Towards automatic detection of atrial fibrillation: A hybrid computational approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Yaghouby2010919,
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author = "Farid Yaghouby and Ahmad Ayatollahi and
Reihaneh Bahramali and Maryam Yaghouby and Amir Hossein Alavi",
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title = "Towards automatic detection of atrial fibrillation: A
hybrid computational approach",
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journal = "Computers in Biology and Medicine",
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volume = "40",
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number = "11-12",
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pages = "919--930",
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year = "2010",
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ISSN = "0010-4825",
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DOI = "doi:10.1016/j.compbiomed.2010.10.004",
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URL = "http://www.sciencedirect.com/science/article/B6T5N-51CRWGV-1/2/c0eaea60cd989fbea5e856e07847ee5f",
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keywords = "genetic algorithms, genetic programming, Atrial
fibrillation, Heart rate variability signal, Orthogonal
least squares, Simulated annealing, Forward floating
selection, Arrhythmia detection",
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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