Grammatical evolution for features of epileptic oscillations in clinical intracranial electroencephalograms

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@Article{Smart20119991,
  author =       "Otis Smart and Ioannis G. Tsoulos and 
                 Dimitris Gavrilis and George Georgoulas",
  title =        "Grammatical evolution for features of epileptic
                 oscillations in clinical intracranial
                 electroencephalograms",
  journal =      "Expert Systems with Applications",
  volume =       "38",
  number =       "8",
  pages =        "9991--9999",
  year =         "2011",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2011.02.009",
  URL =          "http://www.sciencedirect.com/science/article/B6V03-5241FBY-2/2/a7dbf05dd32e0769453262d86a75cb3b",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 evolution, Detector, Epileptic oscillations,
                 Intracranial EEG",
  abstract =     "This paper presents grammatical evolution (GE) as an
                 approach to select and combine features for detecting
                 epileptic oscillations within clinical intracranial
                 electroencephalogram (iEEG) recordings of patients with
                 epilepsy. Clinical iEEG is used in preoperative
                 evaluations of a patient who may have surgery to treat
                 epileptic seizures. Literature suggests that
                 pathological oscillations may indicate the region(s) of
                 brain that cause epileptic seizures, which could be
                 surgically removed for therapy. If this presumption is
                 true, then the effectiveness of surgical treatment
                 could depend on the effectiveness in pinpointing
                 critically diseased brain, which in turn depends on the
                 most accurate detection of pathological oscillations.
                 Moreover, the accuracy of detecting pathological
                 oscillations depends greatly on the selected feature(s)
                 that must objectively distinguish epileptic events from
                 average activity, a task that visual review is
                 inevitably too subjective and insufficient to resolve.
                 Consequently, this work suggests an automated algorithm
                 that incorporates grammatical evolution (GE) to
                 construct the most sufficient feature(s) to detect
                 epileptic oscillations within the iEEG of a patient. We
                 estimate the performance of GE relative to three
                 alternative methods of selecting or combining features
                 that distinguish an epileptic gamma (~65-95 Hz)
                 oscillation from normal activity: forward sequential
                 feature-selection, backward sequential
                 feature-selection, and genetic programming. We
                 demonstrate that a detector with a grammatically
                 evolved feature exhibits a sensitivity and selectivity
                 that is comparable to a previous detector with a
                 genetically programmed feature, making GE a useful
                 alternative to designing detectors.",
}

Genetic Programming entries for Otis L Smart Ioannis G Tsoulos Dimitris Gavrilis George Georgoulas

Citations