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Author:

Liu, Lili (Liu, Lili.) | Duan, Lijuan (Duan, Lijuan.) (Scholars:段立娟) | Xiao, Ying (Xiao, Ying.) | Qiao, Yuanhua (Qiao, Yuanhua.) (Scholars:乔元华)

Indexed by:

CPCI-S EI Scopus

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%.

Keyword:

Epilepsy Deep learning Neural network Seizure prediction

Author Community:

  • [ 1 ] [Liu, Lili]Beijing Univ Technol, Fac Informat Technol Comp Sci, Beijing, Peoples R China
  • [ 2 ] [Duan, Lijuan]Beijing Univ Technol, Fac Informat Technol Comp Sci, Beijing, Peoples R China
  • [ 3 ] [Xiao, Ying]Beijing Univ Technol, Fac Informat Technol Comp Sci, Beijing, Peoples R China
  • [ 4 ] [Qiao, Yuanhua]Beijing Univ Technol, Fac Informat Technol Comp Sci, Beijing, Peoples R China

Reprint Author's Address:

  • 乔元华

    [Qiao, Yuanhua]Beijing Univ Technol, Fac Informat Technol Comp Sci, Beijing, Peoples R China

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Source :

INTELLIGENCE SCIENCE AND BIG DATA ENGINEERING

ISSN: 0302-9743

Year: 2018

Volume: 11266

Page: 672-682

Language: English

Cited Count:

WoS CC Cited Count: 1

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 10

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