphological similarity between topology of the complex networks and the attractor of chaotic system. This indicated that complex networks constructed from time series could depict the characteristics of the original signals. For only one measureable variable, multi-dimensional time series were obtained by reconstruction of the phase space. Therefore, the epileptic EEG network was constructed and analyzed in the ictal and interictal period. The results showed that epileptic EEG network topologies in the ictal period were significantly different from that in the interictal period. Meanwhile, the average path length of the network increased significantly and recurrence rates decreased significantly in the ictal period comparing to in the interictal period. These network features could be used to depict the characteristics of EEG time series and could provide the basis for epilepsy automatic identification and prediction.

Key words: morphological similarity, average path length, network topology, complex networks, recurrence rates, epileptic EEG

中图分类号:

TN911.7

郝崇清,王志宏. 基于复杂网络的癫痫脑电分类与分析[J]. 山东大学学报(工学版), 2017, 47(3): 8-15.

HAO Chongqing, WANG Zhihong. Classification and analysis of epileptic EEG based on complex networks[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(3): 8-15.

参考文献

[1] WATTS D J, STROGATZ S H. Collective dynamics of ‘small-world’ networks[J]. Nature, 1998, 393(6684):440-442.
[2] BARABÁSI A L, ALBERT R. Emergence of scaling in random networks[J]. Science, 1999, 286(5439):509-512.
[3] WANG Q Y, MURKS A, PERC M, et al. Taming desynchronized bursting with delays in the Macaque cortical network[J]. Chin Phys B, 2011, 20(4):040504.
[4] BULLMORE E, SPORNS O. Complex brain networks: graph theoretical analysis of structural and functional systems[J]. Nat Rev Neurosci, 2009, 10:186-198.
[5] HAN F, LU Q S, WIERCIGROCH M, et al. Complete and phase synchronization in a heterogeneous small-world neuronal network[J]. Chin Phys B, 2009, 18(2):482-488.
[6] 刘宗华. 基于复杂网络的信号检测与传递[J]. 中国科学: 物理学力学天文学, 2014, 44(12):1334-1343. LIU Zonghua. Signal detection and transmission in complex networks[J]. Scientia Sinica: Physica, Mechanica & Astronomica, 2014, 44(12):1334-1343.
[7] BATISTA C A S, LOPES S R, VIANA R L, et al. Delayed feedback control of bursting synchronization in a scale-free neuronal network[J]. Neural Networks, 2009, 23:114-124.
[8] LEHNERTZA K, BIALONSKIA S, HORSTMANNA M T, et al. Synchronization phenomena in human epileptic brain networks[J]. Journal of Neuroscience Methods, Journal of Neuroscience Methods, 2009, 183:42-48.
[9] 董泽芹,侯凤贞,戴加飞,等. 基于Kendall改进的同步算法癫痫脑网络分析[J]. 物理学报, 2014, 63(20):208705. DONG Zeqin, HOU Fengzhen, DAI Jiafei, et al. An improved synchronous algorithm based on Kendall for analyzing epileptic brain network[J]. Acta Phys Sin, 2014, 63(20):208705.
[10] 刘莹莹,禚钊,蔡世民,等. 基于分层同步的脑结构和功能网络关系研究[J]. 中国科技大学学报, 2014, 44(1): 43-47. LIU Yingying, ZHUO Zhao, CAI Shimin, et al. The relationship of structural and functional brain networks via hierarchical synchronization[J]. Journal of University of Science and Technology of China, 2014, 44(1): 43-47.
[11] LI L, JIN Z L, LI B. Analysis of a phase synchronized functional network based on the rhythm of brain activities[J]. Chin Phys B, 2011, 20(3):038701.
[12] ZHANG J, SMALL M. Complex network from pseudoperiodic time series: topology versus dynamics[J]. Physical Review Letters, 2006, 96(23):238701.
