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儿童失神癫(癎)脑电图的多尺度特征
引用本文:张美云,张本恕,王凤楼,陈英,姜楠.儿童失神癫(癎)脑电图的多尺度特征[J].临床神经电生理学杂志,2006,15(5):259-267.
作者姓名:张美云  张本恕  王凤楼  陈英  姜楠
作者单位:1. 300121,天津,天津市人民医院神经内科
2. 天津医科大学总医院神经内科
3. 天津大学力学系
摘    要:目的:研究儿童失神癫癎脑电图的多尺度定量特征。方法:对15例失神癫癎患儿10次临床发作和20次亚临床癎样放电的脑电图进行子波分析,提取失神癫癎发作过程中脑电信号的多尺度定量典型特征,与发作前10 s及发作后10 s的脑电信号进行比较,并与12例正常同龄儿童脑电图进行比较。结果:研究显示儿童失神癫癎发作过程中脑电信号的多尺度典型特征主要表现为12尺度(对应频率3 Hz)的节律性活动显著增强,发作时20尺度(低频大尺度,对应频率0.12 Hz)结构与频率3 Hz的结构具有非正常的跳跃式尺度关系,3 Hz节律性棘慢复合波与大尺度(频率1 Hz以下)背景低频放电结构共同存在。发作过程中分尺度功率主要集中在20尺度和12尺度,其演变规律为20尺度能量逐渐减低,12尺度能量逐渐增加。10次临床发作的脑电信号均显示上述特征。发作前10 s和后10 s的脑电多尺度信号中仍然存在隐性的3 Hz棘慢复合波成分,与一般认为3 Hz棘慢复合波突起突止不同.而从传统的脑电图上无法分辨出发作前后的这些多尺度细节的定量特征。亚临床癎样放电的多尺度特征与发作期无明显差别,但持续时间短。结论:子波分析作为一种新的信号分析方法,适合于脑电信号的分析,可以获得比传统视觉脑电图更多的定量信息。通过对失神癫癎患儿的脑电信号进行子波分析,得到其发作过程中典型的多尺度定量特征,有助于失神癫癎发作的临床辅助诊断、预后评价以及神经电生理机理的基础研究。

关 键 词:脑电图  多尺度  癫癎  失神发作  子波分析
文章编号:1009-5934(2006)-05-259-09
收稿时间:2006-03-06
修稿时间:2006-05-15

Multi-scale characteristics of EEG signal of epileptic children in absence seizures
ZHANG Meiyun, ZHANG Benshu, WANG Fenglou, et al ,China.Multi-scale characteristics of EEG signal of epileptic children in absence seizures[J].Journal of Clinical Electroneurophysiology,2006,15(5):259-267.
Authors:ZHANG Meiyun  ZHANG Benshu  WANG Fenglou    China
Abstract:Objective: To study the characteristics of the EEG signal in children absence seizure through multi-scale wavelet transformation. Methods: The EEG signals of 10 clinical seizures and 20 sub-clinical epilepsy-form discharge in 15 cases of children absence epilepsy with multi-scale resolution were examined through wavelet transformation, so as to extract the qualitative features of seizure onset in multi-scale ,compared with EEG 10 seconds before and after onsets, and with the 12 normal children of the same age. Results: The multi-scale characteristics of the absence seizure was shown in the rhythmitic activities of Scale 12 (frequency corresponding to 3 Hz). Their EEG signals 10 s before and after onsets have been different from the normal. The main representation is the multi-scale power of the seizure onset is mainly localized in Scale 20 (frequency corresponding to 0. 12 Hz) and Scale 12. The power in Scale 20 decreases gradually, while the power in Scale 12 increases gradually. The feathers are found in all 10 seizures and 20 subclinical discharge, but duration of the latter is short. Conclusions: The wavelet analysis, as a new signal process method, is a powerful tool to study multi-scale characteristics of EEG signals. More qualitative information can be obtained from the wavelet analysis of EEG for the seizure onset in of children absence epilepsy. The study shows that these multi-scale qualitative characteristics are helpful in clinical diagnosis and play an important role in basic research of neurology.
Keywords:EEG  Multi-scale  Eepilepsy  Absence seizures  Wavelet analysis
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