应用小波熵分析大鼠脑电信号的动态变化特性

被引：29

作者：

封洲燕

机构：

[1] 浙江大学生命科学学院浙江杭州

来源：

生物物理学报 | 2002年 / 03期

关键词：

小波熵; 脑电; 谱熵; 慢波睡眠;

D O I：

暂无

中图分类号：

Q42 [神经生理学];

学科分类号：

071006 ;

摘要：

应用小波熵(一种新的信号复杂度测量方法)分析大鼠在不同生理状态下脑电复杂度的动态时变特性。采用慢性埋植电极记录自由活动大鼠的皮层EEG,使用多分辨率小波变换将EEG信号分解为啄、兹、琢和茁四个分量,求得随时间变化的小波熵。结果表明:在清醒、慢波睡眠和快动眼睡眠三种生理状态下,EEG的小波熵之间存在显著差别,并且在不同时期其值与各个分解分量之间具有不同的关系,其中,慢波睡眠期小波熵还具有较明显的变化节律,反映了EEG微状态中慢波和纺锤波的互补性。由此可见,小波熵既能区别长时间段EEG复杂度之间的差别,又能反映EEG微状态的快速变化特性。

引用

页码：325 / 330

页数：6

共 17 条

[1]

EEG power densities(0.5-20Hz) in different sleep-wake stages in rats. BjorvatnB,FagerlandS,UrsinR. Physiol&Behav . 1998

[2]

A theory for multiresolution signal decomposition: the wavelet representation. MallatSG. IEEETransactiononPatternAnalysisandMachineIntelligence . 1989

[3]

Quantification ofEEG irregularity by use of the entropy of the power spectrum. InouyeT,ShinosakiK,SakamotoH,et al. ElectroencephalographyandClinicalNeurophysiol-ogy . 1991

[4]

Non-stationary analysis of ex-tracellular neural activity. LiD,MagnusonDSK,JungR. Neurocomputing . 2000

[5]

Influence of aGABAB receptor antagonist on the sleep-waking cycle in the rat. GauthierP,ArnaudC,GandolfoG,et al. Brain Research Bulletin . 1997

[6]

Spectral analysis methods for neurological signals. MuthuswamyJ,ThakorNV. JNeurosciMeth . 1998

[7]

StochasticComplexityMeasures forPhysiologicalSignalAnalysis. RezekIA,RobertsSJ. IEEETransBiomedEngin . 1998

[8]

Algorithmic complexity as an index of cortical function in awake and pentobarbi-tal-anesthetized rats. ShawFZ,ChenRF,TsaoHW,et al. JNeurosciMeth . 1999

[9]

The largestLyapunov exponent of theEEG duringECT seizures as a measure ofECT seizure adequacy. KrystalAD,ZaidmanC,GreensideHS,et al. ElectroencephalographyandClinicalNeurophysiology . 1997

[10]

Wavelet entropy: a new tool for analysis of short duration brain electrical sig-nals. RossoOA,BlancoS,YordanovaJ,et al. JNeurosciMeth . 2001

← 1 2 →