Abstract
Epileptic seizure detection plays an important role in the diagnosis of epilepsy. High-performance automatic detection method of epilepsy can reduce the workload of medical workers clinically, which has important clinical research significance. In this paper, we propose a new epileptic seizure detection method based on the statistic fusion feature of complex network. Firstly, we convert electroencephalogram (EEG) signals into complex network using horizontal visibility graph and weighted horizontal visibility graph. Then, we extract the average degree square and average weighted degree of complex network. Finally, the weighted sum of this two features is calculated as a single dimensional feature to classify the epileptic EEG signals. Experimental results show that the classification accuracy based on the feature fusion is up to 99%. It indicates that the classification accuracy of the single feature based on feature fusion is very high and the proposed method is effective to classify EEG signals.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 61671220, 61201428), 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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Zhang, H., Meng, Q., Liu, M., Li, Y. (2018). A New Epileptic Seizure Detection Method Based on Fusion Feature of Weighted Complex Network. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_94
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DOI: https://doi.org/10.1007/978-3-319-92537-0_94
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