Study on Nonlinear Dynamic Characteristic Indexes of Epileptic Electroencephalography and Electroencephalography Subbands_Journal of Biomedical Engineering

Authors：

HUANGRuimei ¹ , DUShouhong ² , CHENZiyi ³ , ZHANGZhen ¹ ,  ZHOUYi ^1,2

1. Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China;
2. College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830002, China;
3. Department of Neurology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China;

Corresponding author：

ZHOUYi, Email: zhouyi@mail.sysu.edu.cn

Keywords：

epilepsy; electroencephalogram; wavelet analysis; nonlinear dynamic characteristic indexes

DOI：

10.7507/1001-5515.20140004

Video：

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Electroencephalogram (EEG) is the primary tool in investigation of the brain science. It is necessary to carry out a deepgoing study into the characteristics and information hidden in EEGs to meet the needs of the clinical research. In this paper, we present a wavelet-nonlinear dynamic methodology for analysis of nonlinear characteristic of EEGs and delta, theta, alpha, and beta sub-bands. We therefore studied the effectiveness of correlation dimension (CD), largest Lyapunov exponen, and approximate entropy (ApEn) in differentiation between the interictal EEG and ictal EEG based on statistical significance of the differences. The results showed that the nonlinear dynamic characteristic of EEG and EEG subbands could be used as effective identification statistics in detecting seizures.

Citation： HUANGRuimei, DUShouhong, CHENZiyi, ZHANGZhen, ZHOUYi. Study on Nonlinear Dynamic Characteristic Indexes of Epileptic Electroencephalography and Electroencephalography Subbands. Journal of Biomedical Engineering, 2014, 31(1): 18-22. doi: 10.7507/1001-5515.20140004 Copy

1.	HSU D, HSU M, GRABENSTATTER H L, et al. Time-frequency analysis using damped-oscillator pseudo-wavelets:application to electrophysiological recordings[J]. J Neurosci Methods, 2010, 194(1):179-192.
2.	GUERRERO C M, TRIGUEROS A M, FRANCO J I. Time-frequency EEG analysis in epilepsy:what is more suitable?[C]//Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, Athens:2005:202-207.
3.	IASEMIDIS L D. Epileptic seizure prediction and control[J]. IEEE Trans Biomed Eng, 2003,50(5):549-558.
4.	SCHREIBER T, SCHMITZ A. Surrogate time series[J]. Physica D, 2000.142(3-4):346-382.
5.	ADELI H, ZHOU Z, DADMEHR N. Analysis of EEG records in an epileptic patient using wavelet transform[J]. J Neurosci Methods, 2003, 123(1):69-87.
6.	RAJENDRA ACHARYA U, VINITHA SREE S, CHUAN A P, et al. Application of NON-linear and wavelet based features for the automated identification of epileptic EEG signals[J]. Int J Neur Syst, 2012, 22(2):14.
7.	ADELI H, GHOSH-DASTIDAR S, DADMEHR N. A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy[J]. IEEE Trans Biomed Eng, 2007, 54(2):205-211.
8.	ROSSO O A, MARTIN M T, FIGLIOLA A, et al. EEG analysis using wavelet-based information tools[J]. J Neurosci Methods, 2006, 153(2):163-182.
9.	RAFIEE J, RAFIEE M A, PRAUSE N, et al. Wavelet basis functions in biomedical signal processing[J]. Expert Syst Appl, 2011, 38(5):6190-6201.
10.	王浩,陈志华,邹飒枫,等.基于Morlet小波变换的EEG信号α波持续性的分析[J].生物医学工程学杂志,2010,27(4):746-748,752.
11.	KUGIUMTZIS D. State space reconstruction parameters in the analysis of chaotic time series--the role of the time window length[J]. Physica D, 1996, 95(1):13-28.
12.	谢勇,徐健学,杨红军,等.皮层脑电时间序列的相空间重构及非线性特征量的提取[J].物理学报,2002,51(2):205-214.
13.	李亚安,张效民,徐德民.混沌吸引子的维数研究[J].探测与控制学报,2002,24(4):43-46,54.
14.	MURUGAVEL A S M, RAMAKRISHNAN S, BALASAMY K, et al. Lyapunov features based EEG signal classification by multi-class SVM[C]//Proceedings of the 2011 World Congress on Information and Communication Technologies, Mumbai:2011:197-201.
15.	王绍宾,王一抗,王志中,等.基于非线性动力学的振幅整合新生儿脑电图分析[J].生物医学工程学杂志,2009,26(6):1201-1205.
16.	KHOA T Q, HUONG N T, TOI V V. Detecting epileptic seizure from scalp EEG using Lyapunov spectrum[J]. Comput Math Methods Med, 2012:847686.
17.	OCAK H. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy[J]. Expert Syst Appl, 2009, 36(2):2027-2036.
18.	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.
19.	洪波,唐庆玉,杨福生,等.近似熵,互近似熵的性质,快速算法及其在脑电与认知研究中的初步应用[J].信号处理,1999,15(2):100-108.
20.	SRINIVASAN V, ESWARAN C, SRIRAAM N. Approximate entropy-based epileptic EEG detection using artificial neural networks[J]. IEEE Trans Inf Technol Biomed, 2007, 11(3):288-295.
21.	GUO L, RIVERO D, PAZOS A. Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks[J]. J Neurosci Methods, 2010, 193(1):156-163.

