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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| Veröffentlicht in: | Expert systems Jg. 29; H. 2; S. 183 - 199 |
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01.05.2012
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| Abstract | 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. |
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| AbstractList | 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. 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. [PUBLICATION ABSTRACT] |
| Author | Yaghouby, Farid Ayatollahi, Ahmad Yaghouby, Maryam Bahramali, Reihaneh |
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| Cites_doi | 10.1007/BF02345439 10.1016/j.patrec.2007.02.015 10.1109/TBME.1985.325532 10.1007/s00366-009-0140-7 10.1111/j.1468-0394.2005.00295.x 10.1016/0167-8655(94)90127-9 10.1109/ITAB.2008.4570638 10.1016/j.patcog.2007.03.008 10.1109/NNSP.1999.788166 10.1109/IEMBS.2008.4649359 10.1111/j.1468-0394.2008.00486.x 10.1109/51.932724 10.1007/BFb0055923 10.1093/oxfordjournals.eurheartj.a060911 10.1109/IEMBS.2008.4650455 10.1109/IEMBS.2008.4649224 10.1109/IEMBS.2008.4649119 10.1109/IEMBS.2008.4649824 10.1016/j.medengphy.2008.04.013 10.1016/S0002-8703(98)70030-4 10.1109/CIC.2005.1588177 10.1111/j.1540-8167.2007.00832.x 10.1016/j.artmed.2008.04.007 10.1016/S0031-3203(99)00041-2 10.1109/TBME.1986.325695 10.1617/s11527-009-9559-y 10.1109/TBME.2007.890741 10.1161/01.CIR.83.1.162 10.1007/978-3-540-46239-2_20 10.1111/j.1468-0394.2009.00478.x 10.1016/0735-1097(93)90381-A 10.1111/j.1468-0394.2007.00432.x |
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Polat, K. and S.Gunes (2007) An improved approach to medical data sets classification: artificial immune recognition system with fuzzy resource allocation mechanism, Expert Systems, 24, 252-270. Rodriguez, C.A.R. and M.A.H.Silveira (2001) Multi-thread implementation of a fuzzy neural network for automatic ECG arrhythmia detection, Computing in Cardiology, 28, 297-300. Conrads, M., O.Dolezal, F.D.Francone and P.Nordin (2004) Discipulus - Fast Genetic Programming Based on AIM Learning Technology, Littleton, CO: Register Machine Learning Technologies Inc. Gujarati, D.N. (1995) Basic Econometrics, 3rd edn, New York: McGraw-Hill. Dilaveris, P.E., E.Gialafos and S.Sideris (1998) Simple electrocardiographic markers for the prediction of paroxysmal idiopathic atrial fibrillation, American Heart Journal, 135, 73-78. Ubeyli, E. (2009) Analysis of electrocardiographic changes in partial epileptic patients by combining eigenvector methods and support vector machines, Expert Systems, 26, 249-259. Asl, B.M., K.Setarehdan and M.Mohebbi (2008) Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal', Artificial Intelligence in Medicine, 44, 51-64. Fukunami, M., T.Yamada, M.Ohmori, K.Kumagai, K.Umemoto, A.Sakai, N.Kondoh, T.Minamino and N.Hoki (1991) Detection of patients at risk for paroxysmal atrial fibrillation during sinus rhythm by P wave-triggered signal-averaged electrocardiogram, Circulation, 83, 162-169. Alavi, A.H., A.H.Gandomi, M.G.Sahab and M.Gandomi (2010) Multi expression programming: a new approach to formulation of soil classification, Engineering with Computers, 26, 111-118. Oltean, M. and C.Grosşan (2003a) A comparison of several linear genetic programming techniques, Complex Systems, 14, 1-29. Chiarugi, F., M.Varanini, F.Cantini, F.Conforti and G.Vrouchos (2007) Noninvasive ECG as a tool for predicting termination of paroxysmal atrial fibrillation, IEEE Transaction on Biomedical Engineering, 54, 1399-1406. 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Mehta, S., N.Lingayat and S.Sanghvi (2009) Detection and delineation of P and T waves in 12-lead electrocardiograms, Expert Systems, 26, 125-143. Moody, G. and R.Mark (2001) The impact of the MIT-BIH arrhythmia database, IEEE Engineering in Medical Biology, 20, 45-50. Christov, I., G.Bortolan and I.Daskalov (2001) Sequential analysis for automatic detection of atrial fibrillation and flutter, Computers in Cardiology, 28, 293-296. Gandomi, A.H., A.H.Alavi and M.G.Sahab (2010) New formulation for compressive strength of CFRP confined concrete cylinders using linear genetic programming, Materials and Structures, 43, 963-83. Kudo, M. and J.Sklansky (2000) Comparison of algorithms that select features for pattern classifiers, Pattern Recognition, 33, 25-41. Guidera, S. and J.Steinberg (1993) The signal-averaged P wave duration: a rapid and noninvasive marker of risk of atrial fibrillation, Journal of the American College Cardiology, 21, 1645-1651. Wheeldon, N.M. 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(e_1_2_7_13_1) 1996 e_1_2_7_8_1 e_1_2_7_18_1 Nordin P.J. (e_1_2_7_38_1) 1994 Rodriguez C.A.R. (e_1_2_7_47_1) 2001; 28 e_1_2_7_16_1 Gujarati D.N. (e_1_2_7_19_1) 1995 e_1_2_7_40_1 e_1_2_7_12_1 e_1_2_7_44_1 e_1_2_7_29_1 Francone F. (e_1_2_7_14_1) 2000 Tateno K. (e_1_2_7_50_1) 2000 e_1_2_7_51_1 e_1_2_7_53_1 e_1_2_7_24_1 e_1_2_7_32_1 e_1_2_7_55_1 e_1_2_7_22_1 e_1_2_7_34_1 e_1_2_7_20_1 e_1_2_7_36_1 Conrads M. (e_1_2_7_10_1) 2004 Oltean M. (e_1_2_7_42_1) 2003 |