[13] ZHANG J, SUN J F, LUO X D, et al. Characterizing pseudoperiodic time series through the complex network approach[J]. Physica D, 2008, 237:2856-2865.
[14] MARWAN N, DONGES J F, ZOU Y, et al. Complex network approach for recurrence analysis of time series[J]. Physics Letters A, 2009, 373(46):4246-4254.
[15] DONNER R V, ZOU Y, DONGES J F, et al. Recurrence networks-a novel paradigm for nonlinear time series analysis[J]. New Journal of Physics, 2010, 12(3): 033025.
[16] XU X K, ZHANG J, SMALL M. Superfamily phenomena and motifs of networks induced from time series[J]. Proc Natl Acad Sci USA, 2008 105(50): 19601.
[17] YANG Y, YANG H J. Complex network-based time series analysis[J]. Physica A, 2008, 387: 1381-1386.
[18] LACASA L, LUQUE B, FERNANDO B F, et al. From time series to complex networks: the visibility graph[J]. Proc Natl Acad Sci USA, 2008, 105(13): 4972-4975.
[19] QI J C, WANG J Y, WANG J B, et al. Visibility graphs for time series containing different components[J]. Fluctuation and Noise Letters, 2011, 10(4): 371-379.
[20] SHAO Z G. Network analysis of human heartbeat dynamics[J]. Applied Physics Letters, 2010, 96:073703.
[21] LUQUE B, LACASA L, BALLESTEROS F J, et al. Feigenbaum graphs: a complex network perspective of chaos[J]. PLOS ONE, 2011, 6(9): e22411.
[22] LUQUE B, LACASA L, BALLESTEROS F J. Horizontal visibility graphs: exact results for random time series[J]. Physical Review E, 2009, 80(4): 046103.
[23] 周婷婷,金宁德,高忠科,等. 基于有限穿越可视图的时间序列网络模型[J]. 物理学报, 2012, 61(3): 030506. ZHOU Tingting, JIN Ningde, GAO Zhongke, et al. Limited penetrable visibility graph for establishing complex network from time series[J]. Acta Phys Sin, 2012, 61(3): 030506.
[24] 王若凡,刘静,王江,等. 基于功率谱及有限穿越可视图的癫痫脑电信号分析算法[J]. 计算机应用, 2017, 37(1): 175-182. WANG Ruofan, LIU Jing, WANG Jiang, et al. Analysis algorithm of electroencephalogram signals for epilepsy diagnosis based on power spectral density and limited penetrable visibility graph[J]. Journal of Computer Applications, 2017, 37(1): 175-182.
[25] GAO Zhongke, JIN Ningde. Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks[J]. Physical Review E, 2009, 79(6): 066303.
[26] GAO Zhongke, JIN Ningde. Complex network from time series based on phase space reconstruction[J]. Chaos, 2009, 19(3): 033137.
[27] KIM H S, EYKHOLT R, SALAS J D. Nonlinear dynamics, delay times, and embedding windows[J]. Physica D, 1999, 127: 48-60.
[28] CAO L Y. Practical method for determining the minimum embedding dimension of a scalar time series[J]. Physica D, 1997, 110: 43-50.
[29] ANDRZEJAK R G, LEHNERTZ K, MORMANN F, et al. Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: dependence on recording region and brain state[J]. Physical Review E, 2001, 64(6): 061907.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed

基于复杂网络的癫痫脑电分类与分析

Classification and analysis of epileptic EEG based on complex networks

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

多维度评价

本文评价

推荐阅读 0

[1]	黄成凯,杨浩,姜斌,程舒瑶. 一类复杂网络的协同容错控制[J]. 山东大学学报(工学版), 2017, 47(5): 203-209.
[2]	张玉婷,李望,王晨光,刘友权,侍红军. 不连续耦合的时滞复杂动态网络的同步[J]. 山东大学学报(工学版), 2017, 47(4): 43-49.
[3]	李望,马志才,侍红军. 时滞复杂动态网络的有限时间随机广义外部同步[J]. 山东大学学报(工学版), 2017, 47(3): 1-8.
[4]	褚晓东,张荣祥,黄昊怡,唐茂森. 全球能源互联网物理-信息系统协同仿真平台[J]. 山东大学学报(工学版), 2016, 46(4): 103-110.
[5]	李望1,2,石咏2,马继伟2. 复杂动力学网络的有限时间外部同步[J]. 山东大学学报(工学版), 2013, 43(2): 48-53.
[6]	赵永清, 江明辉. 混合变时滞二重边复杂网络自适应同步反馈控制[J]. 山东大学学报(工学版), 2010, 40(3): 61-68.