Robust genetic programming-based detection of atrial fibrillation using RR intervals

In this study, two variants of genetic programming, namely linear genetic programming (LGP) and multi‐expression programming (MEP) are utilized to detect atrial fibrillation (AF) episodes. LGP‐ and MEP‐based models are derived to classify samples of AF and Normal episodes based on the analysis of RR...

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Vydané v:Expert systems Ročník 29; číslo 2; s. 183 - 199
Hlavní autori: Yaghouby, Farid, Ayatollahi, Ahmad, Bahramali, Reihaneh, Yaghouby, Maryam
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Blackwell Publishing Ltd 01.05.2012
Predmet:
arrhythmia detection
atrial fibrillation
Cardiac arrhythmia
Cardiovascular disease
Expert systems
forward floating selection
Heart rate
heart rate variability signal
linear genetic programming
Linear programming
multi-expression programming
Studies
ISSN:0266-4720, 1468-0394
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Shrnutí:In this study, two variants of genetic programming, namely linear genetic programming (LGP) and multi‐expression programming (MEP) are utilized to detect atrial fibrillation (AF) episodes. LGP‐ and MEP‐based models are derived to classify samples of AF and Normal episodes based on the analysis of RR interval signals. A weighted least‐squares (WLS) regression analysis is performed using the same features and data sets to benchmark the models. Another important contribution of this paper is identification of the effective time domain features of heart rate variability (HRV) signals upon an improved forward floating selection (IFFS) analysis. The models are developed using MIT‐BIH arrhythmia database. The diagnostic performances of the LGP and MEP classifiers are evaluated through receiver operating characteristics (ROC) analysis. The results indicate that the LGP and MEP models are able to diagnose the AF arrhythmia with an acceptable high accuracy. The proposed models have significantly better diagnosis performances than the regression and several models found in the literature.
Bibliografia:ark:/67375/WNG-CL5ZDP39-K
istex:C244CA5479B63A9671EF81B660A30D257614F0A4
ArticleID:EXSY571
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0266-4720
1468-0394
DOI:10.1111/j.1468-0394.2010.00571.x