搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于递归量化分析与支持向量机的癫痫脑电自动检测方法

孟庆芳 陈珊珊 陈月辉 冯志全

引用本文:
Citation:

基于递归量化分析与支持向量机的癫痫脑电自动检测方法

孟庆芳, 陈珊珊, 陈月辉, 冯志全

Automatic detection of epileptic EEG based on recurrence quantification analysis and SVM

Meng Qing-Fang, Chen Shan-Shan, Chen Yue-Hui, Feng Zhi-Quan
PDF
导出引用
  • 癫痫脑电信号的自动检测对癫痫的临床诊断与治疗具有重要意义. 基于递归图(recurrence plot)的递归量化分析(recurrence quantification analysis,RQA)重现了非线性时间序列的动力学行为,分析了其递归特性,本文提出了基于RQA的癫痫脑电信号特征提取方法. 实验结果表明:直接基于RQA特征的癫痫脑电的检测准确率较高,其中直接基于确定率DET的分类准确率可达到90.25%. 本文还把提取的RQA特征值和变化系数、波动指数相结合组成特征向量,输入到SVM分类器,实现癫痫脑电信号的自动检测;实验结果表明:该方法的分类准确率可达到99%.
    Automatic detection and classification of epileptic EEG signals have been a significance method for the clinical diagnosis and treatment of epilepsy. The recurrence quantification analysis (RQA) based on the recurrence plot could visualize the recurrence behaviors of dynamical systems from the nonlinear time series and analysis of the recurrence properties. This paper presents a new feature extraction method for epileptic EEG signals based on the recurrence quantification analysis. Experimental results show that the seizure detection directly based on recurrence quantification analysis features has a higher detection performance; especially the classification accuracy based on the deterministic feature can be up to 90.25%. This paper also combines the RQA features with the variation coefficient and fluctuation index, and then puts the feature vectors into a support vector machine (SVM) to automatically detect the epileptic EEG from EEG recordings. Experimental results shows that the proposed methods could achieve a great classification accuracy of 99%.
    • 基金项目: 国家自然科学基金(批准号:61201428,61070130,61173079)、山东省自然科学基金(批准号:ZR2010FQ020,ZR2011FZ003)、山东省优秀中青年科学家科研奖励基金(批准号:BS2009SW003)和中国博士后科学基金(批准号:20100470081)资助的课题.
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 61201428, 61070130, 61173079), the Natural Science Foundation of Shandong Province, China (Grant Nos. ZR2010FQ020, ZR2011FZ003), the Shandong Distinguished Middle-aged and Young Scientist Encourage and Reward Foundation, China (Grant No. BS2009SW003), and the China Postdoctoral Science Foundation (Grant No. 20100470081).
    [1]

    Meng Q F, Zhou W D, Chen Y H, Peng Y H 2010 Acta Phys. Sin. 59 123 (in Chinese) [孟庆芳, 周卫东, 陈月辉, 彭玉华 2010 物理学报 59 123]

    [2]

    Swiderski B, Osowski S, Rysz A 1995 Chaos 5 82

    [3]

    Kannathal N, Min L C, Rajendra A U, Sadasivan P K 2005 Computer Methods and Programs in Biomedicine 80 187

    [4]

    Kai C H, Sung N Y 2010 Computers in Biology and Medicine 40 823

    [5]

    Rajendra A U, Molinari F, Vinitha S S, Chattopadhyay S, Hoong N K, Suri J S 2012 Biomedical Signal Processing and Control 7 401

    [6]

    Yuan Q, Zhou W D, Li S F, Cai D M 2012 Chinese Journal of Scientific Instrument 33 514 (in Chinese) [袁琦, 周卫东, 李淑芳, 蔡冬梅 2012 仪器仪表学报 33 514]

    [7]

    Yuan Q, Zhou W D, Li S F, Cai D M 2011 Epilepsy Research 96 29

    [8]

