基于小波包分解的不同状态下脑电信号分析  

Analysis of Different Functional States EEG Signal Based on Wavelet Packet Decomposition

作　　者：黄静霞[1] 许慰玲[1] 沈民奋[1] 

机构地区：[1]汕头大学广东省数字图象处理重点实验室,515063

出　　处：《北京生物医学工程》2004年第1期47-50,共4页Beijing Biomedical Engineering

基　　金：国家自然科学基金 ( 60 2 710 2 3 ) ;广东省自然科学基金重点项目 ( 0 2 12 64 )资助

摘　　要：本文针对脑电信号的非平稳性 ,引入小波包分解理论处理临床脑电。根据脑电信号的不同节律特性 ,提出应用小波包分解构造不同频率特性的时变滤波器 ,提取脑电信号不同节律的动态特性 ,并由此构造各种节律的动态脑电地形图。为了研究不同脑功能状态下脑电信号各种节律的动态特性 ,文中对两组不同的临床脑电数据进行分析 ,比较两种状态下各种节律的动态特性。实验结果表明 ,利用小波包分解对脑电信号进行滤波 ,能够有效提取临床脑电不同节律的动态特性 。In this paper, the theory of wavelet packet decomposition is employed to investigate the clinical EEG signals according to its nonstationarity. On the basis of the property of different EEG rhythms, wavelet packet decomposition is used for designing filters with different frequency characteristics to detect the dynamic characteristics of different EEG rhythms, which are used to form the dynamic electrical brain activity mapping (DBEAM). In order to examine the dynamic characteristics of all sorts of rhythms under different functional states of brain, two kinds of clinical EEG data with different brain function states are analyzed and compared. The experimental results indicate that the dynamic characteristics of clinical brain electrical activities can be demonstrated by using wavelet packet decomposition. The method proposes a new way for the analysis of EEG signals.

关 键 词：小波包分解 脑电信号 小波变换 信号分析 动态脑电地形图 

分 类 号：R741.044[医药卫生—神经病学与精神病学]