Prediction of Paroxysmal Atrial Fibrillation by dynamic modeling of the PR interval of ECG
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @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