Time-varying biomedical signals analysis with multiclass support vector machines employing Lyapunov exponents

Digital Signal Processing: A Review Journal

Volumn 18, Issue 4, 2008, Pages 646-656

  Übeyli, Elif Derya ^a  

^a TOBB UNIVERSITY OF ECONOMICS AND TECHNOLOGY   (Turkey)

Author keywords

Lyapunov exponents; Multiclass support vector machine (SVM); Time varying biomedical signals

Indexed keywords

DOPPLER EFFECT; LYAPUNOV FUNCTIONS; PROBLEM SOLVING; SUPPORT VECTOR MACHINES; TIME VARYING SYSTEMS;

LYAPUNOV EXPONENTS; MULTICLASS SUPPORT VECTOR MACHINE; TIME-VARYING BIOMEDICAL SIGNALS;

BIOMEDICAL ENGINEERING;

EID: 44449092970     PISSN: 10512004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dsp.2007.10.001     Document Type: Article

References (30)

1. Kordylewski H., Graupe D., and Liu K. A novel large-memory neural network as an aid in medical diagnosis applications. IEEE Trans. Inform. Technol. Biomed. 5 3 (2001) 202-209
2. Kwak N., and Choi C.-H. Input feature selection for classification problems. IEEE Trans. Neural Networks 13 1 (2002) 143-159
3. Übeyli E.D., and Güler I. Feature extraction from Doppler ultrasound signals for automated diagnostic systems. Comput. Biol. Med. 35 9 (2005) 735-764
4. Evans D.H., McDicken W.N., Skidmore R., and Woodcock J.P. Doppler Ultrasound: Physics, Instrumentation and Clinical Applications (1989), Wiley, Chichester
5. Vaitkus P.J., Cobbold R.S.C., and Johnston K.W. A comparative study and assessment of Doppler ultrasound spectral estimation techniques, Part II: Methods and results. Ultrasound Med. Biol. 14 (1988) 673-688
6. Übeyli E.D., and Güler I. Spectral analysis of internal carotid arterial Doppler signals using FFT, AR, MA, and ARMA methods. Comput. Biol. Med. 34 4 (2004) 293-306
7. Güler I., and Übeyli E.D. A modified mixture of experts network structure for ECG beats classification with diverse features. Eng. Appl. Artif. Intell. 18 7 (2005) 845-856
8. Güler I., and Übeyli E.D. ECG beat classifier designed by combined neural network model. Pattern Recogn. 38 2 (2005) 199-208
9. Owis M.I., Abou-Zied A.H., Youssef A.-B.M., and Kadah Y.M. Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification. IEEE Trans. Biomed. Eng. 49 7 (2002) 733-736
10. Güler I., and Übeyli E.D. Detecting variability of internal carotid arterial Doppler signals by Lyapunov exponents. Med. Eng. Phys. 26 9 (2004) 763-771
11. Übeyli E.D., and Güler I. Detection of electrocardiographic changes in partial epileptic patients using Lyapunov exponents with multilayer perceptron neural networks. Eng. Appl. Artif. Intell. 17 6 (2004) 567-576
12. Übeyli E.D., and Güler I. Determining variability of ophthalmic arterial Doppler signals using Lyapunov exponents. Comput. Biol. Med. 35 5 (2005) 405-420
13. Maglaveras N., Stamkopoulos T., Diamantaras K., Pappas C., and Strintzis M. ECG pattern recognition and classification using non-linear transformations and neural networks: A review. Int. J. Med. Inform. 52 (1998) 191-208
14. Osowski S., Hoai L.T., and Markiewicz T. Support vector machine-based expert system for reliable heartbeat recognition. IEEE Trans. Biomed. Eng. 51 4 (2004) 582-589
15. Goldberger A.L., Amaral L.A.N., Glass L., Hausdorff J.M., Ivanov P.Ch., Mark R.G., Mietus J.E., Moody G.B., Peng C.K., and Stanley H.E. Physiobank, Physiotoolkit, and Physionet: Components of a new research resource for complex physiologic signals. Circulation 101 23 (2003) e215-e220. http://circ.ahajournals.org/cgi/content/full/101/23/e215 (June 13)
16. Haykin S., and Li X.B. Detection of signals in chaos. Proc. IEEE 83 1 (1995) 95-122
17. Casdagli M. Nonlinear prediction of chaotic time series. Physica D 35 3 (1989) 335-356
18. Eckmann J.-P., Kamphorst S.O., Ruelle D., and Ciliberto S. Liapunov exponents from time series. Phys. Rev. A 34 6 (1986) 4971-4979
19. Abarbanel H.D.I., Brown R., and Kennel M.B. Lyapunov exponents in chaotic systems: Their importance and their evaluation using observed data. Int. J. Mod. Phys. B 5 9 (1991) 1347-1375
20. Abarbanel H.D.I., Brown R., Sidorowich J.J., and Tsimring L.S. The analysis of observed chaotic data in physical systems. Rev. Mod. Phys. 65 4 (1993) 1331-1392
21. Benettin G., Galgani L., Giorgilli A., and Strelcyn J.-M. Lyapunov characteristic exponents for smooth dynamical systems and for Hamiltonian systems; A method for computing all of them. Meccanica 15 1 (1980) 9-30
22. Packard N.H., Crutchfield J.P., Farmer J.D., and Shaw R.S. Geometry from a time series. Phys. Rev. Lett. 45 9 (1980) 712-716
23. Wolf A., Swift J.B., Swinney H.L., and Vastano J.A. Determining Lyapunov exponents from a time series. Physica D 16 3 (1985) 285-317
24. Sano M., and Sawada Y. Measurement of the Lyapunov spectrum from a chaotic time series. Phys. Rev. Lett. 55 10 (1985) 1082-1085
25. Vapnik V. The Nature of Statistical Learning Theory (1995), Springer-Verlag, New York
26. Cortes C., and Vapnik V. Support vector networks. Mach. Learn. 20 3 (1995) 273-297
27. Dietterich T.G., and Bakiri G. Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. 2 (1995) 263-286
28. Wright I.A., Gough N.A.J., Rakebrandt F., Wahab M., and Woodcock J.P. Neural network analysis of Doppler ultrasound blood flow signals: A pilot study. Ultrasound Med. Biol. 23 5 (1997) 683-690
29. Wright I.A., and Gough N.A.J. Artificial neural network analysis of common femoral artery Doppler shift signals: Classification of proximal disease. Ultrasound Med. Biol. 24 5 (1999) 735-743
30. Übeyli E.D., and Güler I. Improving medical diagnostic accuracy of ultrasound Doppler signals by combining neural network models. Comput. Biol. Med. 35 6 (2005) 533-554