Predicting defibrillation success by 'genetic' programming in patients with out-of-hospital cardiac arrest

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Podbregar:2003:R,
  author =       "M. Podbregar and M. Kovacic and A. Podbregar-Mars and 
                 M. Brezocnik",
  title =        "Predicting defibrillation success by 'genetic'
                 programming in patients with out-of-hospital cardiac
                 arrest",
  journal =      "Resuscitation",
  year =         "2003",
  volume =       "57",
  pages =        "153--159",
  number =       "2",
  month =        may,
  owner =        "wlangdon",
  URL =          "http://www.sciencedirect.com/science/article/B6T19-48D3F91-3/2/1a3ece76d7e7c59fb51615980e9791a6",
  keywords =     "genetic algorithms, genetic programming,
                 Cardiopulmonary resuscitation, Ventricular
                 fibrillation, Defibrillation",
  ISSN =         "0300-9572",
  DOI =          "doi:10.1016/S0300-9572(03)00030-3",
  size =         "7 pages",
  abstract =     "Background: In some patients with ventricular
                 fibrillation (VF) there may be a better chance of
                 successful defibrillation after a period of chest
                 compression and ventilation before the defibrillation
                 attempt. It is therefore important to know whether a
                 defibrillation attempt will be successful. The
                 predictive power of a model developed by 'genetic'
                 programming (GP) to predict defibrillation success was
                 studied. Methods and Results: 203 defibrillations were
                 administered in 47 patients with out-of-hospital
                 cardiac arrest due to a cardiac cause. Maximal
                 amplitude, a total energy of power spectral density,
                 and the Hurst exponent of the VF electrocardiogram
                 (ECG) signal were included in the model developed by
                 GP. Positive and negative likelihood ratios of the
                 model for testing data were 35.5 and 0.00,
                 respectively. Using a model developed by GP on the
                 complete database, 120 of the 124 unsuccessful
                 defibrillations would have been avoided, whereas all of
                 the 79 successful defibrillations would have been
                 administered. Conclusion: The VF ECG contains
                 information predictive of defibrillation success. The
                 model developed by GP, including data from the
                 time-domain, frequency-domain and nonlinear dynamics,
                 could reduce the incidence of unsuccessful
                 defibrillations.",
  notes =        "Journal article given in preference to 12th
                 International Symposium on Intensive Care
                 Medicine

                 PMID: 12745183 [PubMed - indexed for MEDLINE]",
}

Genetic Programming entries for M Podbregar Miha Kovacic A Podbregar-Mars Miran Brezocnik

Citations