Epileptic Seizure Detection Using Genetically Programmed Artificial Features

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  title =        "Epileptic Seizure Detection Using Genetically
                 Programmed Artificial Features",
  author =       "Hiram Firpi and Erik D. Goodman and Javier Echauz",
  journal =      "IEEE Transactions on Biomedical Engineering",
  year =         "2007",
  volume =       "54",
  number =       "2",
  pages =        "212--224",
  DOI =          "doi:10.1109/TBME.2006.886936",
  ISSN =         "0018-9294",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, diseases,
                 electroencephalography, genetic algorithms, medical
                 signal detection, medical signal processing, signal
                 classification, signal reconstruction730.6 hr,
                 epileptic seizure detection, genetic programming,
                 genetically programmed artificial features, k-nearest
                 neighbour classifier, patient-specific epilepsy seizure
                 detectors, reconstructed state-space trajectories",
  abstract =     "Patient-specific epilepsy seizure detectors were
                 designed based on the genetic programming artificial
                 features algorithm, a general-purpose, methodic
                 algorithm comprised by a genetic programming module and
                 a k-nearest neighbour classifier to create synthetic
                 features. Artificial features are an extension to
                 conventional features, characterised by being
                 computer-coded and may not have a known physical
                 meaning. In this paper, artificial features are
                 constructed from the reconstructed state-space
                 trajectories of the intracranial EEG signals intended
                 to reveal patterns indicative of epileptic seizure
                 onset. The algorithm was evaluated in seven patients
                 and validation experiments were carried out using 730.6
                 hr of EEG recordings. The results with the artificial
                 features compare favourably with previous benchmark
                 work that used a handcrafted feature. Among other
                 results, 88 out of 92 seizures were detected yielding a
                 low false negative rate of 4.35percent",

Genetic Programming entries for Hiram A Firpi Erik Goodman Javier Echauz