[1]顾家军,叶继伦,崔钰涵,等.BP网络在麻醉深度监测算法上的应用[J].中国医学物理学杂志,2021,38(8):985-989.[doi:DOI:10.3969/j.issn.1005-202X.2021.08.013]
　GU Jiajun,YE Jilun,CUI Yuhan,et al.Application of back-propagation network in algorithm for monitoring depth of anesthesia[J].Chinese Journal of Medical Physics,2021,38(8):985-989.[doi:DOI:10.3969/j.issn.1005-202X.2021.08.013]
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BP网络在麻醉深度监测算法上的应用()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第8期
页码:
985-989
栏目:
医学信号处理与医学仪器
出版日期:
2021-08-02

文章信息/Info

Title:
Application of back-propagation network in algorithm for monitoring depth of anesthesia
文章编号:
1005-202X（2021）08-0985-05
作者:
顾家军1; 叶继伦2; 3; 崔钰涵1; 陈谨1; 陈玲玲1
1.深圳技术大学健康与环境工程学院， 广东 深圳 518118； 2.深圳大学医学部生物医学工程学院， 广东 深圳 518060； 3.广东省生物医学信号检测与超声成像重点实验室， 广东 深圳 518060
Author(s):
GU Jiajun1;  YE Jilun2;  3;  CUI Yuhan1;  CHEN Jin1;  CHEN Lingling1
1. School of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China 2. School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China 3. Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
关键词:
麻醉深度; 脑电信号; 滤波; 前向反馈; BP神经网络
Keywords:
Keywords: depth of anesthesia electroencephalogram signal filter feedforward back-propagation neural network
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2021.08.013
文献标志码:
A
摘要:
麻醉是临床手术中必不可少的环节，但麻醉的过深或过浅可能给病人带来伤害，因而对麻醉深度的监测具有较高的临床价值。脑电是目前检测麻醉深度最有潜力的方法，首先通过滤波等处理方式得到较为纯净的脑电信号，分析时域和频域的特征，计算相应的参数，并将该参数作为前向反馈神经网络的输入参数，选择合适的BP神经网络拟合得到一个能够评价麻醉深度的无量纲常数。使用BP神经网络拟合结果来表征麻醉深度准确率普遍在90%以上，反映了BP神经网络在麻醉深度监测上具有较高的应用价值。
Abstract:
Abstract: Anesthesia is an essential part of clinical operation, but the inappropriate depth of anesthesia (overdose or underdose) may cause harms to patients. Therefore, monitoring the depth of anesthesia has a high clinical value. Currently, electroencephalogram is the most potential method to detect the depth of anesthesia. After obtaining the pure electroencephalogram signal by filtering and other processing methods, the time-domain and frequency-domain characteristics are analyzed, and the corresponding parameters are calculated. Then the parameters are used as the input parameters of feedforward neural network, and a dimensionless constant which can evaluate the depth of anesthesia is obtained by back-propagation (BP) neural network fitting. The accuracy of using BP neural network fitting results to characterize the depth of anesthesia is generally more than 90%, which reflects that BP neural network has a high value in monitoring the depth of anesthesia.

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备注/Memo

备注/Memo:
【收稿日期】2021-03-19 【基金项目】深圳市产业关键技术研发项目（20190215140144982）；教育部产学合作协同育人项目（202002086003）；2020校级“质量工程”建设项目 【作者简介】顾家军，工程师，研究方向：生命信息监测与图形处理，E-mail: gujiajun@sztu.edu.cn
更新日期/Last Update: 2021-07-31