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山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (3): 21-26.doi: 10.6040/j.issn.1672-3961.1.2016.148

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基于EMD和SVM的抑郁症静息态脑电信号分类研究

刘岩1,2,3,李幼军4,陈萌1,2,3   

  1. 1. 北京工业大学电子信息与控制工程学院, 北京 100124;2. 磁共振成像脑信息学北京市重点试验室, 北京 100124;3. 脑信息智慧服务北京国际科技合作基地, 北京 100124;4. 北方工业大学, 北京 100144
  • 收稿日期:2016-03-01 出版日期:2017-06-20 发布日期:2016-03-01
  • 作者简介:刘岩(1989— ),男,河北石家庄人,硕士研究生,主要研究方向为数据挖掘.E-mail:741642676@qq.com
  • 基金资助:
    国家重点基础研究发展计划资助项目(2014CB744600)

Research on the classification of resting state EEG signal between depression patients and normal controls by EMD and SVM methods

LIU Yan1,2,3, LI Youjun4, CHEN Meng1,2,3   

  1. 1. College of Electronic Information &
    Control Engineering, Beijing University of Technology, Beijing 100124, China;
    2. Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing 100124, China;
    3. Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing 100124, China;
    4. North China University of Technology, Beijing 100144, China
  • Received:2016-03-01 Online:2017-06-20 Published:2016-03-01

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摘要： 以静息态脑电信号为基础,通过固有模态分解(empirical mode decomposition, EMD)算法对脑电信号进行信号去噪和特征值提取,通过支持向量机(support vector machine, SVM)算法对抑郁症患者和正常对照组人群的脑电特征值进行分类分析。 通过系统化的数据采集试验,采集了20位抑郁症患者和25位健康对照组的静息态脑电信号;对静息态脑电信号进行信号的去噪和特征提取;采用SVM算法对抑郁症患者和正常人对照组脑电特征值进行二值分类,分类正确率达到93.3%。 相较于传统的小波变换提取的特征值,分类准确率有明显的提高。

关键词: 抑郁症, 脑电信号, 支持向量机, 固有模态函数, 固有模态分解

Abstract: Automatic detection of depression state was significant for mental disease diagnostics and rehabilitation, which could decrease the duration of work required when inspecting the electroencephalography(EEG)signals. A novel method for feature extraction and pattern recognition from subjects resting state EEG signal, based upon empirical mode decomposition(EMD)and support vector machine(SVM)was proposed to make a distinction between depression patients and normal controls. The EEG signals were collected from 20 depression patients and 25 normal persons, and the EEG was filtered and extracted as features. The SVM was used as classifier for recognition which showed whether the person was a depression patient. The experimental results showed that the algorithm could achieve the specificity of 93.3%. And the classification accuracy from the features extracted by EMD was higher than the classification accuracy from features extracted by wavelet clearly.

Key words: EEG, intrinsic mode function(IMF), EMD, depression, SVM

中图分类号: 

