Analysing Epileptic EEG Signals Based on Improved Transition Network
The epileptic automatic detection was very significance in clinical. The nonlinear time series analysis method based on complex network theory provided a new perspective understand the dynamics of nonlinear time series. In this paper, we proposed a new epileptic seizure detection method based on statistical properties of improved transition network. First, we improved the transition network and electroencephalogram (EEG) signal was constructed into the improved transition network. Then, based on the statistical characteristics of improved transition network, the mathematical expectation of node distribution in a network was extracted as the classification feature. Finally, the performance of the algorithm was evaluated by classifying the epileptic EEG dataset. Experimental results showed that the classification accuracy of proposed algorithm is 97%.
KeywordsComplex network Improved transition network Mathematical expectation Seizure detection EEG
This work was supported by the National Natural Science Foundation of China (Grant No. 61671220, 61701192, 61640218), the Natural Science Foundation of Shandong Province, China (Grant No. ZR2017QF004).
- 1.Yuan, Q., Zhou, W., Li, S., Cai, D.: Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res. 96(1–2), 29–38 (2011)Google Scholar
- 2.Kumar, Y., Dewal, M.L., Anand, R.S.: Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing 133, 271–279 (2014)Google Scholar
- 4.Pachori, R.B., Bajaj, V.: Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Comput. Methods Programs Biomed. 104(3), 373–381 (2011)Google Scholar
- 6.Patnaik, L.M., Manyam, O.K.: Epileptic EEG detection using neural networks and post-classification. Comput. Methods Programs Biomed. 91(2), 100–109 (2008)Google Scholar
- 7.Zhang, J., Small, M.: Complex network from pseudoperiodic time series: topology versus dynamics. Phys. Rev. Lett. 96, 238701 (2006)Google Scholar
- 10.Luque, B., Lacasa, L., Ballesteros, F., Luque, L.: Horizontal visibility graphs: exact results for random time series. Phys. Rev. E 80(4), 046103 (2009)Google Scholar
- 12.Li, X., Sun, M., Gao, C., Han, D., Wang, M.: The parametric modified limited penetrable visibility graph for constructing complex networks from time series. Phys. A Stat. Mech. Appl. 492, 1097–1106 (2018)Google Scholar
- 13.Supriya, S., Siuly, S., Wang, H., Cao, J., Zhang, Y.: Weighted visibility graph with complex network features in the detection of epilepsy. IEEE Access 4, 6554–6566 (2016)Google Scholar
- 15.Lake, D.E., Richman, J.S., Griffin, M.P., Moorman, J.R.: Sample entropy analysis of neonatal heart rate variability. Am. J. Physiol. Regul. Integr. Comp. Physiol. 283, 789–797 (2002)Google Scholar
- 16.Meng, Q.F., Chen, S.S., Chen, Y.H., Feng, Z.Q.: Automatic detection of epileptic EEG based on recurrence quantification analysis and SVM. Acta Phys. Sin. 63(5), 050506 (2014)Google Scholar