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Application of Fuzzy Similarity to Prediction of Epileptic Seizures Using EEG Signals

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Fuzzy Systems and Knowledge Discovery (FSKD 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3613))

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Abstract

The prediction of epileptic seizures is a very attractive issue for all patients suffering from epilepsy in EEG (electroencephalograph) signals. It can assist to develop an intervention system to control / prevent upcoming seizures and change the current treatment method of epilepsy. This paper describes a new method based on wavelet transform and fuzzy similarity measurement to predict the seizures by using EEG signals. One part of the method is to calculate the energy and entropy of EEG data at the different scale; another part of this method is to calculate the similarity between the features set of the reference segment and the test segment using fuzzy measure. The test results of real rats show this method detect temporal dynamic changes prior to a seizure in real time.

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Author information

Authors and Affiliations

  1. The Centre of Excellence for Research in Computational Intelligence and Applications, School of Computer Science, The University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    Xiaoli Li & Xin Yao

Authors
  1. Xiaoli Li
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  2. Xin Yao
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

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© 2005 Springer-Verlag Berlin Heidelberg

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Li, X., Yao, X. (2005). Application of Fuzzy Similarity to Prediction of Epileptic Seizures Using EEG Signals. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_80

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  • DOI: https://doi.org/10.1007/11539506_80

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28312-6

  • Online ISBN: 978-3-540-31830-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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