ZHANG Kexu,DU Changwang,ZHAO Haoqi,et al.Analysis of Brain Microstates in Patients with Focal Epilepsy[J].Journal of Xi'an Jiaotong University,2020,54(09):157-163.[doi:10.7652/xjtuxb202009018]





Analysis of Brain Microstates in Patients with Focal Epilepsy
张克旭1 杜昌旺2 赵浩淇1 李扩2 张益榕1 王畅1 刘晓芳2 闫相国1 王刚1
1.西安交通大学生物医学信息工程教育部重点实验室, 710049, 西安; 2.西安交通大学第一附属医院神经外科, 710061, 西安
ZHANG Kexu1 DU Changwang 2 ZHAO Haoqi1 LI Kuo2 ZHANG Yirong1 WANG Chang1 LIU Xiaofang2 YAN Xiangguo1 WANG Gang 1
1. Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi’an Jiaotong University, Xi’an 710049, China; 2. Department of Neurosurgery, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
癫痫 发作间期 脑电信号 微状态 分形分析
epilepsy interictal phase electroencephalography microstate fractal analysis
针对脑电的癫痫神经机制研究仅限于致痫灶相关的局部脑区,提出利用脑电微状态从全局角度揭示癫痫发病的神经机制。使用修正K均值聚类算法提取了癫痫患者发作间期的特征微状态,并与连续脑电信号配对得到微状态序列; 计算每种微状态的平均持续时间、总占比、每秒出现次数,对比癫痫患者和正常人的参数相对趋势差异; 利用小波变换的方法分析癫痫患者微状态序列的分形特性。研究发现:癫痫患者的微状态D的平均持续时间为(101.42±22.91)ms、在整个时间序列上的总占比为(30.86±9.79)%、出现频率为(3.10±0.94)次1 s,数值结果显著大于微状态A、B,和微状态C相近,和正常人趋势不同; 在32 ms~16 s的时间尺度上,分形分析的结果显示癫痫脑电微状态序列的赫斯特指数为0.643 5±0.010 2,具有分形特性。微状态D的参数异常解释了癫痫患者易出现注意力缺失的现象; 癫痫微状态参数相对趋势变化对癫痫疾病诊断有参考意义; 癫痫微状态序列的分形性质表明了序列在很大的时间范围内具有自相似、无标度的特性,使微状态序列具有研究癫痫发病机制和预测癫痫发作的潜力。
Current study of the neural mechanism of epilepsy based on electroencephalography(EEG)is limited to the epilepsy-related local brain regions. This study uses microstate to reveal the cerebral neural mechanism of epilepsy from a global perspective. The modified K-means algorithm is used to cluster typical microstates of epileptic patients, and the sequence of the microstates is obtained by fitting the microstates with the continuous EEG signals. The duration, coverage and occurrence are calculated to compare the relative trend difference of parameters between patients and healthy people. Then wavelet transform is employed to test the fractal characteristics of EEG microstates sequence. Results show that the mean duration of epileptic patients’ microstate D is(101.42±22.91)ms, the proportion of microstate D in the entire time series is(30.86±9.79)%, and the occurrence of microstate D is(3.10±0.94)times per second. The values of the parameters of microstate D are significantly larger than those of microstate A and microstate B, and close to those of microstate C, which is different from the healthy people. In the time range from 32 ms to 16 s, the Hurst parameter of microstate sequence is 0.643 5±0.010 2, so the sequence possesses the fractal property. It is concluded that the anomaly of the parameters of microstate D reveals epileptic patients being prone to attention deficit, and the change of the parameters of microstate is helpful for the diagnosis of epilepsy. The fractal property of the epilepsy microstate sequence indicates that the microstate sequence has self-similarity and scale-free characteristics in a wide time range, so the microstate sequence has the potential in the pathogenesis study of epilepsy and the prediction of its seizures.


