Automatic Classification of Epileptic Electroencephalogram Signal Based on Improved Multivariate Multiscale Entropy_Journal of Biomedical Engineering

Authors：

 XUYonghong , CUIJie , HONGWenxue , LIANGHuijuan

Institute of Biomedical Engineering, Yanshan University, Qinhuangdao 066004, China;

Corresponding author：

XUYonghong, Email: xyh@ysu.edu.cn

Keywords：

multivariate multiscale entropy; epilepsy; embedding theory; wavelet packet transform

DOI：

10.7507/1001-5515.20150047

Video：

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Traditional sample entropy fails to quantify inherent long-range dependencies among real data. Multiscale sample entropy (MSE) can detect intrinsic correlations in data, but it is usually used in univariate data. To generalize this method for multichannel data, we introduced multivariate multiscale entropy into multiscale signals as a reflection of the nonlinear dynamic correlation. But traditional multivariate multiscale entropy has a large quantity of computation and costs a large period of time and space for more channel system, so that it can not reflect the correlation between variables timely and accurately. In this paper, therefore, an improved multivariate multiscale entropy embeds on all variables at the same time, instead of embedding on a single variable as in the traditional methods, to solve the memory overflow while the number of channels rise, and it is more suitable for the actual multivariate signal analysis. The method was tested in simulation data and Bonn epilepsy dataset. The simulation results showed that the proposed method had a good performance to distinguish correlation data. Bonn epilepsy dataset experiment also showed that the method had a better classification accuracy among the five data set, especially with an accuracy of 100% for data collection of Z and S.

Citation： XUYonghong, CUIJie, HONGWenxue, LIANGHuijuan. Automatic Classification of Epileptic Electroencephalogram Signal Based on Improved Multivariate Multiscale Entropy. Journal of Biomedical Engineering, 2015, 32(2): 256-262. doi: 10.7507/1001-5515.20150047 Copy

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13.	COSTA M, PENG C K, GOLDBERGER A L, et al. Multiscale entropy analysis of human gait dynamics[J]. Physica A, 2003, 330:53-60.
14.	王俊, 宁新宝, 李锦, 等.心电图的多尺度熵分析[J].生物医学工程学杂志, 2007, 24(5):978-980.
15.	POLAT K, GUNES S. Classification of epilepty form EEG using a hybrid system based on decision tree classifier and fast Fourier transform[J]. Appl Math Comput, 2007, 187(2):1017-1026.
16.	LIMA C A, COELHO A L, EISENCRAFT M. Tackling EEG signal classification with least squares support vector machines:a sensitivity analysis study[J]. Comput Biol Med, 2010, 40(8):705-714.
17.	ISCAN Z, DOKUR Z, DEMIRALP T. Classification of electroencephalogram signals with combined time and frequency features[J]. Expert Syst Appl, 2011, 38(8):10499-10505.
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20.	TZALLAS A T, TSIPOURAS M G, FOTIADIS D I. Epileptic seizure detection in EEGs using time-frequency analysis[J]. IEEE Trans Inf Technol Biomed, 2009, 13(5):703-710.
21.	LIANG S F, WANG H C, CHANG W L. Combination of EEG complexity and spectral analysis forepilepsy diagnosis and seizure detection[J]. EURASIP J Adv Signal Proces, 2010, 2010:62.

1. PINCUS S M. Approximate entropy as a measure of system complexity[J]. Proc Natl Acad Sci U S A, 1991, 88(6):2297-2301.
2. RICHMAN J S, MOORMAN J R. Physiological time-series analysis using approximate entropy and sample entropy[J]. Am J Physiol Heart Circ Physiol, 2000, 278(6):H2039-H2049.
3. LAKE D E, RICHMAN J S, GRIFFIN M P, et al. Sample entropy analysis of neonatal heart rate variability[J]. Am J Physiol Regul Integr Comp Physiol, 2002, 283(3):R789-R797.
4. AKAREDDY M S, KULKARNI P K. EEG signal classification for epilepsy seizure detection using improved approximate entropy[J].Int J Public Health Sci, 2013, 2(1):23-32.
5. 白冬梅, 邱天爽, 李小兵.样本熵及在脑电癫痫检测中的应用[J].生物医学工程学杂志, 2007, 24(1):200-205.
6. 袁琦, 周卫东, 李淑芳, 等.基于ELM和近似熵的脑电信号检测方法[J].仪器仪表学报, 2012, 33(3):514-519.
7. 李小俚, 欧阳高翔, 关新平, 等.基于EEG模糊相似性的癫痫发作预测[J].中国生物医学工程学报, 2006, 25(3):346-350, 381.
8. GÜLER Ï, ÜBEYLI E D. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients[J]. J Neurosci Methods, 2005, 148(2):113-121.
9. 李鹏, 刘澄玉, 李丽萍, 等.多尺度多变量模糊熵分析[J].物理学报, 2013, 62(12):122-130.
10. ACHARYA U R, MOLINARI F, SREE S V, et al. Automated diagnosis of epileptic EEG using entropies[J]. Biomed Signal Process Control, 2012, 7(1):401-408.
11. AHMED M U, MANDIC D P. Multivariate multiscale entropy:a tool for complexity analysis of multichannel data[J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2011, 84(6 Pt 1):061918.
12. CAO L, MEES A, JUDD K. Dynamics from multivariate time series[J]. Phys D:Nonlinear Phenomena, 1998, 121(2):75-88.
13. COSTA M, PENG C K, GOLDBERGER A L, et al. Multiscale entropy analysis of human gait dynamics[J]. Physica A, 2003, 330:53-60.
14. 王俊, 宁新宝, 李锦, 等.心电图的多尺度熵分析[J].生物医学工程学杂志, 2007, 24(5):978-980.
15. POLAT K, GUNES S. Classification of epilepty form EEG using a hybrid system based on decision tree classifier and fast Fourier transform[J]. Appl Math Comput, 2007, 187(2):1017-1026.
16. LIMA C A, COELHO A L, EISENCRAFT M. Tackling EEG signal classification with least squares support vector machines:a sensitivity analysis study[J]. Comput Biol Med, 2010, 40(8):705-714.
17. ISCAN Z, DOKUR Z, DEMIRALP T. Classification of electroencephalogram signals with combined time and frequency features[J]. Expert Syst Appl, 2011, 38(8):10499-10505.
18. GUO L, RIVERO D, DORADO J, et al. Automatic feature extraction using genetic programming:an application to epileptic EEG classification[J]. Expert Syst Appl, 2011, 38(8):10425-10436.
19. CHUA K C, CHANDRAN V, ACHARYA R, et al. Automatic identification of epilepsy by HOS and power spectrum parameters using EEG signals:a comparative study[J]. Conf Proc IEEE Eng Med Biol Soc, 2008, 2008:3824-3827.
20. TZALLAS A T, TSIPOURAS M G, FOTIADIS D I. Epileptic seizure detection in EEGs using time-frequency analysis[J]. IEEE Trans Inf Technol Biomed, 2009, 13(5):703-710.
21. LIANG S F, WANG H C, CHANG W L. Combination of EEG complexity and spectral analysis forepilepsy diagnosis and seizure detection[J]. EURASIP J Adv Signal Proces, 2010, 2010:62.

Journal of Biomedical Engineering

Automatic Classification of Epileptic Electroencephalogram Signal Based on Improved Multivariate Multiscale Entropy

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