    Cai D M, Zhou W D, Liu K, Li S F, Geng S J 2010 Chinese Journal of Biomedical Engineering 29 836 (in Chinese) [蔡冬梅, 周卫东, 刘凯, 李淑芳, 耿淑娟 2010 中国生物医学工程学报 29 836]

    [9]

    Eckmann J P, Kamphorst S O, Ruelle D 1987 Europhysics Letters 5 973

    [10]

    Acharya U R, Sree V, Chattopadhyay S, Yu W W, Alvin P C 2011 International Journal of Neural Systems 21 199

    [11]

    Thomasson N, Hoeppner T J, Webber C L, Zbilut J P 2001 Physics Letters A 279 94

    [12]

    Rajendra A U, Faustand O, Kannathal N 2005 Computer Methods and Programs in Biomedicine 80 37

    [13]

    Ouyang G X, Li X L, Dang C Y, Richards D A 2008 Clinical Neurophysiology 119 1747

    [14]

    Zhong J K, Song Z H, Hao W Q 2002 Acta Biophysica Sinica 18 241 (in Chinese) [钟季康, 宋志怀, 郝为强 2002 生物物理学报 18 241]

    [15]

    Marwan W N, Kurths J 2002 Phys. Rev. E 66 1539

    [16]

    Chen X M, Qiu Y H, Zhu Y S 2008 Journal of Biomedical Engineering 25 39 (in Chinese) [陈晓鸣, 邱意弘, 朱贻盛 2008 生物医学工程学杂志 25 39]

    [17]

    Liu X, Cheng J H, Lu H B, Zhang L P, Ma J, Dong X Z 2006 Space Medicine & Medical Engineering 19 394 (in Chinese) [刘欣, 程九华, 卢虹冰, 张立藩, 马进, 董秀珍 2006 航天医学与医学工程 19 394]

    [18]

    Deng N Y, Tian Y J 2004 A new method of data mining: support vector machines (Science Press) (in Chinese) [邓乃扬, 田英杰 2004 数据挖掘中的新方法-支持向量机(科学出版社)]

    [19]

    Cai D M, Zhou W D, Li S F, Wang J W, Jia G J, Liu X W 2011 Acta Biophysica Sinica 27 175 (in Chinese) [蔡冬梅, 周卫东, 李淑芳, 王纪文, 贾桂娟, 刘学伍 2011 生物物理学报 27 175]

    [20]

    Nicolaou N, Georgiou J 2012 Expert Systems with Applications 39 202

    [21]

    Ubeyli E D 2009 Digital Signal Processing 2009(19) 297

    [22]

    Song Y, Lio P 2010 Journal of Biomedical Science and Engineering 3 556

  • [1]

    Meng Q F, Zhou W D, Chen Y H, Peng Y H 2010 Acta Phys. Sin. 59 123 (in Chinese) [孟庆芳, 周卫东, 陈月辉, 彭玉华 2010 物理学报 59 123]

    [2]

    Swiderski B, Osowski S, Rysz A 1995 Chaos 5 82

    [3]

    Kannathal N, Min L C, Rajendra A U, Sadasivan P K 2005 Computer Methods and Programs in Biomedicine 80 187

    [4]

    Kai C H, Sung N Y 2010 Computers in Biology and Medicine 40 823

    [5]

    Rajendra A U, Molinari F, Vinitha S S, Chattopadhyay S, Hoong N K, Suri J S 2012 Biomedical Signal Processing and Control 7 401

    [6]

    Yuan Q, Zhou W D, Li S F, Cai D M 2012 Chinese Journal of Scientific Instrument 33 514 (in Chinese) [袁琦, 周卫东, 李淑芳, 蔡冬梅 2012 仪器仪表学报 33 514]

    [7]

    Yuan Q, Zhou W D, Li S F, Cai D M 2011 Epilepsy Research 96 29

    [8]

    Cai D M, Zhou W D, Liu K, Li S F, Geng S J 2010 Chinese Journal of Biomedical Engineering 29 836 (in Chinese) [蔡冬梅, 周卫东, 刘凯, 李淑芳, 耿淑娟 2010 中国生物医学工程学报 29 836]

