Entropy and complexity measures for EEG signal classification of schizophrenic and control participants

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@Article{Sabeti2009263,
  author =       "Malihe Sabeti and Serajeddin Katebi and 
                 Reza Boostani",
  title =        "Entropy and complexity measures for EEG signal
                 classification of schizophrenic and control
                 participants",
  journal =      "Artificial Intelligence in Medicine",
  volume =       "47",
  number =       "3",
  pages =        "263--274",
  year =         "2009",
  ISSN =         "0933-3657",
  DOI =          "doi:10.1016/j.artmed.2009.03.003",
  URL =          "http://www.sciencedirect.com/science/article/B6T4K-4W5VD7D-1/2/e0544c622bf428467d21d81a8257be7b",
  keywords =     "genetic algorithms, genetic programming,
                 Schizophrenic, Entropy, Complexity, Features selection,
                 EEG classification",
  abstract =     "Objective

                 In this paper, electroencephalogram (EEG) signals of 20
                 schizophrenic patients and 20 age-matched control
                 participants are analyzed with the objective of
                 classifying the two groups.

                 Materials and methods

                 For each case, 20 channels of EEG are recorded. Several
                 features including Shannon entropy, spectral entropy,
                 approximate entropy, Lempel-Ziv complexity and Higuchi
                 fractal dimension are extracted from EEG signals.
                 Leave-one (participant)-out cross-validation is used
                 for reliable estimate of the separability of the two
                 groups. The training set is used for training the two
                 classifiers, namely, linear discriminant analysis (LDA)
                 and adaptive boosting (Adaboost). Each classifier is
                 assessed using the test dataset.

                 Results

                 A classification accuracy of 86percent and 90percent is
                 obtained by LDA and Adaboost respectively. For further
                 improvement, genetic programming is employed to select
                 the best features and remove the redundant ones.
                 Applying the two classifiers to the reduced feature
                 set, a classification accuracy of 89percent and
                 91percent is obtained by LDA and Adaboost respectively.
                 The proposed technique is compared and contrasted with
                 a recently reported method and it is demonstrated that
                 a considerably enhanced performance is
                 achieved.

                 Conclusion

                 This study shows that EEG signals can be a useful tool
                 for discrimination of the schizophrenic and control
                 participants. It is suggested that this analysis can be
                 a complementary tool to help psychiatrists diagnosing
                 schizophrenic patients.",
}

Genetic Programming entries for Malihe Sabeti Serajeddin Katebi Reza Boostani

Citations