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

Expert Systems

Volumn 29, Issue 2, 2012, Pages 183-199

  Yaghouby, Farid ^a   Ayatollahi, Ahmad ^b   Bahramali, Reihaneh ^c   Yaghouby, Maryam ^d  

^a UNIVERSITY OF KENTUCKY   (United States)

^b IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Iran)

^c UNIVERSITY OF APPLIED SCIENCE AND TECHNOLOGY   (Iran)

^d ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Arrhythmia detection; Atrial fibrillation; Forward floating selection; Heart rate variability signal; Linear genetic programming; Multi expression programming

Indexed keywords

ARRHYTHMIA DETECTION; ATRIAL FIBRILLATION; FORWARD FLOATING SELECTION; HEART RATE VARIABILITY SIGNALS; LINEAR GENETIC PROGRAMMING; MULTI-EXPRESSION PROGRAMMING;

EID: 84879723608     PISSN: 02664720     EISSN: 14680394     Source Type: Journal    
DOI: 10.1111/j.1468-0394.2010.00571.x     Document Type: Article

References (54)

1. AHO, A., R. SETHI and J. ULLMAN (1986) Compilers: Principles, Techniques, and Tools, Reading, MA: Addison-Wesley.
2. 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.
3. 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.
4. BANZHAF, W., P. NORDIN, R. KELLER and F. FRANCONE (1998) Genetic Programming - An Introduction: On the Automatic Evolution of Computer Programs and its Application, Heidelberg/San Francisco: Morgan Kaufmann.
5. BRAMEIER, M. and W. BANZHAF (2007) Linear Genetic Programming, New York: Springer Science + Business Media, LLC.
6. BRAMEIER, M., W. KANTSCHIK, P. DITTRICH and W. BANZHAF (1998). SYSGP - A C + + library of different GP variants, Technical Report [CI-98/48], Collaborative Research Center 531, University of Dortmund, Germany.
7. 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.
8. CHRISTOV, I., G. BORTOLAN and I. DASKALOV (2001) Sequential analysis for automatic detection of atrial fibrillation and flutter, Computers in Cardiology, 28, 293-296.
9. 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.
10. 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.
11. FELDT, R. and P. NORDIN (2000). Using factorial experiments to evaluate the effect of genetic programming parameters, Proceedings of EuroGP 2000, LNCS 1802, pp. 271-282.
12. FOX, S.I. (1996) Human Physiology, Boston: W.C. Brown Publishers.
13. FRANCONE, F. (2000) Discipulust Owner's Manual, Version 3.0, Littleton, Colorado: Register Machine Learning Technologies.
14. 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.
15. 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.
16. GHODRATI, A., B. MURRAY and S. MARINELLO (2008) RR Interval Analysis for Detection of Atrial Fibrillation in ECG Monitors, in Engineering in Medicine and Biology Society (IEMBS '08. 30th Annual International Conference of the IEEE), Vancouver, British Columbia, Canada, pp. 601-604.
17. 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.
18. GUJARATI, D.N. (1995) Basic Econometrics, 3rd edn, New York: McGraw-Hill.
19. GULER, I. and D. UBEYLI (2005) An expert system for detection of electrocardiographic changes inpatients with partial epilepsy using wavelet-based neural networks, Expert System, 22, 62-71.
20. HAMILTON, P.S. and W.J. TOMPKINS (1986) Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database, IEEE Bio-Medical Engineering, 33, 1157-1165.
21. 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.
22. KARA, S. and M. OKANDAN (2007) Atrial fibrillation classification with artificial neural networks, Pattern Recognition, 40, 2967-2973.
23. KIM, D., Y. SEO and C.H. YOUN (2008) Detection of atrial fibrillation episodes using multiple heart rate variability features in different time periods, in Engineering in Medicine and Biology Society (IEMBS '08. 30th Annual International Conference of the IEEE), pp. 5482-5485.
24. KOSTKA, P.S. and E.J. TKACZ (2008a) Feature extraction for improving the support vector machine biomedical data classifier performance, in Information Technology and Application in Biomedicine, Shenzhen, China, pp. 362-365.
25. KOSTKA, P.S. and E.J. TKACZ (2008b) Feature Extraction Based on Time-Frequency and Independent Component Analysis for Improvement of Separation Ability in Atrial Fibrillation Detector, in Engineering in Medicine and Biology Society (IEMBS '08. 30th Annual International Conference of the IEEE), Vancouver, British Columbia, Canada, pp. 2960-2963.