    [9]

    Eckmann J P, Kamphorst S O, Ruelle D 1987 Europhysics Letters 5 973

    [10]

    Acharya U R, Sree V, Chattopadhyay S, Yu W W, Alvin P C 2011 International Journal of Neural Systems 21 199

    [11]

    Thomasson N, Hoeppner T J, Webber C L, Zbilut J P 2001 Physics Letters A 279 94

    [12]

    Rajendra A U, Faustand O, Kannathal N 2005 Computer Methods and Programs in Biomedicine 80 37

    [13]

    Ouyang G X, Li X L, Dang C Y, Richards D A 2008 Clinical Neurophysiology 119 1747

    [14]

    Zhong J K, Song Z H, Hao W Q 2002 Acta Biophysica Sinica 18 241 (in Chinese) [钟季康, 宋志怀, 郝为强 2002 生物物理学报 18 241]

    [15]

    Marwan W N, Kurths J 2002 Phys. Rev. E 66 1539

    [16]

    Chen X M, Qiu Y H, Zhu Y S 2008 Journal of Biomedical Engineering 25 39 (in Chinese) [陈晓鸣, 邱意弘, 朱贻盛 2008 生物医学工程学杂志 25 39]

    [17]

    Liu X, Cheng J H, Lu H B, Zhang L P, Ma J, Dong X Z 2006 Space Medicine & Medical Engineering 19 394 (in Chinese) [刘欣, 程九华, 卢虹冰, 张立藩, 马进, 董秀珍 2006 航天医学与医学工程 19 394]

    [18]

    Deng N Y, Tian Y J 2004 A new method of data mining: support vector machines (Science Press) (in Chinese) [邓乃扬, 田英杰 2004 数据挖掘中的新方法-支持向量机(科学出版社)]

    [19]

    Cai D M, Zhou W D, Li S F, Wang J W, Jia G J, Liu X W 2011 Acta Biophysica Sinica 27 175 (in Chinese) [蔡冬梅, 周卫东, 李淑芳, 王纪文, 贾桂娟, 刘学伍 2011 生物物理学报 27 175]

    [20]

    Nicolaou N, Georgiou J 2012 Expert Systems with Applications 39 202

    [21]

    Ubeyli E D 2009 Digital Signal Processing 2009(19) 297

    [22]

    Song Y, Lio P 2010 Journal of Biomedical Science and Engineering 3 556

计量
  • 文章访问数:  2773
  • PDF下载量:  766
  • 被引次数: 0
出版历程
  • 收稿日期:  2013-10-11
  • 修回日期:  2013-11-27
  • 刊出日期:  2014-03-05

基于递归量化分析与支持向量机的癫痫脑电自动检测方法

  • 1. 济南大学信息科学与工程学院, 济南 250022;
  • 2. 山东省网络环境智能计算技术重点实验室, 济南 250022
    基金项目: 

    国家自然科学基金(批准号:61201428,61070130,61173079)、山东省自然科学基金(批准号:ZR2010FQ020,ZR2011FZ003)、山东省优秀中青年科学家科研奖励基金(批准号:BS2009SW003)和中国博士后科学基金(批准号:20100470081)资助的课题.

摘要: 癫痫脑电信号的自动检测对癫痫的临床诊断与治疗具有重要意义. 基于递归图(recurrence plot)的递归量化分析(recurrence quantification analysis,RQA)重现了非线性时间序列的动力学行为,分析了其递归特性,本文提出了基于RQA的癫痫脑电信号特征提取方法. 实验结果表明:直接基于RQA特征的癫痫脑电的检测准确率较高,其中直接基于确定率DET的分类准确率可达到90.25%. 本文还把提取的RQA特征值和变化系数、波动指数相结合组成特征向量,输入到SVM分类器,实现癫痫脑电信号的自动检测;实验结果表明:该方法的分类准确率可达到99%.

English Abstract

参考文献 (22)

目录

    /

    返回文章
    返回