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

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@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, ANN, electrocardiography, neural nets",
• DOI = "doi:10.1109/ICBPE.2009.5384063",
• size = "5 pages",
• 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