Wang Feng, Li Xiaoou
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2015 Feb;32(1):13-8, 31.
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 of wavelet 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.
所有收集到的原始脑电图（EEG）信号都受到低频和尖峰噪声的影响。基于这一事实，我们在本研究中基于小波变换和独立成分分析（ICA）的组合进行去噪。然后，我们使用三个特征参数，即复杂度、近似熵和小波熵值，来计算预处理后的EEG数据。接着，我们通过特征参数的状态变化率对EEG状态进行区分判断。通过对麻醉和非麻醉EEG数据的处理结果表明，三种状态变化率分别可达约50.5%、21.6%、19.5%，其中小波熵的性能最高。它们都可作为基于EEG分析的麻醉深度量化研究的基础。
