A novel genetic programming approach for epileptic seizure detection

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

  author =       "Arpit Bhardwaj and Aruna Tiwari and Ramesh Krishna and 
                 Vishaal Varma",
  title =        "A novel genetic programming approach for epileptic
                 seizure detection",
  journal =      "Computer Methods and Programs in Biomedicine",
  volume =       "124",
  pages =        "2--18",
  year =         "2016",
  ISSN =         "0169-2607",
  DOI =          "doi:10.1016/j.cmpb.2015.10.001",
  URL =          "http://www.sciencedirect.com/science/article/pii/S016926071500262X",
  abstract =     "The human brain is a delicate mix of neurons (brain
                 cells), electrical impulses and chemicals, known as
                 neurotransmitters. Any damage has the potential to
                 disrupt the workings of the brain and cause seizures.
                 These epileptic seizures are the manifestations of
                 epilepsy. The electroencephalograph (EEG) signals
                 register average neuronal activity from the cerebral
                 cortex and label changes in activity over large areas.
                 A detailed analysis of these electroencephalograph
                 (EEG) signals provides valuable insights into the
                 mechanisms instigating epileptic disorders. Moreover,
                 the detection of interictal spikes and epileptic
                 seizures in an EEG signal plays an important role in
                 the diagnosis of epilepsy. Automatic seizure detection
                 methods are required, as these epileptic seizures are
                 volatile and unpredictable. This paper deals with an
                 automated detection of epileptic seizures in EEG
                 signals using empirical mode decomposition (EMD) for
                 feature extraction and proposes a novel genetic
                 programming (GP) approach for classifying the EEG
                 signals. Improvements in the standard GP approach are
                 made using a Constructive Genetic Programming (CGP) in
                 which constructive crossover and constructive subtree
                 mutation operators are introduced. A hill climbing
                 search is integrated in crossover and mutation
                 operators to remove the destructive nature of these
                 operators. A new concept of selecting the Globally
                 Prime offspring is also presented to select the best
                 fitness offspring generated during crossover. To
                 decrease the time complexity of GP, a new dynamic
                 fitness value computation (DFVC) is employed to
                 increase the computational speed. We conducted five
                 different sets of experiments to evaluate the
                 performance of the proposed model in the classification
                 of different mixtures of normal, interictal and ictal
                 signals, and the accuracies achieved are outstandingly
                 high. The experimental results are compared with the
                 existing methods on same datasets, and these results
                 affirm the potential use of our method for accurately
                 detecting epileptic seizures in an EEG signal.",
  keywords =     "genetic algorithms, genetic programming, Constructive
                 crossover, Dynamic fitness value computation,

Genetic Programming entries for Arpit Bhardwaj Aruna Tiwari M Ramesh Krishna M Vishaal Varma