癫痫发作脑电信号的相位幅度调制研究

doi:10.3969/j.issn.0258-8021. 2018. 01.005

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (840 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract EEG is the most important tool in the diagnosis and treatment of epilepsy, and the analysis of large data volume of EEG records has brought considerable difficulties, which may be overcome by the automatic computer assistant classification tools. In this paper, the coupling between the phase of low frequency and the amplitude of high frequency was studied from the perspective of phase amplitude modulation, and the normalized modulation index (MI) was further used to quantify the coupling intensity in different frequency pairs. The Bonn datasets with total 200 samples of interictal and ictal EEG segment were used in this study. A method of partitioning the MI map according to the ranges of high and low frequency rhythms was proposed. Results have shown that the MI value between Gamma and Delta (2～4 Hz) rhythm was significantly increased (P<0.01) from the interictal state (0.0036±0.0087) to the ictal state (0.0099±0.0096). The MI value of Theta (4～8 Hz) Gamma was significantly increased (P<0.01) from the interictal state (0.0014±0.0032) to ictal state (0.0087±0.0062). Modulation strength of Theta Beta rhythm also increased significantly from interictal state (0.0005±0.0007) to ictal state (0.0022±0.0013). The classification accuracy based on MI characteristics was 97% for the distinguishing the ictal data, and the accuracy was not changed much by using support vector machine with five folder cross validation and random forest. The using of proposed method would greatly improve the efficiency of clinical video EEG analysis for determining the ictal state.

Key words： epilepsy EEG phase amplitude coupling classification modulation index

Received: 10 April 2017

PACS:

R318

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Cao Chunyu
	Han Ling
	Li Chunsheng

Cite this article:

Cao Chunyu,Han Ling,Li Chunsheng. Research on EEG Phase Amplitude Modulation during Epileptic Seizure[J]. Chinese Journal of Biomedical Engineering, 2018, 37(1): 33-39.

URL:

http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021. 2018. 01.005 OR http://cjbme.csbme.org/EN/Y2018/V37/I1/33

[1] Jensen O, Colgin LL. Cross-frequency coupling between neuronal oscillations [J]. Trends Cogn Sci, 2007,11(7): 267-269.
[2] Belluscio MA, Mizuseki K,Schmidt R, et al. Across frequency phase-phase coupling between theta and gamma oscillations in the hippocampus [J]. J Neuroscience, 2012, 32(2): 423-435.
[3] Guirgis M, Chinvarun Y, Del Campo M, et al. Defining regions of interest using cross-frequency coupling in extratemporal lobe epilepsy patients [J]. J Neural Eng, 2015, 26(11): 1-19.
[4] Li Chunsheng, Jacobs D, Hilton T, et al. Epileptogenic source imaging using cross-frequency coupled signals from scalp EEG [J]. IEEE Trans Biomed Eng, 2016, 63(12): 2607-2618.
[5] Amri M, Frauscher B, Gotman J. Phase-Amplitude coupling is elevated in deep sleep and in the onset zone of focal epileptic seizures [J]. Front Hum Neurosci, 2016, 10: 1-11.
[6] Ubeyli ED. Combined neural network model employing wavelet coefficients for EEG signals classification [J]. Digital Signal Processing, 2009, 19(2): 297-308.
[7] 宦飞,郑崇勋,黄远桂.基于时频分布的EEG中棘波的相关检测[J]. 仪器仪表学报, 1999,20(5): 446-450.
[8] 李春胜,王宏.脑电Alpha波的混沌关联维数分析[J]. 仪器仪表学报, 2009, 30(3): 477-480.
[9] Mohseni HR, Maghsoudi A, Kadbi MH, et al. Automatic detection of epileptic seizure using time-frequency distributions[C]//IET 3rd International Conference MEDSIP 2006. IEEE, 2006:29.
[10] 崔林阳,田心.基于颞叶癫痫脑电相位互相关分析法的癫痫超同步放电的初步研究[J]. 中华神经医学杂志, 2008,7(3): 219-222.
[11] 王杰,李牧潇.改进的极限学习机在癫痫脑电分类中的应用[J].计算机仿真, 2014, 31(6): 343-347.
[12] 张涛,陈万忠,李明阳. 基于频率切片小波变换和支持向量机的癫痫脑电信号自动检测 [J]. 物理学报, 2016, 65(3): 038703.
[13] 袁琦,周卫东,李淑芳,等.基于ELM和近似熵的脑电信号检测方法[J]. 仪器仪表学报, 2012, 33(3): 514-519.
[14] Andrzejak RG, 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: 061907.
[15] Tort AB, Komorowski R, Eichenbaum H, et al. Measuring phase-amplitude coupling between neuronal oscillations of different frequencies [J]. J Neurophysiol, 2010, 104(2): 1195-1210.
[16] Kullback S. The Kullback-Leibler distance [J]. Am Statist Assoc, 1987, 41: 340-341.
[17] Chang CC, Lin CJ. A library for support vector machines [J]. ACMTIST, 2011, 2(3):1-27.
[18] Canolty RT, Edwards E, Dalal SS, et al. High gamma power is phase-locked to theta oscillations in human neocortex [J]. Science, 2006, 313(5793): 1626-1628.
[19] Nonoda Y, Miyakoshi M, Ojeda A, et al. Interictal high-frequency oscillations generated by seizure onset and eloquent areas may be differentially coupled with different slow waves [J]. Clin Neurophysiol, 2016, 127(6): 2489-2499.
[20] 李红利.癫痫脑电信号的非线性分析 [D]. 天津: 天津大学, 2012.
[21] Ren L, Kucewicz MT, Cimbalnik J, et al. Gamma oscillations precede interictalepileptiform spikes in the seizure onset zone [J]. Neurology, 2015, 84(6): 602-608.
[22] Brown P, Oliviero A, Mazzine P, et al. Dopamine dependency of oscillations between subthalamic nucleus and pallidum in parkinson′s disease [J]. J Neurosci, 2001, 21(3): 1033-1038.