基于脑电信号的麻醉特征参数分析
Analysis of Anesthesia Characteristic Parameters Based on the EEG Signal
王锋 1李晓欧2
作者信息
- 1. 上海理工大学医疗器械与食品学院,上海200093
- 2. 上海理工大学医疗器械与食品学院,上海200093;上海理工大学上海医疗器械高等专科学校,上海200093
- 折叠
摘要
针对所有原始脑电信号都受到低频和尖峰噪声干扰的问题,提出了小波变换和独立分量分析相结合的去噪算法;对预处理后的脑电数据,进行小波熵、近似熵和复杂度这三种特征参数的数值表征,并进一步通过特征参数的状态变化率来判断脑电信号的状态区分效果.麻醉与非麻醉的脑电数据处理结果表明,三种特征参数的状态变化率分别达到50.5％、21.6％和19.5％,其中小波熵的状态变化率最高,这些特征参数可作为基于脑电信号分析的麻醉深度量化研究的基础.
Abstract
All the collected original electroencephalograph (EEG) signals were the subjects to low-frequency and spike noise.According to this fact,we in this study performed denoising based on the combination ofwavelet transform and independent component analysis (ICA).Then we used three characteristic parameters,complexity,approximate entropy and wavelet entropy values,to calculate the preprocessed EEG data.We then made a distinguishing judge on the EEG state by the state change rate of the characteristic parameters.Through the anesthesia and non-anesthesia EEG data processing results showed that each of the three state change rates could reach about 50.5％,21.6％,19.5％,respectively,in which the performance of wavelet entropy was the highest.All of them could be used as a foundation in the quantified research of depth of anesthesia based on EEG analysis.
关键词
麻醉脑电信号/小波变换/独立分量分析/复杂度/熵Key words
anesthesia electroencephalogram signals/wavelet transform/independent component analysis/complexity/entropy引用本文复制引用
基金项目
上海市教委科研创新项目资助(12YZ194)
上海市科委科技创新行动计划项目资助(11441902400)
出版年
2015
NETL
NSTL
维普
万方数据