Epilepsy Electroencephalogram Signal Analysis Based on Improved <i>k</i>-nearest Neighbor Network_Journal of Biomedical Engineering

Authors：

YUXian ¹ , LIUChengcheng ¹ , DAIJiafei ² , LIJin ³ ,  WANGJun ¹ , HOUFengzhen ⁴

1. Image Processing and Image Communications Key Lab, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
2. Nanjing General Hospital of Nanjing Military Command, Nanjing 210002, China;
3. College of Physics and Information Technology, Shaanxi Normal University, Xi'an 710062, China;
4. School of Science, China Pharmaceutical University, Nanjing 210009, China;

Corresponding author：

WANGJun, Email: wangj@njupt.edu.cn

Keywords：

improved k-nearest neighbor network; time series; epilepsy; analysis of electroencephalogram signals

DOI：

10.7507/1001-5515.20160167

Video：

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Abstract Full text Figures/Tables Video References Cited by

The study of complex networks has become a hot research area of electroencephalogram signal. Electroencephalogram time series generated by the network keeps node information of network, so studying the time series from the network can also achieve the purpose of study epileptic electroencephalogram. In this paper, we propose a method to analyze epileptic electroencephalogram based on time series which is based on improved k-nearest neighbor network. The results of the experiment showed that studying power spectrum of time series from network was easier than power spectrum of time series directly generated from the original brain data to distinguish between normal controls and epileptic patients. In addition, studying the clustering coefficient of improved k-nearest neighbor network was able to distinguish between normal persons and patients with epilepsy. This study can provide important reference for the study of epilepsy and clinical diagnosis.

Citation： YUXian, LIUChengcheng, DAIJiafei, LIJin, WANGJun, HOUFengzhen. Epilepsy Electroencephalogram Signal Analysis Based on Improved k-nearest Neighbor Network. Journal of Biomedical Engineering, 2016, 33(6): 1039-1045. doi: 10.7507/1001-5515.20160167 Copy

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12.	WENG Tongfeng, ZHAO Yi, SMALL M, et al. Time-series analysis of networks: exploring the structure with random walks[J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2014, 90(2): 022804.
13.	SCHREIBER T, SCHMITZ A. Surrogate time series[J]. Physica D, 2000, 142(3/4): 346-382.
14.	STAM C J. Nonlinear dynamical analysis of EEG and MEG: review of an emerging field[J]. Clin Neurophysiol, 2005, 116(10): 2266-2301.
15.	TAKENS F. Dynamical systems and turbulence,warwick 1980[M]. Berlin: Springer Berlin Heidelberg, 1981: 366-381.
16.	KIM H S, EYKHOLT R, SALAS J D. Nonlinear dynamics, delay times, and embedding Windows[J]. Physica D, 1999, 127(1/2): 48-60.
17.	DONNER R V, SMALL M, DONGES J F, et al. Recurrence-based time series analysis by means of complex network methods[J]. Int J Bifurcat Chaos, 2011, 21(4): 1019-1046.

1. IASEMIDIS L D, SHIAU D S, CHAOVALITWONGSE W, et al. Adaptive epileptic seizure prediction system[J]. IEEE Trans Biomed Eng, 2003, 50(5): 616-627.
2. FISHER R S, BOAS W V, BLUME W, et al. Epileptic seizures and epilepsy: Definitions proposed by the International League against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE)[J]. Epilepsia, 2005, 46(4): 470-472.
3. 姚文坡, 刘铁兵,戴加飞,等.脑电信号的多尺度排列熵分析[J].物理学报,2014(7):078701-078704.
4. 黄小娜, 张军英.癫痫脑电信号的非线性分析方法[J].河西学院学报,2010,26(2):29-32.
5. LACASA L, GÓMEZ-GARDEÑES J. Correlation dimension of complex networks[J]. Phys Rev Lett, 2013, 110(16): 168703.
6. 王俊, 马千里. Multiscale entropy based study of the pathological time series[J]. ChinPhysB, 2008, 17(12): 4424-4427.
7. 吴莎. 基于改进的符号转移熵的生理电信号耦合网络研究[D].南京:南京邮电大学,2014.
8. 董泽芹, 侯凤贞,戴加飞,等.基于Kendall改进的同步算法癫痫脑网络分析[J].物理学报,2014(20):208701-208705.
9. PESSOA L. Understanding brain networks and brain organization[J]. Phys Life Rev, 2014, 11(3): 400-435.
10. ZHAO Yi, WENG Tongfeng, YE Shengkui. Geometrical invariability of transformation between a time series and a complex network[J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2014, 90(1): 012804.
11. YUTAKA S, TOHRU I, TAKAOMI S. From networks to time series[J]. Phys Rev Lett, 2012, 109(15): 684-691.
12. WENG Tongfeng, ZHAO Yi, SMALL M, et al. Time-series analysis of networks: exploring the structure with random walks[J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2014, 90(2): 022804.
13. SCHREIBER T, SCHMITZ A. Surrogate time series[J]. Physica D, 2000, 142(3/4): 346-382.
14. STAM C J. Nonlinear dynamical analysis of EEG and MEG: review of an emerging field[J]. Clin Neurophysiol, 2005, 116(10): 2266-2301.
15. TAKENS F. Dynamical systems and turbulence,warwick 1980[M]. Berlin: Springer Berlin Heidelberg, 1981: 366-381.
16. KIM H S, EYKHOLT R, SALAS J D. Nonlinear dynamics, delay times, and embedding Windows[J]. Physica D, 1999, 127(1/2): 48-60.
17. DONNER R V, SMALL M, DONGES J F, et al. Recurrence-based time series analysis by means of complex network methods[J]. Int J Bifurcat Chaos, 2011, 21(4): 1019-1046.

Journal of Biomedical Engineering

Epilepsy Electroencephalogram Signal Analysis Based on Improved k-nearest Neighbor Network

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