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International Conference on Intelligent Science and Big Data Engineering

IScIDE 2018: Intelligence Science and Big Data Engineering pp 672–682Cite as

A Novel Seizure Prediction Method Based on Generative Features

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11266))

Abstract

The diagnosis of epilepsy in hospital is mostly judged by experienced medical personnel visually observing brain waves combined with some characteristic clinical manifestations. As brain signal’s complexity the understanding of EEG signal still remains challenge. In this paper we proposed a novel seizure prediction method based on generative features. Then predict seizures according to the EEG information of the epileptic patients. And we use the Extreme learning machine as the classifier of generative features. Finally, we get the highest accuracy score of 98%.

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Acknowledgments

This research is partially sponsored by Natural Science Foundation of China (Nos. 61672070, 81471770, 61572004), the Beijing Municipal Natural Science Foundation (grant number 4182005).

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Authors and Affiliations

  1. Faculty of Information Technology, Computer Science, Beijing University of Technology, Beijing, China

    Lili Liu, Lijuan Duan, Ying Xiao & Yuanhua Qiao

Authors
  1. Lili Liu
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  2. Lijuan Duan
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  3. Ying Xiao
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  4. Yuanhua Qiao
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Corresponding author

Correspondence to Yuanhua Qiao .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Shanghai Jiao Tong University, Shanghai, China

    Kai Yu

  3. Tsinghua University, Beijing, China

    Jiwen Lu

  4. Central China Normal University, Wuhan, China

    Xingpeng Jiang

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Liu, L., Duan, L., Xiao, Y., Qiao, Y. (2018). A Novel Seizure Prediction Method Based on Generative Features. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_59

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  • DOI: https://doi.org/10.1007/978-3-030-02698-1_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02697-4

  • Online ISBN: 978-3-030-02698-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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