Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm

Signal Processing

Volumn 88, Issue 7, 2008, Pages 1858-1867

  Ocak, Hasan ^a  

^a KOCAELI UNIVERSITY   (Turkey)

Author keywords

Approximate entropy; Genetic algorithm; Learning vector quantization (LVQ); Seizure detection; Wavelet

Indexed keywords

APPROXIMATION THEORY; DOMAIN DECOMPOSITION METHODS; ELECTROENCEPHALOGRAPHY; GENETIC ALGORITHMS; VECTOR QUANTIZATION; WAVELET ANALYSIS;

APPROXIMATE ENTROPY; DECOMPOSITION TREES; LEARNING VECTOR QUANTIZATION (LVQ); SEIZURE DETECTION;

IMAGE CLASSIFICATION;

EID: 41249099701     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sigpro.2008.01.026     Document Type: Article

References (28)

1. Iasemidis L.D., Shiau D.S., Chaovalitwongse W., et al. Adaptive epileptic seizure prediction system. IEEE Trans. Biomed. Eng. 50 5 (2003) 616-627
2. Liu A., Hahn J.S., Heldt G.P., and Coen R.W. Detection of neonatal seizures through computerized EEG analysis. Electroen. Clin. Neurophysiol. 82 (1992) 30-37
3. Gotman J., Flanagah D., Zhang J., and Rosenblatt B. Automatic seizure detection in the newborn: methods and initial evaluation. Electroen. Clin. Neurophysiol. 103 (1997) 356-362
4. Gardner A.B., Worrell G.A., Marsh E., et al. Human and automated detection of high-frequency oscillations in clinical intracranial EEG recordings. Clin. Neurophysiol. 118 5 (2007) 1134-1143
5. Rosso O.A., Blanco S., and Rabinowicz A. Wavelet analysis of generalized tonic-clonic epileptic seizures. Signal Processing 83 6 (2003) 1275-1289
6. Adeli H., Zhou Z., and Dadmehr N. Analysis of EEG records in an epileptic patient using wavelet transform. J. Neurosci. Meth. 123 (2003) 69-87
7. Khan Y.U., and Gotman J. Wavelet based automatic seizure detection in intracerebral electroencephalogram. Clin. Neurophysiol. 114 (2003) 898-908
8. P. Zarjam, M. Mesbah, B. Boashash, Detection of newborns EEG seizure using optimal features based on discrete wavelet transform, in: Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing, Hong Kong, 6-10 April 2003, pp. 265-268.
9. Andrzejak R.G., Lehnertz K., Rieke C., et al. Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys. Rev. E 64 6 (2001) 061907
10. Pradhan N., Sadasivan P.K., and Arunodaya G.R. Detection of seizure activity in EEG by an artificial neural network: a preliminary study. Comput. Biomed. Res. 29 (1996) 303-313
11. Petrosian A., Prokhorov D., Homan R., Dashei R., and Wunsch D. Recurrent neural network based prediction of epileptic seizures in intra- and extracranial EEG. Neurocomputing 30 (2000) 201-218
12. Kiymik M.K., Subasi A., and Ozcalik H.R. Neural networks with periodogram and autoregressive spectral analysis methods in detection of epileptic seizure. J. Med. Syst. 28 6 (2004) 511-522
13. Guler I., and Ubeyli E.D. Application of adaptive neuro-fuzzy inference system for detection of electrocardiographic changes in patients with partial epilepsy using feature extraction. Expert Syst. Appl. 27 3 (2004) 323-330
14. Subasi A. Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction. Comput. Biol. Med. 37 2 (2007) 227-244
15. Subasi A. Automatic detection of epileptic seizure using dynamic fuzzy neural networks. Expert Syst. Appl. 31 (2006) 320-328
16. Acharya R., Faust O., Kannathal N., et al. Non-linear analysis of EEG signals at various sleep stages. Comput. Meth. Programs Biomed. 80 (2005) 37-45
17. Bruhn J., Ropcke H., Rehberg B., et al. Electroencephalogram approximate entropy correctly classifies the occurrence of burst suppression pattern as increasing anesthetic drug effect. Anesthesiology 93 4 (2000) 981-985
18. Bruhn J., Ropcke H., and Hoeft A. Approximate entropy as an electroencephalographic measure of anesthetic drug effect during desflurane anesthesia. Anesthesiology 92 3 (2000) 715-726
19. Kannathal N., Choo M.L., Acharya U.R., and Sadasivan P.K. Entropies for detection of epilepsy in EEG. Comput. Meth. Programs Biomed. 80 (2005) 187-194
20. Peterson D.A., Knight J.N., Kirby M.J., et al. Feature selection and blind source separation in an EEG-based brain-computer interface. EURASIP J. Appl. Signal Process. 19 (2005) 3128-3140
21. Mitra S.K., and Sarbadhikari S.N. Iterative function system and genetic algorithm-based EEG compression. Med. Eng. Phys. 19 7 (1997) 605-617
22. Firpi H., Goodman E.D., and Echauz J. Epileptic seizure detection using genetically programmed artificial features. IEEE Trans. Biomed. Eng. 54 2 (2007) 212-224
23. Holland J.H. Adaptation in Natural and Artificial Systems (1975), MIT Press, Cambridge, MA
24. Yao X. Evolving artificial neural networks. Proc. IEEE 87 9 (1999) 694-713
25. Mitchell M. An Introduction to Genetic Algorithms (Complex Adaptive Systems) (1998), MIT Press, Cambridge, MA
26. Pincus S.M., Gladstone I.M., and Ehrangranz R.A. A regularity statistics for medical data analysis. J. Clin. Monitor. 7 (1991) 335-345
27. Pincus S.M. Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA 88 (1991) 2297-2301
28. T. Kohonen, Improved versions of learning vector quantization, in: IEEE International Joint Conference on Neural Networks, San Diego, CA, 17-21 June 1990, pp. 545-550.