  • TP391.4
[1] 朱艺.抑郁症研究进展[J].实用中医药杂志,2008,24(2):131-132. ZHU Yi. Research progress on depression[J]. Journal of Practical Traditional Chinese Medicine, 2008, 24(2):131-132.
[2] 李跃华,张兰凤.抑郁症研究现状及未来研究目标探讨[J].中国中医药信息杂志,2006,13(10):1-3. LI Yuehua, ZHANG Lanfeng. Research status and future research goals of depression[J]. Chinese Journal of Information on Traditional Chinese Medicine, 2006, 13(10):1-3.
[3] GUI Guangzhao, CAO xianghong, WANG Yanfeng, et al. Digital signal processing in bioinformatics[J]. Science Technology and Engineering, 2005, 5(20):1494-1502.
[4] HUANG Norden E, SHEN Zheng, LONG Steven R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London. Series A: Mathematical Physical and Engineering Sciences, 1998, 454(1971):903-995.
[5] 穆峰,常发亮,蒋沁宇.基于改进EMD算法的信号滤波[J].山东大学学报(工学版), 2015, 45(3):35-42. MU Feng, CHANG Faliang, JIANG Qinyu. Signal filtering based on Improved Empirical Mode Decomposition[J]. Journal of Shandong University(Engineering Science), 2015, 45(3):35-42.
[6] LI Y, TSE P W, YANG X, et al. EMD-based fault diagnosis for abnormal clearance between contacting components in a diesel engine[J]. Mechanical Systems and Signal Processing, 2010, 24(1):193-210.
[7] SHEN Z, CHEN X,ZHANG X, et al. A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM[J]. Measurement, 2012, 45(1):30-40.
[8] 卢丹,周以齐.基于EEMD和CWT的挖掘机座椅振动分析[J].山东大学学报(工学版), 2015, 45(3):58-64. LU Dan, ZHOU Yiqi. Vibration analysis of excavator seat based on EEMD and CWT[J]. Journal of Shandong University(Engineering Science), 2015, 45(3):58-64.
[9] LI Shufang, ZHOU Weidong, YUAN Qi, et al. Feature extraction and recognition of ictal EEG using EMD and SVM[J]. Computer in Biology and Medicine, 2013, 43(7):807-816.
[10] 翟俊海,翟梦尧,张素芳,等.基于小波子空间集成的人脸识别[J].山东大学学报(工学版), 2012, 42(2):1-6. ZHAI Junhai, ZHAI Mengyao, ZHANG Sufang, et al. Face recognition based on ensemble of wavelet subspaces[J]. Journal of Shandong University(Engineering Science), 2012, 42(2):1-6.
[11] 张宏兵, 陆建峰, 汤九斌.一种基于近似EMD的DBSCAN改进算法[J].山东大学学报(工学版), 2012, 42(4):35-40. ZHANG Hongbing, LU Jianfeng, TANG Jiubin. An improved DBSCAN algorithm based on the approximate EMD[J]. Journal of Shandong University(Engineering Science), 2012, 42(4):35-40.
[12] HARRINGTON P. Machine learning in action[M]. Beijing: Posts & Telecom Press, 2013:73-86.
[13] 尧德中. 脑功能探测的电学理论与方法[M]. 北京:科学出版社,2003:10-236. YAO Dezhong. Electrical theory and method of detection of brain function[M]. Beijing: Science Press, 2003:10-236.
[14] LIKHACHEV M. Search-based Planning for Large Dynamic Environments[D]. ProQuest Dissertations Publishing: Carnegie Mellon University, 2005.
[15] SCHNEIDER M, MUSTARO P N, LIMA CA M. Automatic recognition of epileptic seizure in EEG via support vector machine and dimension fractal[C] //Proceedings of the 2009 International Joint Conference on Neural Networks. Atlanta, USA:IJCNN, 2009:3321-3325.
[16] 李发权,杨立才,颜红博.基于PCA-SVM多生理信息融合的情绪识别方法[J].山东大学学报(工学版), 2014, 44(6):70-76. LI Faquan, YANG Licai, YAN Hongbo. An emotion recognition method of multiphysiological information fusion based on PCA-SVM[J]. Journal of Shandong University(Engineering Science), 2014, 44(6):70-76.
[17] PLATT J C. Fast training of support vector machines using sequential minimal optimization[C] //Advances in Kernel Methods-Support Vector Learning. Cambridge, MA: MIT Press, 1999:185-208.
[18] GUMUS E, KILIC N, SERTBAS A, et al. Evaluation of face recognition techniques using PCA, wavelets and SVM[J]. Expert Systems with Applications, 2010, 37(9):6404-6408.
[19] WANG Deng, MIAO Duoqian, XIE Chen. Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection[J]. Expert Systems with Applications, 2011, 38(11):14314-14320.
[20] NANDAN M, TALATHI S S, MYERS S, et al. Support vector machines for seizure detection in an animal model of chronic epilepsy[J]. Journal of Neural Engineering, 2010, 7(3):361-372.
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