摘要
脑电图(EEG)是研究脑科学的重要工具,对EEG信号中隐藏的特征和信息进行深入研究,能更好地满足现在临床研究的需要。本文通过小波变换和非线性动力学两种分析方法,提取癫痫发作间期和发作期EEG信号及其节律波(δ波、θ波、α波和β波)的非线性特征,计算分析关联维数(CD)、Lyapunov指数、近似熵(ApEn)特征值在癫痫发作过程是否存在显著变化。研究结果表明,EEG信号及其节律波的非线性动力学特征在检测癫痫发作过程时可作为有效的鉴别统计量。
Electroencephalogram (EEG) is the primary tool in investigation of the brain science. It is necessary to carry out a deepgoing study into the characteristics and information hidden in EE(;s to meet the needs of the clinical research. In this paper, we present a wavelet-nonlinear dynamic methodology for analysis of nonlinear characteristic of EEGs and delta, theta, alpha, and beta sub-bands. We therefore studied the effectiveness of correlation dimension (CD), largest Lyapunov exponen, and approximate entropy (ApEn) in differentiation between the interictal EEG and ictal EEG based on statistical significance of the differences. The results showed that the nonlinear dynamic char acteristic of EEG and EEG subbands could be used as effective identification statistics in detecting seizures.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2014年第1期18-22,共5页
Journal of Biomedical Engineering
基金
国家自然科学基金资助项目(61263011
81000554)
中央高校基本科研业务费中山大学培育项目资助(11ykpy07)
广东省自然科学基金资助项目(S2011010005309)
新疆医科大学创新基金资助项目(XJC201209)
关键词
癫痫
脑电图
小波分析
非线性动力学特征指标
epilepsy
electroencephalogram
wavelet analysis
nonlinear dynamic characteristic indexes