Novel approach for fetal heart rate classification introducing grammatical evolution

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@Article{Georgoulas200769,
  author =       "George Georgoulas and Dimitris Gavrilis and 
                 Ioannis G. Tsoulos and Chrysostomos Stylios and Joao Bernardes and 
                 Peter P. Groumpos",
  title =        "Novel approach for fetal heart rate classification
                 introducing grammatical evolution",
  journal =      "Biomedical Signal Processing and Control",
  volume =       "2",
  number =       "2",
  pages =        "69--79",
  year =         "2007",
  ISSN =         "1746-8094",
  DOI =          "DOI:10.1016/j.bspc.2007.05.003",
  URL =          "http://www.sciencedirect.com/science/article/B7XMN-4P9K9C1-1/2/26899c02af37c6edf88c6baa6282a061",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, Fetal heart rate, Multilayer perceptron,
                 Feature construction, Classification",
  abstract =     "Fetal heart rate (FHR) variations reflect the level of
                 oxygenation and blood pressure of the fetus. Electronic
                 Fetal Monitoring (EFM), the continuous monitoring of
                 the FHR, was introduced into clinical practice in the
                 late 1960s and since then it has been considered as an
                 indispensable tool for fetal surveillance. However, EFM
                 evaluation and its merit is still an open field of
                 controversy, mainly because it is not consistently
                 reproducible and effective. In this work, we present a
                 novel method based on grammatical evolution to
                 discriminate acidemic from normal fetuses, using
                 features extracted from the FHR signal during the
                 minutes immediately preceding delivery. The proposed
                 method identifies linear and nonlinear correlations
                 among the originally extracted features and
                 creates/constructs a set of new ones, which, in turn,
                 feed a nonlinear classifier. The classifier, which also
                 uses a hybrid method for training, along with the
                 constructed features was tested using a set of real
                 data achieving an overall performance of 90percent
                 (specificity=sensitivity=90percent).",
}

Genetic Programming entries for George Georgoulas Dimitris Gavrilis Ioannis G Tsoulos Chrysostomos Stylios Joao Bernardes Peter P Groumpos

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