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-  2018 

儿童失神癫痫发作期脑电信号子波熵分析

DOI: doi:10.7507/1001-5515.201701002

Keywords: 儿童失神癫痫, 脑电图, 子波熵

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Abstract:

本研究采用脑电信号的整体子波熵和分尺度子波熵研究脑电信号的信息复杂性,探索儿童失神癫痫(CAE)发作的动力学机制。研究采集儿童失神癫痫患者及正常对照的脑电信号;采用连续子波变换提取脑电信号的时频特征;采用子波功率谱分析提取分尺度功率谱特征;根据分尺度功率谱计算整体子波熵和分尺度子波熵,分析整体子波熵和分尺度子波熵随 CAE 发作的时间演变过程,并与正常对照进行比较。结果显示:CAE 患者发作期脑电信号的整体子波熵显著低于正常对照组,也低于发作间期。CAE 发作时第 12 尺度(对应中心频率 3 Hz)的分尺度子波熵显著高于正常对照,α 频带(中心频率 10 Hz)脑电节律的子波熵明显低于正常对照。脑电信号整体子波熵可以反映脑电信号的复杂程度,CAE 发作时脑电信号的信息复杂度明显降低。子波熵降低有可能成为癫痫发作的特征神经电生理参数,为癫痫发作的神经调控技术的研究提供依据

References

[1]  2. Inouye T, Shinosaki K, Iyama A, et al. Localization of activated areas and directional EEG patterns during mental arithmetic. Clin Neurophysiol, 1993, 86(4): 224-230.
[2]  4. Inouye T, Shinosaki K, Sakamoto H, et al. Quantification of EEG irregularity by use of the entropy of power spectrum. Electroencephalogr Clin Neurophysiol, 1991, 79(3): 204-210.
[3]  5. Yildiz A, Akin M, Poyraz M, et al. Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction. Expert Syst Appl, 2009, 36(4): 7390-7399.
[4]  7. 郁洪强, 赵欣, 詹启生, 等. 基于小波熵的网络成瘾脑电复杂性分析. 天津大学学报, 2008, 41(6): 751-756.
[5]  9. 王锋, 李晓欧. 基于脑电信号的麻醉特征参数分析. 生物医学工程学杂志, 2015, 32(1): 13-18, 31.
[6]  10. Quiroga R Q, Rosso O A, Basar E, et al. Wavelet entropy in event-related potentials: a new method shows ordering of EEG oscillations. Biol Cybern, 2001, 84(4): 291-299.
[7]  11. Yordanova J, Kolev V, Rosso O A, et al. Wavelet entropy analysis of event-related potentials indicates modality-independent theta dominance. J Neurosci Methods, 2002, 117(1): 99-109.
[8]  12. Rosso O A. Entropy changes in brain function. Int J Psychophysiol, 2007, 64(1): 75-80.
[9]  13. Emre C M, Ozgoren M, Acar S F. Continuous time wavelet entropy of auditory evoked potentials. Comput Biol Med, 2010, 40: 90-96.
[10]  18. Rosso O A, Hyslop W, Gerlach R, et al. Quantitative EEG analysis of the maturational changes associated with childhood absence epilepsy. Physica A, 2005, 356(1): 184-189.
[11]  19. Rosso O A, Martin M T, Figliola A, et al. EEG analysis using wavelet-based information tools. J Neurosci Methods, 2006, 153(2): 163-182.
[12]  20. Pereyra M E, Lamberti P W, Rosso O A. Wavelet Jensen-Shannon divergence as a tool for studying the dynamics of frequency band components in EEG epileptic seizures. Physica A, 2007, 379(1): 122-132.
[13]  21. Acharya U R, Molinari F, Sree S, et al. Automated diagnosis of epileptic EEG using entropies. Biomed Signal Process Control, 2012, 7(4): 401-408.
[14]  1. Powell C E, Perceival I C. A spectral entropy method for distinguishing regular and irregular motion of Hamiltonian systems. J Phys A, 1979, 12: 2053-2071.
[15]  3. Stam C J, Tavy D L J, Keunen R W M. Quantification of alpha rhythm desynchronization using the acceleration spectrum entropy of the EEG. Clin Electroencephalogr, 1993, 24(3): 104-109.
[16]  6. Thanaraj P, Parvathavarthini B. Multichannel interictal spike activity detection using time-frequency entropy measure. Australas Phys Eng Sci Med, 2017, 40(2): 413-425.
[17]  8. Liu Quan, Chen Yifeng, Fan Shouzen, et al. EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery. Med Biol Eng Comput, 2017, 55(8): 1435-1450.
[18]  14. Blanco S, Figliola A, Quiroga R Q, et al. Time-frequency analysis of electroencephalogram series. Ⅲ. Wavelet packets and information cost function. Physical Review E, 1998, 57(1): 932-940.
[19]  15. Shannon C E. A mathematical theory of communication. Bell Syst Tech J, 1948, 27(3): 379-656.
[20]  16. Feldman D P, Crutchfield J P. Measures of statistical complexity: why? Phys Lett A, 1998, 238(4-5): 244-252.
[21]  17. Mitschake F, M?ller M, Large W. Measuring filtered chaotic signals. Phys Rev A, 1988, 37(11): 4518-4521.
[22]  22. Wang Deng, Miao Duoqian, Xie Chen. Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection. Expert Syst Appl, 2011, 38(11): 14314-14320.
[23]  23. Rosso O A, Blanco S, Yordanova J, et al. Wavelet entropy: a new tool for analysis of short duration brain electrical signals. J Neurosci Methods, 2001, 105(1): 65-75.

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