| References_xml | – reference: Ricci, R.P., M.Russo and M.Santini (2006) Management of atrial fibrillation - what are the possibilities of early detection with home monitoring?, Clinical Research in Cardiology, 95, 1861-1892. – reference: Christov, I., G.Bortolan and I.Daskalov (2001) Sequential analysis for automatic detection of atrial fibrillation and flutter, Computers in Cardiology, 28, 293-296. – reference: Fukunami, M., T.Yamada, M.Ohmori, K.Kumagai, K.Umemoto, A.Sakai, N.Kondoh, T.Minamino and N.Hoki (1991) Detection of patients at risk for paroxysmal atrial fibrillation during sinus rhythm by P wave-triggered signal-averaged electrocardiogram, Circulation, 83, 162-169. – reference: Hong-Wei, L., S.Ying, L.Min, L.Pi-Ding and Z.Zheng (2009) A probability density function method for detecting atrial fibrillation using R-R intervals, Medical Engineering and Physics, 31, 116-123. – reference: Guler, I. and D.Ubeyli (2005) An expert system for detection of electrocardiographic changes in patients with partial epilepsy using wavelet-based neural networks, Expert System, 22, 62-71. – reference: Kudo, M. and J.Sklansky (2000) Comparison of algorithms that select features for pattern classifiers, Pattern Recognition, 33, 25-41. – reference: Polat, K. and S.Gunes (2007) An improved approach to medical data sets classification: artificial immune recognition system with fuzzy resource allocation mechanism, Expert Systems, 24, 252-270. – reference: Koza, J.R. (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection, Cambridge, MA: MIT Press. – reference: Dilaveris, P.E., E.Gialafos and S.Sideris (1998) Simple electrocardiographic markers for the prediction of paroxysmal idiopathic atrial fibrillation, American Heart Journal, 135, 73-78. – reference: Guidera, S. and J.Steinberg (1993) The signal-averaged P wave duration: a rapid and noninvasive marker of risk of atrial fibrillation, Journal of the American College Cardiology, 21, 1645-1651. – reference: Francone, F. (2000) Discipulus™ Owner's Manual, Version 3.0, Littleton, Colorado: Register Machine Learning Technologies. – reference: Gandomi, A.H., A.H.Alavi and M.G.Sahab (2010) New formulation for compressive strength of CFRP confined concrete cylinders using linear genetic programming, Materials and Structures, 43, 963-83. – reference: Asl, B.M., K.Setarehdan and M.Mohebbi (2008) Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal', Artificial Intelligence in Medicine, 44, 51-64. – reference: Pudil, P., J.Novovicova and J.Kittler (1994) Floating search methods in feature selection, Pattern Recognition Letters, 15, 1119-1125. – reference: Rodriguez, C.A.R. and M.A.H.Silveira (2001) Multi-thread implementation of a fuzzy neural network for automatic ECG arrhythmia detection, Computing in Cardiology, 28, 297-300. – reference: Maravall, A. and V.Gomez (2004) EViews Software, Version 5, Irvine, CA: Quantitative Micro Software, LLC. – reference: Chiarugi, F., M.Varanini, F.Cantini, F.Conforti and G.Vrouchos (2007) Noninvasive ECG as a tool for predicting termination of paroxysmal atrial fibrillation, IEEE Transaction on Biomedical Engineering, 54, 1399-1406. – reference: Ng, J. and J.J.Goldberger (2007) Understanding and interpreting dominant frequency analysis of AF electrograms, Journal of Cardiovascular Electrophysiology, 18, 680-685. – reference: Tateno, K. and L.Glass (2001) Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and ΔRR intervals, Medical and Biological Engineering and Computing, 39, 664-671. – reference: Brameier, M. and W.Banzhaf (2007) Linear Genetic Programming, New York: Springer Science+Business Media, LLC. – reference: Moody, G. and R.Mark (2001) The impact of the MIT-BIH arrhythmia database, IEEE Engineering in Medical Biology, 20, 45-50. – reference: Ryan, T.P. (1997) Modern Regression Methods, New York: Wiley. – reference: Kara, S. and M.Okandan (2007) Atrial fibrillation classification with artificial neural networks, Pattern Recognition, 40, 2967-2973. – reference: Pan, J. and W.J.Tompkins (1985) A real-time QRS detection algorithm, IEEE Bio-Medical Engineering, 32, 230-236. – reference: Alavi, A.H., A.H.Gandomi, M.G.Sahab and M.Gandomi (2010) Multi expression programming: a new approach to formulation of soil classification, Engineering with Computers, 26, 111-118. – reference: Mehta, S., N.Lingayat and S.Sanghvi (2009) Detection and delineation of P and T waves in 12-lead electrocardiograms, Expert Systems, 26, 125-143. – reference: Ubeyli, E. (2009) Analysis of electrocardiographic changes in partial epileptic patients by combining eigenvector methods and support vector machines, Expert Systems, 26, 249-259. – reference: Wheeldon, N.M. 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| SubjectTerms | 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 |
| Title | Robust genetic programming-based detection of atrial fibrillation using RR intervals |
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