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基于多重多尺度熵的孤独症静息态脑电信号分析

李昕 安占周 李秋月 蔡二娟 王欣

李昕, 安占周, 李秋月, 蔡二娟, 王欣. 基于多重多尺度熵的孤独症静息态脑电信号分析. 自动化学报, 2020, 46(6): 1255-1263. doi: 10.16383/j.aas.c170687
引用本文: 李昕, 安占周, 李秋月, 蔡二娟, 王欣. 基于多重多尺度熵的孤独症静息态脑电信号分析. 自动化学报, 2020, 46(6): 1255-1263. doi: 10.16383/j.aas.c170687
LI Xin, AN Zhan-Zhou, LI Qiu-Yue, CAI Er-Juan, WANG Xin. Autistic Resting EEG Signals Analysis via Multiple Multi-scale Entropies. ACTA AUTOMATICA SINICA, 2020, 46(6): 1255-1263. doi: 10.16383/j.aas.c170687
Citation: LI Xin, AN Zhan-Zhou, LI Qiu-Yue, CAI Er-Juan, WANG Xin. Autistic Resting EEG Signals Analysis via Multiple Multi-scale Entropies. ACTA AUTOMATICA SINICA, 2020, 46(6): 1255-1263. doi: 10.16383/j.aas.c170687

基于多重多尺度熵的孤独症静息态脑电信号分析

doi: 10.16383/j.aas.c170687
基金项目: 

国家自然科学基金 51677162

中国博士后科学基金 2014M550 582

河北省自然科学基金 F20142 03244

河北省自然科学基金 F2019203515

详细信息
    作者简介:

    安占周  燕山大学硕士研究生. 2016年于燕山大学获得学士学位.主要研究方向为医学信息处理和情感计算. E-mail: 18332553763@163.com

    李秋月  燕山大学硕士研究生. 2015年于燕山大学里仁学院获得学士学位.主要研究方向为医学信息处理和情感计算. E-mail: 18233586099@163.com

    蔡二娟  燕山大学硕士研究生. 2015年于燕山大学里仁学院获得学士学位.主要研究方向为孤独症儿童脑电信息的处理和计算. E-mail: 18233587424@163.com

    王欣  燕山大学硕士研究生. 2017年于燕山大学里仁学院获得学士学位.主要研究方向为医学信息处理和情感计算. E-mail: yddywangxin@163.com

    通讯作者:

    李昕  燕山大学教授. 1992年于东北重型机械学院获得学士学位, 2002年于燕山大学获得硕士学位, 2008年于燕山大学获得博士学位.主要研究方向为医学信息处理, 情感计算.本文通信作者. E-mail: yddylixin@ysu.edu.cn

Autistic Resting EEG Signals Analysis via Multiple Multi-scale Entropies

Funds: 

National Natural Science Foundation of China 51677162

China Postdoctoral Science Foundation 2014M550 582

Hebei Provincial Natural Science Foundation F20142 03244

Hebei Provincial Natural Science Foundation F2019203515

More Information
    Author Bio:

    LI Xin  Professor at Yanshan University. She received her bachelor degree in 1992 from Northeast Heavy Machinery Institute, her master degree in 2002 and Ph. D. degree in 2008 from Yanshan University, respectively. Her research interest covers medical information processing and afiective computing. Corresponding author of this paper

    AN Zhan-Zhou  Master student at Yanshan University. He received his bachelor degree in 2016 from Yanshan University. His research interest covers medical information processing and afiective computing

    LI Qiu-Yue  Master student at Yanshan University. She received her bachelor degree in 2015 from Liren College of Yanshan University. Her research interest covers medical information processing and afiective computing

    WANG Xin  Master student at Yanshan University. She received her bachelor degree in 2015 from Liren College of Yanshan University. Her research interest covers medical information processing and afiective computing

    Corresponding author: CAI Er-Juan  Master student at Yanshan University. She received her bachelor degree in 2015 from Liren College of Yanshan University. Her research interest covers the EEG information processing and computing for autism children
  • 摘要: 面向孤独症儿童脑功能状态评估问题, 提出一种多重多尺度熵脑电特征提取算法.算法针对传统多尺度熵信息丢失问题, 在移动均值粗粒化基础上, 采用延搁取值法构建多个尺度上的多重脑电信号序列, 再进一步计算各个尺度的样本熵.算法不仅克服了传统多尺度熵的信息丢失问题, 还能充分挖掘脑电信号的细节信息, 同时减小了尺度间的波动.基于该算法分析了16名孤独症儿童和16名正常儿童的19个通道的脑电信号.结果表明:正常儿童F7、F8、T4、P3通道的多重多尺度熵和复杂度均高于孤独症儿童, 且存在显著性差异(P < 0.05).表明前颞叶(F7、F8)可以作为孤独症儿童脑功能状态评估的敏感脑区, T4、P3可以作为辅助干预的敏感通道.
    Recommended by Associate Editor XU Bin
    1)  本文责任编委 许斌
  • 图  1  均值粗粒化过程

    Fig.  1  Coarse graining process

    图  2  移动均值粗粒化过程

    Fig.  2  Moving averaging process

    图  3  熵值结果对比

    Fig.  3  Comparison of entropy

    图  4  熵值标准差与方差比较

    Fig.  4  Comparison of standard deviation and variance between entropy

    图  5  国际10~20电极放置系统

    Fig.  5  International 10~20 electrode placement system

    图  6  孤独症儿童与正常儿童复杂度比较

    Fig.  6  Complexity comparison of autistic and normal children

    图  7  多重多尺度熵随尺度变化情况

    Fig.  7  Multiple multiscale entropy change with scale

    图  8  6通道多重多尺度熵

    Fig.  8  Multiple multiscale entropy of 6 channels

    图  9  多重多尺度熵与传统多尺度熵脑电信号结果对比

    Fig.  9  EEG comparison of traditional multiscale entropy and multiple multiscale entropy

    表  1  孤独症与正常儿童复杂度显著性检验结果

    Table  1  Autistic children with normal complexity signiflcant test results

    通道名称显著性(P值)通道名称显著性(P值)
    FP10.009FP20.134
    F30.094F40.067
    F70.003F80.028
    T30.001T40.001
    T50.032T60.019
    C30.041C40.080
    P30.003P40.019
    O10.036O20.079
    下载: 导出CSV

    表  2  孤独症与正常儿童多重多尺度熵显著性检验结果

    Table  2  Autistic and normal children multiple multiscale entropy significant test results

    通道名称显著性(P值)通道名称显著性(P值)
    F70.001F80.017
    T30.148T40.001
    P30.001P40.060
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-12-05
  • 录用日期:  2018-05-18
  • 刊出日期:  2020-07-10

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