Abstract
With the increasing incidence of epilepsy, we need to detect the epilepsy with high efficiency to avoid the disease attack. In this paper, we proposed two novel feature extraction methods for automatic epileptic seizure detection with high performance based on the statistic properties of complex network. One is the degree centrality combined with the linear features as the features to classify the epileptic EEG signal. Firstly, we transformed the time series into complex network by using horizontal visibility graph (HVG). Then we extracted the degree centrality of the complex network combined with the fluctuation index and variation coefficient as the three-dimensional features and the classification accuracy is up to 95.98%. To enhance the difference of the degree centrality feature, we put the other new feature. That is the improved degree centrality and chose the improved degree centrality as the single feature to classify the signal. Experimental results showed that the classification accuracy of this single feature is 96.50%.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61671220, 61640218, 61201428), the Shandong Distinguished Middle-aged and Young Scientist Encourage and Reward Foundation, China (Grant No. ZR2016FB14), the Project of Shandong Province Higher Educational Science and Technology Program, China (Grant No. J16LN07), the Shandong Province Key Research and Development Program, China (Grant No. 2016GGX101022).
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Liu, H., Meng, Q., Wei, Y., Zhang, Q., Liu, M., Zhou, J. (2017). A New Epileptic Seizure Detection Method Based on Degree Centrality and Linear Features. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_51
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DOI: https://doi.org/10.1007/978-3-319-59081-3_51
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