文献类型：期刊文献

中文题名：基于多分辨分析的脑电癫痫波自动检测

英文题名：Automatic detection of epileptiform wave in EEG by multi-resolution analysis

作者：汪春梅[1];邹俊忠[1];张见[1];张志锁[2]

机构：[1]华东理工大学信息科学与工程学院,上海200237;[2]第二军医大学附属长海医院脑电图室,上海200433

年份：2009

卷号：26

期号：8

起止页码：2959

中文期刊名：计算机应用研究

外文期刊名：Application Research of Computers

收录：CSTPCD;;北大核心:【北大核心2008】；CSCD:【CSCD2011_2012】；

基金：国家自然科学基金资助项目(60543005;60674089);上海市重点学科资助项目(B504)

语种：中文

中文关键词：脑电信号;癫痫波;多分辨分析;自动检测

外文关键词：electroencephalogram （EEG） ; epileptiform activity ; multi-resolution analysis ; automatic detection

摘要：分析了小波多分辨分析特征提取的特点,提出了八通道脑电信号癫痫波自动检测的方法。每个通道的信号利用小波变换进行五层分解,以提取小波变换各子带的小波系数和信号偏差组成特征值计算自适应阈值,并将其应用到关键子带,提取出信号中的癫痫波。研究的重点是对脑电信号进行分解选择合适的小波;确定适当的分解层次以及自适应阈值的计算。实验结果表明,方法能够为癫痫脑电的特征提取提供快速而有效的手段。
This paper proposed a new scheme for detecting epileptiform activity in 8-channel EEG based on the characteristic of a multi-resolution analysis. The EEG signal on each channel was decomposed to five levels using discrete wavelet transform. Formed wavelet coefficients and standard deviation of all 8-channel raw data to compute adaptive threshold, which applied on sub-bandsl, 2 and 3. Then extracted the spike portion of EEG signal extracted from the raw data. The key points of this research work were identification of a suitable wavelet for decomposition of EEG signals, recognition of a proper resolution level, and computation of a dynamic threshold. The experiment results show that the proposed method offers a fast and effective measure for detecting epileptiform activity in human EEG.