[1] TAN C T. Recent advances in epilepsy [J]. Singapore Medical Journal, 1998, 39(4): 141-142.
[2] FILATOVA O G, YANG Y, DEWALD J P A, et al. Dynamic information flow based on EEG and diffusion MRI in stroke: a proof-of-principle study [J]. Front Neural Circuits, 2018, 12: 79.
[3] WANG Gang, REN Doutian. Effect of brain-to-skull conductivity ratio on EEG source localization accuracy [J]. Biomed Research International, 2013, 2013: 459346.
[4] WANG Dong, REN Doutian, LI Kuo, et al. Epileptic seizure detection in long-term EEG recordings by using wavelet-based directed transfer function [J]. IEEE Transactions on Biomedical Engineering, 2018, 65(11): 2591-2599.
[5] WANG Gang, REN Doutian, LI Kuo, et al. EEG-based detection of epileptic seizures through the use of a directed transfer function method [J]. IEEE Access, 2018, 6: 47189-47198.
[6] WANG Gang, SUN Zhongjiang, TAO Ran, et al. Epileptic seizure detection based on partial directed coherence analysis [J]. IEEE Journal of Biomedical and Health Informatics, 2015, 20(3): 873-879.
[7] SHARMILA A. Epilepsy detection from EEG signals: a review [J]. Journal of Medical Engineering and Technology, 2018, 42(5): 368-380.
[8] BRODBECK V, KUHN A, VON WEGNER F, et al. EEG microstates of wakefulness and NREM sleep [J]. Neuroimage, 2012, 62(3): 2129-2139.
[9] KOENIG T, PRICHEP L, LEHMANN D, et al. Millisecond by millisecond, year by year: normative EEG microstates and developmental stages [J]. Neuroimage, 2002, 16(1): 41-48.
[10] CANTERO J L, ATIENZA M, SALAS R M, et al. Brain spatial microstates of human spontaneous alpha activity in relaxed wakefulness, drowsiness period, and REM sleep [J]. Brain Topography, 1999, 11: 257-263.
[11] GRIEDER M, KOENIG T, KINOSHITA T, et al. Discovering EEG resting state alterations of semantic dementia [J]. Clin Neurophysiol, 2016, 127(5): 2175 -2181.
[12] RIEGER K, DIAZ HERNANDEZ L, BAENNINGER A, et al. 15 years of microstate research in schizophrenia: where are we? a meta-analysis [J]. Front Psychiatry, 2016, 7: 22.
[13] YOSHIMURA M, KOENIG T, IRISAWA S, et al. A pharmaco-EEG study on antipsychotic drugs in healthy volunteers [J]. Psychopharmacology, 2007, 191(4): 995-1004.
[14] RAJ V K S R S, SUJAS B, ET A L. Machine learning detects EEG microstate alterations in patients living with temporal lobe epilepsy [J]. Seizure: European Journal of Epilepsy, 2018, 61: 8-13.
[15] GARTNER M, BRODBECK V, LAUFS H, et al. A stochastic model for EEG microstate sequence analysis [J]. Neuroimage, 2015, 104: 199-208.
[16] VAN DE VILLE D, BRITZ J, MICHEL C M. EEG microstate sequences in healthy humans at rest reveal scale-free dynamics [J]. Proceedings of the National Academy of Ences, 2010, 107(42): 18179-18184.
[17] PASCUAL-MARQUI R D, MICHEL C M, LEHMANN D. Segmentation of brain electrical activity into microstates: model estimation and validation [J]. IEEE Transactions on Biomedical Engineering, 1995, 42(7): 658-665.
[18] MICHEL C M, KOENIG T. EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: a review [J]. Neuroimage, 2018, 180: 577-593.
[19] DAUBECHIES I. Ten lectures on wavelets [J]. Computers in Physics, 1992, 93(3): 1671-1671.
[20] BRITZ J, VAN DE VILLE D, MICHEL C M. BOLD correlates of EEG topography reveal rapid resting-state network dynamics [J]. Neuroimage, 2010, 52(4): 1162-1170.
[21] PEREGRINE M K, TARA C, LISA J S, et al. Attention impairment in rolandic epilepsy: systematic review [J]. Epilepsia, 2008, 49(9): 1570-1580.
[22] LUNN J, DONOVAN T, LITCHFIELD D, et al. Social attention in children with epilepsy [J]. Brain and Cognition, 2017, 113: 76-84.
[23] LEHMANN D, FABER P L, GALDERISI S, et al. EEG microstate duration and syntax in acute, medication-naive, first-episode schizophrenia: a multi-center study [J]. Psychiatry Research, 2005, 138(2): 141-156.


 Wang Jing,Xu Guanghua,Zhang Qing.Epileptic Seizure Prediction Method Based on Empirical Mode Decomposition and Kolmogorov Complexity[J].Journal of Xi'an Jiaotong University,2007,41(09):1364.[doi:10.7652/xjtuxb200711026]


收稿日期: 2020-01-11。作者简介: 张克旭(1998—),男,本科生; 王刚(通信作者),男,副教授。基金项目: 国家自然科学基金资助项目(315710000); 陕西省自然科学基础研究计划资助项目(2020JM-037)。
更新日期/Last Update: 2020-09-10