Detection of epileptic seizure in EEG signals using linear least squares preprocessing

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@Article{Zamir:2016:CMPB,
  author =       "Z. Roshan Zamir",
  title =        "Detection of epileptic seizure in {EEG} signals using
                 linear least squares preprocessing",
  journal =      "Computer Methods and Programs in Biomedicine",
  year =         "2016",
  ISSN =         "0169-2607",
  DOI =          "doi:10.1016/j.cmpb.2016.05.002",
  URL =          "http://www.sciencedirect.com/science/article/pii/S016926071530273X",
  abstract =     "An epileptic seizure is a transient event of abnormal
                 excessive neuronal discharge in the brain. This
                 unwanted event can be obstructed by detection of
                 electrical changes in the brain that happen before the
                 seizure takes place. The automatic detection of
                 seizures is necessary since the visual screening of EEG
                 recordings is a time consuming task and requires
                 experts to improve the diagnosis. Much of the prior
                 research in detection of seizures has been developed
                 based on artificial neural network, genetic
                 programming, and wavelet transforms. Although the
                 highest achieved accuracy for classification is
                 100percent, there are drawbacks such as, existence of
                 unbalanced datasets and the lack of investigations in
                 performances consistency. To address these, four linear
                 least squares-based preprocessing models are proposed
                 to extract key features of an EEG signal in order to
                 detect seizures. The first two models are newly
                 developed. The original signal (EEG) is approximated by
                 a sinusoidal curve. Its amplitude is formed by a
                 polynomial function and compared with the pre developed
                 spline function. Different statistical measures namely
                 classification accuracy, true positive and negative
                 rates, false positive and negative rates and precision
                 are used to assess the performance of the proposed
                 models. These metrics are derived from confusion
                 matrices obtained from classifiers. Different
                 classifiers are used over the original dataset and the
                 set of extracted features. The proposed models
                 significantly reduce the dimension of the
                 classification problem and the computational time while
                 the classification accuracy is improved in most cases.
                 The first and third models are promising feature
                 extraction methods with the classification accuracy of
                 100percent. Logistic, LazyIB1, LazyIB5, and J48 are the
                 best classifiers. Their true positive and negative
                 rates are 1 while false positive and negative rates are
                 zero and the corresponding precision values are 1.
                 Numerical results suggest that these models are robust
                 and efficient for detecting epileptic seizure.",
  keywords =     "genetic algorithms, genetic programming, Biological
                 signal classification, Signal approximation, Feature
                 extraction, Data analysis, Linear least squares
                 problems, EEG Seizure detection",
}

Genetic Programming entries for Z Roshan Zamir

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