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| A New Method of Epileptic EEG Identification Based on Improved EMD |
| School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022，China |
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Abstract In order to improve the recognition rate of epileptic EEG and to predict epileptic disease in the early stage of epileptic seizures, the characteristic wave extraction is very important in the process of clinical diagnosis of epilepsy. To solve this problem, a method that combined parallel extension with mirror extension to improve EMD algorithm was proposed. Firstly, extreme values were predicted respectively in the left and right endpoints of the original EEG using the parallel extension method. Then, the EMD method based on the mirror extension was used in order to avoid the end effect in the process of EMD. Finally, the SVM classifier was used for signal classification and recognition. The algorithm validation data were from EEG database of the Epilepsy Research Center, University of Bonn, Germany (50 cases normal EEG signals and 50 cases epileptic EEG signals). The result shows that the recognition rate (the sum of normal EEG and epileptic EEG) of total test EEG signals by this method can reach 94%, and the recognition rates of normal EEG and epileptic EEG are both 94%. This result is 5% higher than the recognition rate of EEG processed by the traditional EMD algorithm. Therefore the method can predict epileptic EEG effectively.
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［1］雷国亮.为什么癫痫病发病率居高不下［EB/OL］. http://www.haodf.com/zhuanjiaguandian/zxyylgl_410709901.htm,2011-08-10/2013-03-27.
［2］吴晓斌，郭影，邱天爽，等. B样条小波在提取脑电癫痫棘波中的应用 ［J］. 生物医学工程学杂志，2006, 23(5): 947-950.
［3］Bethesda MD. Seizures and epilepsy: Hope through research national institute of neurological fisorders and stroke［EB/OL］. http://www.ninds.nih.gov/disorders/epilepsy/detail_epilepsy.htm,2004-05/2013-03-27.
［4］Adeli H, Zhou Z, Dadmehr N. Analysis of EEG records in an epileptic patient using wavelet transform ［J］. J Neurosci Methods, 2003 Feb 15, 123(1): 69-87.［5］Piotr JD. From wavelets to adaptive approximations: Timefrequency parameterization of EEG ［J］. BioMedical Engineering OnLine, 2003, 2(1): 1-30.
［6］Adeli H, GhoshDastidar S, Dadmehr N. A waveletchaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy［J］. IEEE Trans Biomed Eng, 2007 Feb, 54(2): 205-211.
［7］于德介，程军圣，杨宇. 机械故障诊断的HilbertHuang变换方法 ［M］. 北京：科学出版社，2007: 20-26.
［8］Huang NE, Shen Z, Long SR, et al. The empirical mode decomposition and the hilbert spectrum for nonlinear and nonstationary time series analysis［J］. Proc R Soc Lond, 1998: 903-995.
［9］Ira Pohl. The avoidance of (relative) catastrophe, heuristic competence, genuine dynamic weighting and computational issues in heuristic problem solving ［C］//Proceedings of International Joint Conferences on Artificial Intelligence. Stanford: Morgan Kaufmann, 1973: 12-17.
［10］Pearl J, Kim JH. Study in Semiadmissible Heuristics ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1982, 4(4): 392-399.
［11］Qin SR, Zhong YM. A new envelope algorithm of HilbertHuang transform ［J］. Mechanical Systems and Signal Processing, 2006, 20(8): 1941-1952.
［12］许宝杰，张建民，徐小力，等. 抑制EMD端点效应方法的研究［J］. 北京理工大学学报. 2006, 26(3): 196-200.
［13］郭兴明，袁志会.基于小波变换和经验模式分解的心音信号研究 ［J］. 中国生物医学工程学报. 2012, 31(1): 39-44.
［14］邓拥军，王伟，钱成春，等. EMD方法及Hilbert变换中边界问题的处理［J］. 科学通报，2001, 46(3): 257-263.
［15］Cheng JS, Yu DJ, Yang Y. Application of support vector regression machines to the recessing of end effects of HilbertHuang transform ［J］. Mechanical System and Signal Processing, 2007(21): 1197-1211.
［16］陈志彬，陈娟，邱天爽. 基于经验模式分解的脑电棘波检测方法 ［J］. 生物医学工程学杂志.2007,24(5):973-977.
［17］万柏坤，郑旭媛，朱欣，等.癫痫脑电特征波的综合检测分类方法研究［J］.中国生物医学工程学报. 2002， 21(3): 215-218.
［18］Cristianini N, ShaweTaylor J. An Introduction to Support Vector Machine and Other KernelBased Learning Methods［M］. Beijing:Beijing Machinery Industry Press, 2005: 158-172.
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