[1]李冬,金韬,冯智英,等.基于脑电信号的疼痛强度识别方法研究[J].中国医学物理学杂志,2019,36(7):836-840.[doi:DOI:10.3969/j.issn.1005-202X.2019.07.017]
　LI Dong,JIN Tao,FENG Zhiying,et al.Pain intensity recognition based on EEG signals[J].Chinese Journal of Medical Physics,2019,36(7):836-840.[doi:DOI:10.3969/j.issn.1005-202X.2019.07.017]
点击复制

基于脑电信号的疼痛强度识别方法研究()
分享到：

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
36卷
期数:
2019年第7期
页码:
836-840
栏目:
医学信号处理与医学仪器
出版日期:
2019-07-25

文章信息/Info

Title:
Pain intensity recognition based on EEG signals
文章编号:
1005-202X（2019）07-0836-05
作者:
李冬1; 金韬1; 冯智英2; 左路路1; 朱翔1; 刘伟明1
1.浙江大学信息与电子工程学院，浙江杭州310027；2. 浙江大学第一附属医院疼痛科，浙江杭州310027
Author(s):
LI Dong1;  JIN Tao1;  FENG Zhiying2;  ZUO Lulu1;  ZHU Xiang1;  LIUWeiming1
1. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; 2. Department of Pain, the First Affiliated Hospital, Zhejiang University, Hangzhou 310027, China
关键词:
脑电信号; 疼痛强度识别; 带状疱疹后遗神经痛; 特征提取; 随机森林
Keywords:
electroencephalogram;  pain intensity recognition signal;  postherpetic neuralgia;  feature extraction;  random forest
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2019.07.017
文献标志码:
A
摘要:
目的：通过对疼痛患者的脑电信号进行特征提取和特征选择，实现对疼痛等级的量化评估。方法：对临床采集的 脑电信号进行离散小波变换得到近似和细节系数，根据每层分解系数计算子带能量占比、系数统计特征、样本熵和锁相 值，组成特征向量。利用随机森林进行特征选择和疼痛预测。结果：实现对疼痛等级的三分类，平均分类准确率为 91.7%，其中无痛和重痛的分类准确率达100%。结论：本研究方法可以有效地对脑电信号进行特征提取和选择，以较高 的准确率实现疼痛强度的识别，为临床疼痛的客观评估奠定基础。
Abstract:
Abstract: Objective To perform feature extraction and feature selection for electroencephalogram (EEG) signals collected from patients with postherpetic neuralgia for quantitatively evaluating the level of pain. Methods Discrete wavelet transform was employed to decompose clinically collected EEG signals to obtain approximate and detail coefficients. The feature vectors were composed of sub-band energy ratio, coefficient statistics, sample entropy and phase-locked value which were calculated based on the decomposition coefficients of each level. Random forest was used for feature selection and pain intensity recognition. Results The proposed method realized the 3 classifications of pain levels, with an average classification accuracy of 91.7%. Moreover, the accuracy of the classification between no-pain and high-pain reached 100%. Conclusion The proposed method can be used to effectively extract and select features from EEG signals, and realize pain intensity recognition with a high accuracy, which lays a foundation for the objective evaluation of clinical pain.

相似文献/References:

[1]王怡玲,覃玉荣,郭湛超,等.基于不同闪烁频率光刺激的脑电压变化研究[J].中国医学物理学杂志,2014,31(05):5184.[doi:10.3969/j.issn.1005-202X.2014.05.019]
[2]杨建平,张德乾,吕敬祥,等.操作发起过程多脑区协作的脑电谱熵特征[J].中国医学物理学杂志,2016,33(1):44.[doi:DOI:10.3969/j.issn.1005-202X.2016.01.010]
　[J].Chinese Journal of Medical Physics,2016,33(7):44.[doi:DOI:10.3969/j.issn.1005-202X.2016.01.010]
[3]崔招焕,等.大鼠癫痫脑电信号采集[J].中国医学物理学杂志,2016,33(2):118.[doi:10.3969/j.issn.1005-202X.2016.02.003]
　[J].Chinese Journal of Medical Physics,2016,33(7):118.[doi:10.3969/j.issn.1005-202X.2016.02.003]
[4]顾家军,叶继伦.麻醉深度监测中脑电信号特征提取方法[J].中国医学物理学杂志,2016,33(2):157.[doi:10.3969/j.issn.1005-202X.2016.02.010]
　[J].Chinese Journal of Medical Physics,2016,33(7):157.[doi:10.3969/j.issn.1005-202X.2016.02.010]
[5]刘岩,李幼军,陈萌. 基于固有模态分解和深度学习的抑郁症脑电信号分类分析[J].中国医学物理学杂志,2017,34(9):963.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.021]
　[J].Chinese Journal of Medical Physics,2017,34(7):963.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.021]
[6]马玉良,刘卫星,张淞杰,等.基于ABC-SVM的运动想象脑电信号模式分类[J].中国医学物理学杂志,2018,35(9):1056.[doi:10.3969/j.issn.1005-202X.2018.09.012]
　MAYuliang,LIUWeixing,ZHANG Songjie,et al.Pattern classification of motor imagery EEG signals based on ABC-SVM algorithm[J].Chinese Journal of Medical Physics,2018,35(7):1056.[doi:10.3969/j.issn.1005-202X.2018.09.012]
[7]刘畅,覃玉荣,时文健.视听觉刺激下大脑头皮电位空间变化特性[J].中国医学物理学杂志,2018,35(10):1225.[doi:DOI:10.3969/j.issn.1005-202X.2018.010.023]
　LIU Chang,QIN Yurong,SHI Wenjian. Spatial variation characteristics of scalp potentials under audiovisual stimuli[J].Chinese Journal of Medical Physics,2018,35(7):1225.[doi:DOI:10.3969/j.issn.1005-202X.2018.010.023]
[8]周杰,杨国雨,徐涛. 基于空间频率与时间序列信息的多类运动想象脑电分类[J].中国医学物理学杂志,2019,36(1):81.[doi:DOI:10.3969/j.issn.1005-202X.2019.01.016]
　ZHOU Jie,YANG Guoyu,XU Tao. Classification of multi-class motor imagery EEG data based on spatial frequency and time-series information[J].Chinese Journal of Medical Physics,2019,36(7):81.[doi:DOI:10.3969/j.issn.1005-202X.2019.01.016]
[9]冯琴昌.基于OpenBCI与OpenViBE的脑机接口设计[J].中国医学物理学杂志,2020,37(2):210.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.014]
　FENG Qinchang.Design of brain-computer interface based on OpenBCI and OpenViBE[J].Chinese Journal of Medical Physics,2020,37(7):210.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.014]
[10]赵丽,崔立杰.基于变分模态分解的眼电伪迹去除[J].中国医学物理学杂志,2020,37(2):237.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.018]
　ZHAO Li,CUI Lijie.Electrooculogram artifacts removal based on variational mode decomposition[J].Chinese Journal of Medical Physics,2020,37(7):237.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.018]

备注/Memo

备注/Memo:
【收稿日期】2019-02-19 【作者简介】李冬，硕士研究生，研究方向：生物医学信号处理，E-mail: 21631108@zju.edu.cn 【通信作者】金韬，教授，研究方向：智能医疗、光通信，E-mail:jint@zju. edu.cn
更新日期/Last Update: 2019-07-25