Evolving a Bayesian classifier for ECG-based age classification in medical applications

Applied Soft Computing Journal

Volumn 8, Issue 1, 2008, Pages 599-608

  Wiggins, M ^a   Saad, A ^b   Litt, B ^c,d   Vachtsevanos, G ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^b Armstrong State University   (United States)

^c UNIVERSITY OF PENNSYLVANIA   (United States)

^d UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

ECG based age classification; Evolved Bayesian classifier; Hybrid soft computing techniques

Indexed keywords

BAYESIAN NETWORKS; DATA ACQUISITION; ELECTROCARDIOGRAPHY; FEATURE EXTRACTION; GENETIC ALGORITHMS; PATIENT MONITORING; SOFT COMPUTING;

AGE CLASSIFICATION; BAYESIAN CLASSIFIERS; HYBRID SOFT COMPUTING TECHNIQUES; NETWORK DISCOVERY;

MEDICAL APPLICATIONS;

EID: 34548479030     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2007.03.009     Document Type: Article

References (33)

1. Addison P.S., et al. Evaluating arrhythmias in ECG signals using wavelet transforms. IEEE Eng. Med. Biol. Mag. 19 5 (2000) 104-109
2. Tsipouras M.G., Fotiadis D.I., and Sideris D. An arrhythmia classification system based on the RR-interval signal. Artif. Intel. Med. 33 3 (2005) 237-250
3. Papaloukas C., et al. An ischemia detection method based on artificial neural networks. Artif. Intel. Med. 24 2 (2002) 167-178
4. Zimmerman M.W., and Povinelli R.J. On Improving the Classification of Myocardial Ischemia using Holter ECG Data. Computers in Cardiology, 19-22 September (2004), IEEE, Chicago, IL, USA
5. Rabelo Jr. A., et al. An expert system for diagnosis of acute myocardial infarction with ECG analysis. Artif. Intel. Med. 10 1 (1997) 75-92
6. Stallman F.W., and Pipberger H.V. Automatic recognition of electrocardiographic waves by digital computer. Circul. Res. 9 (1961) 138-143
7. Thomas Jr. L.J., et al. Automated cardiac dysrhythmia analysis. Proc. IEEE 67 9 (1979) 1322-1337
8. Steinberg J.S. Postoperative atrial fibrillation: a billion-dollar problem. J. Am. College Cardiol. 43 6 (2004) 1001-1003
9. Kohler B.-U., Hennig C., and Orglmeister R. The principles of software QRS detection. IEEE Eng. Med. Biol. Mag. 21 1 (2002) 42-57
10. Nugent C.D., et al. An intelligent framework for the classification of the 12-lead ECG. Artif. Intel. Med. 16 3 (1999) 205-222
11. Bortolan G., and Pedrycz W. An interactive framework for an analysis of ECG signals. Artif. Intel. Med. 24 2 (2002) 109-132
12. Chandy J., et al. Increases in P-wave dispersion predict postoperative atrial fibrillation after coronary artery bypass graft surgery. Anesthesia Analgesia 98 2 (2004) 303-310
13. Zhang X.-S., et al. Detecting ventricular tachycardia and fibrillation by complexity measure. IEEE Trans. Biomed. Eng. 46 5 (1999) 548-555
14. Wei J.-J., et al. ECG data compression using truncated singular value decomposition. IEEE Trans. Inform. Technol. Biomed. 5 4 (2001) 290-299
15. Hilton M.L. Wavelet and wavelet packet compression of electrocardiograms. IEEE Trans. Biomed. Eng. 44 5 (1997) 394-402
16. Ramakrishnan A.G., and Saha S. ECG coding by wavelet-based linear prediction. IEEE Trans. Biomed. Eng. 44 12 (1997) 1253-1261
17. Li C., Zheng C., and Tai C. Detection of ECG characteristic points using wavelet transforms. IEEE Trans. Biomed. Eng. 42 1 (1995) 21-28
18. Goldberger A.L., et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101 23 (2000) 215e-220e
19. D'Alessandro M., et al. Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients. IEEE Trans. Biomed. Eng. 50 5 (2003) 603-615
20. Tu C., Zeng Y., and Yang X. Nonlinear processing and analysis of ECG data. Technology & Health Care (2004), IOS Press 1-9 pp
21. Clayton R.H., Murray A., and Campbell R.W.F. Frequency analysis of self-terminating ventricular fibrillation. Computers in Cardiology (1994)
22. Heckerman D. A Tutorial on Learning With Bayesian Networks (1995), Microsoft Research
23. Neapolitan R. Learning Bayesian Networks (2004), Pearson Prentice-Hall, London
24. Krishnapuram B., et al. A Bayesian approach to joint feature selection and classifier design. IEEE Trans. Pattern Anal. Mach. Intel. 26 9 (2004) 1105-1111
25. Bressan M., and Vitria J. On the selection and classification of independent features. IEEE Trans. Pattern Anal. Mach. Intel. 25 10 (2003) 1312-1317
26. Larranaga P., et al. Structure learning of Bayesian networks by genetic algorithms: a performance analysis of control parameters. IEEE Trans. Pattern Anal. Mach. Intel. 18 9 (1996) 912-926
27. Domingos P., and Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss. Mach. Learn. 29 2-3 (1997) 103-130
28. True H.L., and Lindquist S.L. A yeast prion provides a mechanism for genetic variation and phenotypic diversity. Nature 407 6803 (2000) 477-483
29. Houck C., Joines J., and Kay M. A genetic algorithm for function optimization: A Matlab Implementation. NCSU-IE TR (1995) 95-109
30. Friedman N., Geiger D., and Goldszmidt M. Bayesian network classifiers. Mach. Learn. 29 2-3 (1997) 131-163
31. Saxena A., and Saad A. Genetic algorithms for artificial neural net-based condition monitoring system design for rotating mechanical systems. J. Appl. Soft Comput., Elsevier 7 1 (2007) 441-454
32. Oduguwa V., Tiwari A., and Roy R. Evolutionary computing in manufacturing industry: an overview of recent applications. J. Appl. Soft Comput. 5 3 (2005) 281-299
33. Lopes H.S. Genetic programming for epileptic pattern recognition in electroencephalographic signals. J. Appl. Soft Comput. 7 1 (2006) 343-352