Prediction of Paroxysmal Atrial Fibrillation by dynamic modeling of the PR interval of ECG

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

@InProceedings{Arvaneh:2009:ICBPE,
  author =       "M. Arvaneh and H. Ahmadi and A. Azemi and 
                 M. Shajiee and Z. S. Dastgheib",
  title =        "Prediction of Paroxysmal Atrial Fibrillation by
                 dynamic modeling of the PR interval of ECG",
  booktitle =    "International Conference on Biomedical and
                 Pharmaceutical Engineering, ICBPE '09",
  year =         "2009",
  month =        "2-4 " # dec,
  pages =        "1--5",
  keywords =     "genetic algorithms, genetic programming, ECG signal,
                 PR interval, Paroxysmal Atrial Fibrillation,
                 electrocardiography, neural networks,
                 electrocardiography, neural nets",
  DOI =          "doi:10.1109/ICBPE.2009.5384063",
  abstract =     "In this work, we propose a new method for prediction
                 of Paroxysmal Atrial Fibrillation (PAF) by only using
                 the PR interval of ECG signal. We first obtain a
                 nonlinear structure and parameters of PR interval by a
                 Genetic Programming (GP) based algorithm. Next, we use
                 the neural networks for prediction of PAF. The inputs
                 of the neural networks are the parameters of nonlinear
                 model of the PR intervals. For the modeling and
                 prediction we have limited ourselves to only 30 seconds
                 of an ECG signal, which is one of the advantages of our
                 proposed approach. For comparison purposes, we have
                 modeled 30 seconds of ECG signals by time based
                 modeling method and have compared prediction results of
                 them.",
  notes =        "Also known as \cite{5384063}",
}

Genetic Programming entries for Mahnaz Arvaneh Hamed Ahmadi Asad Azemi Mahnoosh Shajiee Zeinab Sadat Dastgheib

Citations