[1]丁正敏,熊冬生,陈宇珂,等. 基于脑电样本熵和小波熵的麻醉深度监测[J].中国医学物理学杂志,2018,35(2):243-248.[doi:DOI:10.3969/j.issn.1005-202X.2018.02.024]
　DING Zhengmin,XIONG Dongsheng,CHEN Yuke,et al. Sample entropy and wavelet entropy of electroencephalogram for monitoring the depth of anesthesia[J].Chinese Journal of Medical Physics,2018,35(2):243-248.[doi:DOI:10.3969/j.issn.1005-202X.2018.02.024]
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 基于脑电样本熵和小波熵的麻醉深度监测()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
35卷
期数:
2018年第2期
页码:
243-248
栏目:
脑科学与神经物理
出版日期:
2018-02-08

文章信息/Info

Title:
 Sample entropy and wavelet entropy of electroencephalogram for monitoring the depth of anesthesia
文章编号:
1005-202X（2018）02-0243-06
作者:
 丁正敏1; 熊冬生1; 陈宇珂2; 张兴安3; 窦建洪3; 谌雅雨3
 1.华南理工大学材料科学与工程学院生物医学工程系， 广东 广州 510006； 2.广州军区总医院设备科， 广东 广州 510010； 3.广州军区总医院麻醉科， 广东 广州 510010
Author(s):
DING Zhengmin1;  XIONG Dongsheng1;  CHEN Yuke2;  ZHANG Xing’an3;  DOU Jianhong3;  CHEN Yayu3
 1. Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China; 2. Department of Equipment, General Hospital of Guangzhou Military Command of PLA, Guangzhou 510010, China; 3. Department of Anesthesia, General Hospital of Guangzhou Military Command of PLA, Guangzhou 510010, China
关键词:
 ; 麻醉深度; 脑电; 样本熵; 小波熵
Keywords:
 ; depth of anesthesia;  electroencephalogram;  sample entropy;  wavelet entropy
分类号:
R318.6
DOI:
DOI:10.3969/j.issn.1005-202X.2018.02.024
文献标志码:
A
摘要:
 目的：通过研究全麻手术病人的脑电信号特征，从分类准确率、算法难易程度、计算时间等方面讨论样本熵和小波熵算法在麻醉深度监测中的应用。 方法：基于脑电信号的非线性和不稳定性，采用两种非线性动力学分析方法（样本熵和小波熵）对30例全麻手术病人的脑电信号进行特征提取，并对每位病人清醒状态、轻度麻醉状态和中度麻醉状态下的脑电信号的样本熵和小波熵进行差异分析。 结果：不同麻醉状态下的脑电信号的样本熵和小波熵均有明显差异。相同脑电信号的样本熵的变化阈值较小波熵的变化阈值大。 结论：样本熵和小波熵算法均可以作为麻醉深度监测的有效指标。从分类准确率、算法难易程度和计算时间等方面考虑，使用样本熵算法的效果优于小波熵算法。
Abstract:
 Objective To research the characteristics of the electroencephalogram (EEG) signals of patients under general anesthesia, and to compare the performances of sample entropy and wavelet entropy algorithms in monitoring the depth of anesthesia, including classification accuracy, calculation complexity and calculation time. Methods Based on the characteristics of nonlinearity and instability of EEG signals, two kinds of nonlinear dynamics analysis methods, namely sample entropy algorithm and wavelet entropy algorithm, were used to extract the characteristics of the EEG signals of 30 patients under general anesthesia. The sample entropy and wavelet entropy of the EEG signals of patients under different anesthesia states (including waking state, light anesthesia and moderate anesthesia) were also compared with variance analysis. Results The sample entropy and wavelet entropy of the EEG signals under different states was significantly different. Moreover, the change threshold of sample entropy was larger than that of wavelet entropy. Conclusion Both sample entropy and wavelet entropy algorithms can be used as effective indicators for monitoring the depth of anesthesia, but when classification accuracy, calculation complexity and calculation time are taken into consideration, sample entropy algorithm is better than wavelet entropy algorithm.

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备注/Memo

备注/Memo:
【收稿日期】2017-08-28
【基金项目】广东省科技计划项目（2013B090500113）
【作者简介】丁正敏，硕士研究生，研究方向：生物医学电子与仪器，E-mail: 1032767568@qq.com.
【通信作者】熊冬生，副教授，研究方向：生物医学电子与仪器，E-mail: btlxiong@scut.edu.cn
更新日期/Last Update: 2018-01-29