CASIA OpenIR  > 毕业生  > 硕士学位论文
Degree Grantor中国科学院自动化研究所
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Abstract脑电图是大脑信息过程的一种反映.它记录了神经元在大 脑活动中的综合的生物放电现象.利用脑电图,不但能分 析大脑的各种不同的功能状态,而且在送学临床中也具有很重 要的实际价值. 本文首先对脑电图自动分析的各种方法作一般性的介 绍和简单的评述.在这个基础上,提出一种识别癫痫脑电波的 新方法:"自回归--反向滤波--夹度判别法.即用自回归模型结 合癫痫波形的特征,把脑电图的异常成分从背景活动中分离 出来. 自回归--反向滤波--夹度判别法对模拟的脑电 信号和四种不同类型的癫痫脑电记录进行试验.结果证明, 这种方法在识别和定量分析癫痫脑电图中是可行的. 本文在讨论部分,对不同的线性模型,模型的阶段 检验统计量的长度,以及长记录脑电图的处理方法,予测滤 波谱的估计,脑电记录的自适应分割等问题略作探讨.并且 与常用的癫痫脑电图的分析方法进行比较. 最后,附录(一),附录(二)分别给出了"脑电图简介",和 "主要程序".以供参考.
Other AbstractSome essential methods which represented the milestone in the development of EEG analysis are reviewed in this paper. Especially, several important methods for the detecting of the transient events in the EEG recurs are stated in detail. A new method for detecting the EEG's events: Auto- regressive Modeling--Inverse Filtering-- Sharpness Criterion (ARIS),is described, based upon the general review, this method have been tested on the simulated signal and four kinds of real epileptic EEG segments. The results are compared with some usually used methods in %he field of EEG transient detection. In the end, a brief introduction of EEG and programs are given in the appendix.
Other Identifier24
Document Type学位论文
Recommended Citation
GB/T 7714
翁心南. 癫痫脑电图的自动分析[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,1981.
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