26. KOZA, J.R. (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection, Cambridge, MA: MIT Press.
27. KUDO, M. and J. SKLANSKY (2000) Comparison of algorithms that select features for pattern classifiers, Pattern Recognition, 33, 25-41.
28. LOGAN, B. and J. HEALEY (2005) Robust detection of atrial fibrillation for a long term telemonitoring system, In Computers in Cardiology, Lyon, France, pp. 619-622.
29. MARAVALL, A. and V. GOMEZ (2004) EViews Software, Version 5, Irvine, CA: Quantitative Micro Software, LLC.
30. MARK, R.G. and G.B. MOODY (1997) MIT-BIH arrhythmia database. Available at http://ecg.mit.edu/dbinfo.html (accessed 1 April 2008).
31. 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.
32. MOHEBBI, M. and H. GHASEMIAN (2008) Detection of atrial fibrillation episodes using SVM, In Engineering in Medicine and Biology Society (IEMBS '08. 30th Annual International Conference of the IEEE), Vancouver, British Columbia, Canada, pp. 177-180.
33. MOODY, G. and R. MARK (2001) The impact of the MIT-BIH arrhythmia database, IEEE Engineering in Medical Biology, 20, 45-50.
34. MOODY, G. and R.G. MARK (1983) A new method for detecting atrial fibrillation using R-R intervals, in Computers in Cardiology, Germany: Aachen, 227-230.
35. NAKARIYAKUL, S. and D.P. CASASENT (2007) Adaptive branch and bound algorithm for selecting optimal features, Pattern Recognition Letters, 28, 1415-1427.
36. NG, J. and J.J. GOLDBERGER (2007) Understanding and interpreting dominant frequency analysis of AF electrograms, Journal of Cardiovascular Electrophy-siology, 18, 680-685.
37. NORDIN, P.J. (1994) A compiling genetic programming system that directly manipulates the machine code (Chapter 14), in E. Kenneth and J.R. Kinnear (ed.) Proceedings of the International Conference on Advances in Genetic Programming, New York, USA: MIT Press, 311-331.
38. OLTEAN, M. (2004) Multi Expression Programming source code. Available at http://www.mep.cs.ubbcluj.ro/ (accessed 12 February 2010).
39. OLTEAN, M. and D. DUMITRESCU (2002) Multi expression programming, Technical report, UBB-01-2002, Babes,-Bolyai University, Cluj-Napoca, Romania.
40. OLTEAN, M. and C. GROSSAN (2003a) A comparison of several linear genetic programming techniques, Complex Systems, 14, 1-29.
41. OLTEAN, M. and C. GROSSAN (2003b) Evolving evolutionary algorithms using multi expression programming, in W. Banzhaf et al. (eds.) The 7th European conference on artificial life, September 14-17, Dortmund, LNAI, Dortmund, Germany: Springer-Verlag, Vol. 2801, 651-658.
42. PAN, J. and W.J. TOMPKINS (1985) A real-time QRS detection algorithm, IEEE Bio-Medical Engineering, 32, 230-236.
43. 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.
44. PUDIL, P., J. NOVOVICOVA and J. KITTLER (1994) Floating search methods in feature selection, Pattern Recognition Letters, 15, 1119-1125.
45. 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.
46. 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.
47. RYAN, T.P. (1997) Modern Regression Methods, New York: Wiley.
48. SHETA, W., N. ELTONSY, G. TOURASSI and A. ELMAGHRABY (2005) Automated detection of breast cancer from screening mammograms using genetic programming, International Journal of Intelligent Computing and Information Science, Vol. 5, no. 1.
49. TATENO, K. and L. GLASS (2000) A method for detection of atrial fibrillation using RR intervals, Computers in Cardiology, 391-394.
50. TATENO, K. and L. GLASS (2001) Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and DRR intervals, Medical and Biological Engineering and Computing, 39, 664-671.
51. UBEYLI, E. (2009) Analysis of electrocardiographic changes in partial epileptic patients by combining eigenvector methods and support vector machines, Expert Systems, 26, 249-259.
52. WENG, B., J.J. WANG, F. MICHAUD and M. BLANCO-VELASCO (2008) Atrial Fibrillation Detection Using Stationary Wavelet Transform Analysis, in Engineering in Medicine and Biology Society (IEMBS '08. 30th Annual International Conference of the IEEE), Vancouver, British Columbia, Canada, pp. 1128-1131.
53. WHEELDON, N.M. (1995) Atrial fibrillation and anticoagulant therapy, European Heart Journal, 16, 302-331.
54. YOUNG, B., D. BRODNICK and R. SPAULDING (1999) A comparative study of a hidden Markov model detector for atrial fibrillation, In Neural Networks for Signal Processing IX (IEEE Signal Processing Society Workshop) 468-476.