1. HSU D, HSU M, GRABENSTATTER H L, et al. Time-frequency analysis using damped-oscillator pseudo-wavelets:application to electrophysiological recordings[J]. J Neurosci Methods, 2010, 194(1):179-192.
2. GUERRERO C M, TRIGUEROS A M, FRANCO J I. Time-frequency EEG analysis in epilepsy:what is more suitable?[C]//Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, Athens:2005:202-207.
3. IASEMIDIS L D. Epileptic seizure prediction and control[J]. IEEE Trans Biomed Eng, 2003,50(5):549-558.
4. SCHREIBER T, SCHMITZ A. Surrogate time series[J]. Physica D, 2000.142(3-4):346-382.
5. ADELI H, ZHOU Z, DADMEHR N. Analysis of EEG records in an epileptic patient using wavelet transform[J]. J Neurosci Methods, 2003, 123(1):69-87.
6. RAJENDRA ACHARYA U, VINITHA SREE S, CHUAN A P, et al. Application of NON-linear and wavelet based features for the automated identification of epileptic EEG signals[J]. Int J Neur Syst, 2012, 22(2):14.
7. ADELI H, GHOSH-DASTIDAR S, DADMEHR N. A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy[J]. IEEE Trans Biomed Eng, 2007, 54(2):205-211.
8. ROSSO O A, MARTIN M T, FIGLIOLA A, et al. EEG analysis using wavelet-based information tools[J]. J Neurosci Methods, 2006, 153(2):163-182.
9. RAFIEE J, RAFIEE M A, PRAUSE N, et al. Wavelet basis functions in biomedical signal processing[J]. Expert Syst Appl, 2011, 38(5):6190-6201.
10. 王浩,陈志华,邹飒枫,等.基于Morlet小波变换的EEG信号α波持续性的分析[J].生物医学工程学杂志,2010,27(4):746-748,752.
11. KUGIUMTZIS D. State space reconstruction parameters in the analysis of chaotic time series--the role of the time window length[J]. Physica D, 1996, 95(1):13-28.
12. 谢勇,徐健学,杨红军,等.皮层脑电时间序列的相空间重构及非线性特征量的提取[J].物理学报,2002,51(2):205-214.
13. 李亚安,张效民,徐德民.混沌吸引子的维数研究[J].探测与控制学报,2002,24(4):43-46,54.
14. MURUGAVEL A S M, RAMAKRISHNAN S, BALASAMY K, et al. Lyapunov features based EEG signal classification by multi-class SVM[C]//Proceedings of the 2011 World Congress on Information and Communication Technologies, Mumbai:2011:197-201.
15. 王绍宾,王一抗,王志中,等.基于非线性动力学的振幅整合新生儿脑电图分析[J].生物医学工程学杂志,2009,26(6):1201-1205.
16. KHOA T Q, HUONG N T, TOI V V. Detecting epileptic seizure from scalp EEG using Lyapunov spectrum[J]. Comput Math Methods Med, 2012:847686.
17. OCAK H. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy[J]. Expert Syst Appl, 2009, 36(2):2027-2036.
18. 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.
19. 洪波,唐庆玉,杨福生,等.近似熵,互近似熵的性质,快速算法及其在脑电与认知研究中的初步应用[J].信号处理,1999,15(2):100-108.
20. SRINIVASAN V, ESWARAN C, SRIRAAM N. Approximate entropy-based epileptic EEG detection using artificial neural networks[J]. IEEE Trans Inf Technol Biomed, 2007, 11(3):288-295.
21. GUO L, RIVERO D, PAZOS A. Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks[J]. J Neurosci Methods, 2010, 193(1):156-163.

Journal of Biomedical Engineering

Study on Nonlinear Dynamic Characteristic Indexes of Epileptic Electroencephalography and Electroencephalography Subbands

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