Robust genetic programming-based detection of atrial fibrillation using RR intervals

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@Article{journals/es/YaghoubyABY12,
  author =       "Farid Yaghouby and Ahmad Ayatollahi and 
                 Reihaneh Bahramali and Maryam Yaghouby",
  title =        "Robust genetic programming-based detection of atrial
                 fibrillation using {RR} intervals",
  journal =      "Expert Systems",
  year =         "2012",
  volume =       "29",
  number =       "2",
  pages =        "183--199",
  keywords =     "genetic algorithms, genetic programming, atrial
                 fibrillation, heart rate variability signal, linear
                 genetic programming, multi-expression programming,
                 forward floating selection, arrhythmia detection",
  ISSN =         "1468-0394",
  DOI =          "doi:10.1111/j.1468-0394.2010.00571.x",
  size =         "17 pages",
  abstract =     "In this study, two variants of genetic programming,
                 namely linear genetic programming (LGP) and
                 multi-expression programming (MEP) are used to detect
                 atrial fibrillation (AF) episodes. LGP- and MEP-based
                 models are derived to classify samples of AF and Normal
                 episodes based on the analysis of RR interval signals.
                 A weighted least-squares (WLS) regression analysis is
                 performed using the same features and data sets to
                 benchmark the models. Another important contribution of
                 this paper is identification of the effective time
                 domain features of heart rate variability (HRV) signals
                 upon an improved forward floating selection (IFFS)
                 analysis. The models are developed using MIT-BIH
                 arrhythmia database. The diagnostic performances of the
                 LGP and MEP classifiers are evaluated through receiver
                 operating characteristics (ROC) analysis. The results
                 indicate that the LGP and MEP models are able to
                 diagnose the AF arrhythmia with an acceptable high
                 accuracy. The proposed models have significantly better
                 diagnosis performances than the regression and several
                 models found in the literature.",
  bibdate =      "2012-06-05",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/es/es29.html#YaghoubyABY12",
}

Genetic Programming entries for Farid Yaghouby Ahmad Ayatollahi Reihaneh Bahramali Maryam Yaghouby

Citations