基于脑电波复杂度的麻醉深度监测

脑电(EEG)是能反映麻醉深浅程度的电生理信号，为了能从非线性、非平稳的脑电信号中提取与麻醉深度相关的有效信息，笔者采用由Lempel和Ziv提出的复杂度算法，对实测SD大鼠在不同麻醉状态下脑电信号的复杂度进行计算，比较了不同麻醉深度下脑电复杂度的变化情况．从实验观察的现象与数据比较结果可见，利用复杂度来表征脑电信号的特征值可以很好地反映麻醉的深浅程度，而且算法简单、实时性好，是一种量化庆醉深度的新方法．

EEG Complexity-Based Anesthetic Depth Monitoring

EEG is a biomedical signal capable of characterizing the depth of anesthesia. In order to quantify the information of EEG, the complexity algorithm proposed by Lem-Ziv is used to calculate the complexity of the measured EEG signals of SD mice under different depth of anesthesia. Different cases of EEG complexity changing with the depth of anesthesia are compared. From the observed experimental phenomena and the data comparison result, it shows that using complexity to characterize EEG signal can well reflect the depth of anesthesia and quantify the anesthetic depth with arithmetic simplicity and a good real time performance.