Towards automatic detection of atrial fibrillation: A hybrid computational approach

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

@Article{Yaghouby2010919,
  author =       "Farid Yaghouby and Ahmad Ayatollahi and 
                 Reihaneh Bahramali and Maryam Yaghouby and Amir Hossein Alavi",
  title =        "Towards automatic detection of atrial fibrillation: A
                 hybrid computational approach",
  journal =      "Computers in Biology and Medicine",
  volume =       "40",
  number =       "11-12",
  pages =        "919--930",
  year =         "2010",
  ISSN =         "0010-4825",
  DOI =          "doi:10.1016/j.compbiomed.2010.10.004",
  URL =          "http://www.sciencedirect.com/science/article/B6T5N-51CRWGV-1/2/c0eaea60cd989fbea5e856e07847ee5f",
  keywords =     "genetic algorithms, genetic programming, Atrial
                 fibrillation, Heart rate variability signal, Orthogonal
                 least squares, Simulated annealing, Forward floating
                 selection, Arrhythmia detection",
  abstract =     "In this study, new methods coupling genetic
                 programming with orthogonal least squares (GP/OLS) and
                 simulated annealing (GP/SA) were applied to the
                 detection of atrial fibrillation (AF) episodes.
                 Empirical equations were obtained to classify the
                 samples of AF and Normal episodes based on the analysis
                 of RR interval signals. Another important contribution
                 of this paper was to identify the effective time domain
                 features of heart rate variability (HRV) signals via an
                 improved forward floating selection analysis. The
                 models were developed using the MIT-BIH arrhythmia
                 database. A radial basis function (RBF) neural
                 networks-based model was further developed using the
                 same features and data sets to benchmark the GP/OLS and
                 GP/SA models. The diagnostic performance of the GP/OLS
                 and GP/SA classifiers was evaluated using receiver
                 operating characteristics analysis. The results
                 indicate a high level of efficacy of the GP/OLS model
                 with sensitivity, specificity, positive predictivity,
                 and accuracy rates of 99.11%, 98.91%, 98.23%, and
                 99.02%, respectively. These rates are equal to 99.11%,
                 97.83%, 98.23%, and 98.534% for the GP/SA model. The
                 proposed GP/OLS and GP/SA models have a significantly
                 better performance than the RBF and several models
                 found in the literature.",
}

Genetic Programming entries for Farid Yaghouby Ahmad Ayatollahi Reihaneh Bahramali Maryam Yaghouby A H Alavi

Citations