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Epileptic seizure detection using a neural network ensemble method and wavelet transform

This paper presents a new method to automate the process of epileptic seizure detection in electroencephalogram (EEG) signals using wavelet transform and an improved version of negative correlation learning (NCL) algorithm. An improved version of NCL is proposed by incorporating the capability of gating network, as a dynamic combining part of the mixture of experts (ME), into the combining outputs…

  • Knihovna Akademie věd České republiky (opens in new window)
  • Ebrahimpour , Reza
  • Babakhani, Kioumars
  • Abbaszadeh Arani, Seyed Ali Asghar
  • Masoudnia, Saeed
  • Epileptic seizure
  • electroencephalogram (EEG) signals
  • discrete wavelet transform
  • mixture of experts
  • negatively correlated learning
  • model:article
  • Knihovna Akademie věd České republiky (opens in new window)
  • Ebrahimpour , Reza
  • Babakhani, Kioumars
  • Abbaszadeh Arani, Seyed Ali Asghar
  • Masoudnia, Saeed
  • Epileptic seizure
  • electroencephalogram (EEG) signals
  • discrete wavelet transform
  • mixture of experts
  • negatively correlated learning
  • model:article
  • Česká digitální knihovna
  • http://creativecommons.org/publicdomain/mark/1.0/
  • false
  • policy:public
  • Neural network world: international journal on neural and mass-parallel computing and information systems | 2012 Volume:22 | Number:3
  • uuid:0e6b5441-4533-4f11-a89a-2ebaf4b46678
  • https://cdk.lib.cas.cz/client/handle/uuid:0e6b5441-4533-4f11-a89a-2ebaf4b46678
  • uuid:0e6b5441-4533-4f11-a89a-2ebaf4b46678
  • doi:10.14311/NNW.2012.22.017
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  • svazek
  • eng
  • eng
  • Czech Republic
  • 336_CZ_DigitalLibrary_article
  • 2021-06-01T12:19:28.026Z
  • 2021-06-01T12:19:28.026Z