Artificial immune recognition system with fuzzy resource allocation mechanism classifier, principal component analysis and FFT method based new hybrid automated identification system for classification of EEG signals

Expert Systems with Applications

Volumn 34, Issue 3, 2008, Pages 2039-2048

  Polat, Kemal ^a   Güneş, Salih ^a  

^a SELCUK UNIVERSITY   (Turkey)

Author keywords

Artificial immune recognition system; EEG signals; Expert systems; Fuzzy resource allocation mechanism; Principal component analysis; Welch method

Indexed keywords

CLASSIFICATION (OF INFORMATION); ELECTROENCEPHALOGRAPHY; IDENTIFICATION (CONTROL SYSTEMS); NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS; RESOURCE ALLOCATION; SIGNAL ANALYSIS;

ARTIFICIAL IMMUNE RECOGNITION SYSTEM; EEG SIGNALS; FUZZY RESOURCE ALLOCATION MECHANISM; WELCH METHOD;

FUZZY SETS;

EID: 37349026733     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.02.009     Document Type: Article

References (30)

1. Adeli H., Zhou Z., and Dadmehr N. Analysis of EEG records in an epileptic patient using wavelet transform. Journal of Neuroscience Methods 123 1 (2003) 69-87
2. Andrzejak R.G., Lehnertz K., Mormann F., Rieke C., David P., and Elger C.E. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Physical Review E 64 (2001) 061907
3. Boccaletti S., Grebogi C., Lai Y.C., Mancini H., and Mazaet D. The control of chaos: theory and applications. Physics Report 329 (2000) 108-109
4. De Castro L.N., and Timmis J. Artificial immune systems: a new computational intelligence approach (2002), Springer, UK
5. Duke D., and Pritchard W. Measuring chaos in the human brain (1991), World Scientific, Singapore
6. Evans D. Doppler signal analysis. Ultrasound in Medicine and Biology 26 Supplement 1 (2000) S13-S15
7. Gabor A.J., and Seyal M. Automated interictal EEG spike detection using artificial neural networks. Electroencephalography and Clinical Neurophysiology 83 5 (1992) 271-280
8. Glass L., Michel R.G., Mackey M., and Shrier A. Chaos in neurobiology. IEEE Transactions on Systems Man and Cybernetics SMC 5 (1983) 790-798
9. Glover Jr. J.R., Raghaven N., Ktonas P.Y., and Frost Jr. J.D. Context-based automated detection of epileptogenic sharp transients in the EEG: elimination of false positives. IEEE Transactions on Biomedical Engineering 36 5 (1989) 519-527
10. Guler I., and Ubeyli E.D. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. Journal of Neuroscience Methods 148 (2005) 113-121
11. Guler N.F., Ubeyli E.D., and Guler I. Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Systems with Applications 29 (2005) 506-514
12. Hazarika N., Chen J.Z., Tsoi A.C., and Sergejew A. Classification of EEG signals using the wavelet transform. Signal Processing 59 1 (1997) 61-72
13. Jaeseung J., Jeong-Ho C., Kim S.Y., and Seol-Heui H. Nonlinear dynamical analysis of the EEG in patients with Alzheimer's disease and vascular dementia. Clinical Neurophysiology 18 1 (2001) 58-67
14. Jeff Schneider's Home Page, http://www.cs.cmu.edu/~schneide/tut5/node42.html (last accessed: August, 2006).
15. Kannathal N., Choo M.L., Rajendra Acharya U., and Sadasivana P.K. Entropies for detection of epilepsy in EEG. Computer Methods and Programs in Biomedicine 80 (2005) 187-194
16. Kohavi, R., & Provost, F. (1998). Glossary of terms. Editorial for the Special Issue on Applications of Machine Learning and the Knowledge Discovery process, 30 (2/3).
17. Lindsay, I. Smith (2002). A tutorial on principal components analysis. http://kybele.psych.cornell.edu/~edelman/Psych-465Spring-2003/PCA-tutorial.
18. Marwah, G., & Boggess, L. (2002) Artificial immune systems for classification: some issues. In: Proceedings of the first international conference art. immune systems, University of Kent at Canterbury, England (pp. 149-153).
19. Nigam V.P., and Graupe D. A neural-network-based detection of epilepsy. Neurologial Research 26 1 (2004) 55-60
20. Philippe F., and Henri K. Is there chaos in the brain? Concepts of nonlinear dynamics and methods of investigation. Life Sciences 324 (2001) 773-793
21. Polat K., Şahan S., and Güneş S. A New method to medical diagnosis: artificial immune recognition system (AIRS) with fuzzy weighted pre-processing and application to ECG Arrhythmia (2005). Expert Systems with Applications 31 2 (2006) 264-269
22. Polat K., Şahan S., Kodaz H., and Güneş S. Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism. Expert Systems with Applications 32 1 (2007) 172-183
23. Guler I., Hardalac F., and Kaymaz M. Comparison of FFT and adaptive ARMA methods in transcranial Doppler signals recorded from the cerebral vessels. Computers in Biology and Medicine 32 (2002) 445-453
24. Rosso O.A., Figliola A., Creso J., and Serrano E. Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings. Medical & Biological Engineering & Computing 42 4 (2004) 516-523
25. Şahan, S., Kodaz, H., Güneş, S., & Polat, K. (2004). A new classifier based on attribute weighted artificial immune system, Lecture notes in computer science, LNCS 3280, ISSN 0302-9743 (pp. 11-20).
26. Subasi A. EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Applications 32 4 (2007) 1084-1093
27. 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 in Medicine and Biology 14 (1988) 673-688
28. Wang X., and Paliwal K.K. Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition. Pattern Recognition, The Journal of The Pattern Recognition Society 36 (2003) 2429-2439
29. Watkins, A. (2001) AIRS: a resource limited artificial immune classifier. Master thesis, Mississippi State University.
30. Webber W.R.S., Litt B., Lesser R.P., Fisher R.S., and Bankman I. Automatic EEG spike detection: what should the computer imitate?. Electroencephalography and Clinical Neurophysiology 87 6 (1993) 364-373