Genetic programming of conventional features to detect seizure precursors

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Otis Smart and Hiram Firpi and George Vachtsevanos",
  title =        "Genetic programming of conventional features to detect
                 seizure precursors",
  journal =      "Engineering Applications of Artificial Intelligence",
  year =         "2007",
  volume =       "20",
  number =       "8",
  pages =        "1070--1085",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, C-features,
                 Feature-selection, Feature-fusion, Epilepsy, Seizure
                 precursors, IEEG",
  DOI =          "doi:10.1016/j.engappai.2007.02.002",
  abstract =     "This paper presents an application of genetic
                 programming (GP) to optimally select and fuse
                 conventional features (C-features) for the detection of
                 epileptic waveforms within intracranial
                 electroencephalogram (IEEG) recordings that precede
                 seizures, known as seizure precursors. Evidence
                 suggests that seizure precursors may localise regions
                 important to seizure generation on the IEEG and
                 epilepsy treatment. However, current methods to detect
                 epileptic precursors lack a sound approach to
                 automatically select and combine C-features that best
                 distinguish epileptic events from background, relying
                 on visual review predominantly. This work suggests GP
                 as an optimal alternative to create a single feature
                 after evaluating the performance of a binary detector
                 that uses: (1) genetically programmed features; (2)
                 features selected via GP; (3) forward sequentially
                 selected features; and (4) visually selected features.
                 Results demonstrate that a detector with a genetically
                 programmed feature outperforms the other three
                 approaches, achieving over 78.5percent positive
                 predictive value, 83.5percent sensitivity, and
                 93percent specificity at the 95percent level of

Genetic Programming entries for Otis L Smart Hiram A Firpi George Vachtsevanos