A new approach for EEG signal classification of schizophrenic and control participants

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@Article{Sabeti20112063,
  author =       "M. Sabeti and S. D. Katebi and R. Boostani and 
                 G. W. Price",
  title =        "A new approach for EEG signal classification of
                 schizophrenic and control participants",
  journal =      "Expert Systems with Applications",
  volume =       "38",
  number =       "3",
  pages =        "2063--2071",
  year =         "2011",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2010.07.145",
  URL =          "http://www.sciencedirect.com/science/article/B6V03-50PJWS9-5/2/bb2bad471833b7c3a03419c6fef86266",
  keywords =     "genetic algorithms, genetic programming,
                 Schizophrenic, EEG classification, Channel selection,
                 Features reduction",
  abstract =     "This paper is concerned with a two stage procedure for
                 analysis and classification of electroencephalogram
                 (EEG) signals for twenty schizophrenic patients and
                 twenty age-matched control participants. For each case,
                 20 channels of EEG are recorded. First, the more
                 informative channels are selected using the mutual
                 information techniques. Then, genetic programming is
                 employed to select the best features from the selected
                 channels. Several features including autoregressive
                 model parameters, band power and fractal dimension are
                 used for the purpose of classification. Both linear
                 discriminant analysis (LDA) and adaptive boosting
                 (Adaboost) are trained using tenfold cross validation
                 to classify the reduced feature set and a
                 classification accuracy of 85.90% and 91.94% is
                 obtained by LDA and Adaboost, respectively. Another
                 interesting observation from the channel selection
                 procedure is that most of the selected channels are
                 located in the prefrontal and temporal lobes confirming
                 neuropsychological and neuroanatomical findings. The
                 results obtained by the proposed approach are compared
                 with a one stage procedure, the principal component
                 analysis (PCA)-based feature selection, using only 100
                 features selected from all channels. It is illustrated
                 that the two stage procedure consisting of channel
                 selection followed by feature reduction gives a more
                 enhanced results in an efficient computation time.",
}

Genetic Programming entries for Malihe Sabeti Serajeddin Katebi Reza Boostani Greg W Price

Citations