[1]汤卫雄,程云章,张天逸,等.基于脑电非线性特征和AdaBoost算法的诱导期麻醉深度检测[J].中国医学物理学杂志,2023,40(5):616-621.[doi:DOI:10.3969/j.issn.1005-202X.2023.05.015]
　TANG Weixiong,CHENG Yunzhang,et al.Monitoring depth of anesthesia during induction using EEG nonlinear characteristics combined with AdaBoost algorithm[J].Chinese Journal of Medical Physics,2023,40(5):616-621.[doi:DOI:10.3969/j.issn.1005-202X.2023.05.015]
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基于脑电非线性特征和AdaBoost算法的诱导期麻醉深度检测()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第5期
页码:
616-621
栏目:
医学信号处理与医学仪器
出版日期:
2023-05-26

文章信息/Info

Title:
Monitoring depth of anesthesia during induction using EEG nonlinear characteristics combined with AdaBoost algorithm
文章编号:
1005-202X（2023）05-0616-06
作者:
汤卫雄1; 2; 程云章1; 张天逸1; 宋金超2
1.上海理工大学健康科学与工程学院， 上海 200093； 2.上海理工大学附属市东医院麻醉科， 上海 200082
Author(s):
TANG Weixiong1;  2;  CHENG Yunzhang1;  ZHANG Tianyi1;  SONG Jinchao2
1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2. Department of Anesthesiology, Shidong Hospital Affiliated to University of Shanghai for Science and Technology, Shanghai 200082, China
关键词:
麻醉深度; 诱导期; 脑电信号; 非线性特征; AdaBoost算法
Keywords:
Keywords: depth of anesthesia induction period electroencephalogram signal nonlinear characteristic AdaBoost algorithm
分类号:
R318；R614
DOI:
DOI:10.3969/j.issn.1005-202X.2023.05.015
文献标志码:
A
摘要:
提出一种结合自适应增强学习AdaBoost算法和脑电非线性特征的麻醉深度评估方法，通过提取脑电信号中的4种非线性特征（KC复杂度、小波熵、排序熵、模糊熵）作为输入，以双谱指数作为参考输出，将诱导期麻醉深度分为清醒、轻度麻醉、中度麻醉。使用9例全麻患者的诱导期脑电信号对该方法进行评估，3种不同麻醉状态分类准确度为86.69%，Kappa系数为0.837，表明该方法可以较好地区分诱导期3种不同麻醉水平，为麻醉深度监测提供新思路。
Abstract:
Abstract: A method that combines adaptive boosting (AdaBoost) algorithm with nonlinear characteristics of electroencephalogram (EEG) is proposed to estimate the depth of anesthesia. With 4 nonlinear features (KC complexity, wavelet entropy, permutation entropy and fuzzy entropy) extracted from EEG signals as input and bispectral index as reference output, the depth of anesthesia during the induction is divided into awake, mild anesthesia and moderate anesthesia. The proposed method is evaluated using the EEG signals of 9 patients during the induction of general anesthesia, and the results show that the method achieves an accuracy of 86.69% in classifying 3 different anesthetic states, with a Kappa coefficient of 0.837. The proposed method can better distinguish the depth of anesthesia during the induction, which provides a new idea for monitoring the depth of anesthesia.

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备注/Memo

备注/Memo:
【收稿日期】2022-12-15 【基金项目】上海市杨浦区科委/卫健委科研面上项目（YPM202105）；上海工程技术研究中心资助项目（18DZ2250900） 【作者简介】汤卫雄，硕士，研究方向：生物电信号处理、数据分析，E-mail: 18460305619@163.com 【通信作者】宋金超，主任医师，教授，硕士生导师，E-mail: sjch2013@163.com
更新日期/Last Update: 2023-05-26