Spectral exponent characteristics of intracranial EEGs for epileptic seizure classification

IRBM

Volumn 36, Issue 1, 2015, Pages 33-39

  Janjarasjitt, S ^a  

^a UBON RATCHATHANI UNIVERSITY   (Thailand)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CLASSIFICATION; DATA ANALYSIS; ELECTROENCEPHALOGRAM; ENTROPY; FRACTAL ANALYSIS; HUMAN; RECEIVER OPERATING CHARACTERISTIC; SEIZURE;

EID: 84922337318     PISSN: 19590318     EISSN: 18760988     Source Type: Journal    
DOI: 10.1016/j.irbm.2014.07.005     Document Type: Article

References (32)

1. National Institute of Neurological Disorders and Stroke (NINDS). Seizure and epilepsy: ahope through research. Available: ahttp://www.ninds.nih.gov/disorders/epilepsy/detailepilepsy.htm, 2004
2. Kovacs Z, Dobolyi A. Epilepsy and its therapy: Present and future. Curr Med Chem 2014; 6: 651
3. World Health Organization (WHO). Epilepsy, 2010. Available: ahttp://www.who.int/mediacentre/factsheets/fs999/en/
4. Goldberger AL. Complex systems. Proc Am Thorac Soc 2006; 3: 467
5. Elger CE, Widman G, Andrzejak R, Dumpelman M, Arnhold J, Grass-berger P, et al. Value of nonlinear time series analysis of the EEG in neocortical epilepsies. In: Williamson PD, Siegel AM, Roberts DW, Thadani VM, Gazzaniga MS, editors. Advances in neurology. Philadel-phia: aLippincott Williams & Wilkins; 2000
6. Havlin S, BuldyrevS V, Goldberger AL, Mantegna RN, Ossadnik SM, Peng CK, et al. Fractals in biology and medicine. Chaos Solitons Frac-Tals 1995; 6: 171
7. Stam CJ, de Bruin EA. Scale-free dynamics of global functional connec-Tivity in the human brain. Hum Brain Mapp 2004; 22: 97
8. Linkenkaer-Hansen K, Nikouline VV, Palva JM, IlmoniemiRJ. Long-range temporal correlations and scaling behavior in human brain oscil-lations. J Neurosci 2001; 21: 1370
9. Wornell GW. Wavelet-based representations for the 1/ffamily of fractal processes. Proc IEEE 1993; 81: 1428
10. Wornell GW. Signal processing with fractals: a wavelet-based approach. New Jersey: Prentice Hall; 1995
11. Janjarasjitt S, Loparo KA. Examination of scale-invariant characteristics of multi-channel ECoG data for epileptic seizure localization. J Med Biol Eng. In press. http://dx.doi.org/10.5405/jmbe.1692
12. Janjarasjitt S, Loparo KA. Wavelet-based fractal analysis of multi-channel epileptic ECoG. In: Proceedings of IEEE region 10 annual international conference. Fukuoka, Japan, November 21-24, 2010. 2010. p.373
13. Janjarasjitt S, Loparo KA. Wavelet-based fractal analysis of the epileptic EEG signal. In: Proceedings of the 2009 international symposium on in-Telligent signal processing and communication systems. Kanazawa, Japan, December 7-9, 2009. 2009. p.127
14. JanjarasjittS, LoparoKA. Comparison of complexity measures using two complex system analysis methods applied to the epileptic ECoG. J Korean Phys Soc 2013; 63: 1659
15. Janjarasjitt S, Loparo KA. Scale-invariant behavior of epileptic ECOG. JMed Biol Eng. In press. http://dx.doi.org/10.5405/jmbe.1433
16. Janjarasjitt S, Loparo KA. Examination of scale-invariant characteristics of epileptic electroencephalograms using wavelet-based analysis. Comput Electr Eng 2014; 40: 1766
17. Paivinen N, Lammi S, Pitkanen A, Nissinen J, Penttonen M, Gronfors T. Epileptic seizure detection: a nonlinear viewpoint. Comput Methods Pro-grams Biomed 2005; 79: 151
18. Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE. In-dications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: adependence on recording region and brain state. Phys Rev E 2001; 64(061907): 1
19. Güler I, Ubeyli ED. Adaptive neuro-fuzzy inference system for classi-fication of EEG signals using wavelet coefficients. J Neurosci Methods 2005; 148: 113
20. Srinivasan V, Eswaran C, Sriraam N. Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans Inf Technol Biomed 2007; 11: 288
21. Polat K, Günes S. Artificial immune recognition system with fuzzy re-source allocation mechanism classifier, principal component analysis and FFT method based new hybrid automated identification system for classi-fication of EEG signals. Expert Syst Appl 2008; 34: 2039
22. Srinivasan V, Eswaran C, Sriraam N. Artificial neural network based epileptic detection using time-domain and frequency-domain features. JMed Syst 2005; 29: 647
23. Polat K, Günes S. Classification of epileptiform EEG using a hybrid sys-Tem based on decision tree classifier and fast Fourier transform. Appl Math C 2007; 187: 1017
24. Subasi A. EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl 2007; 32: 1084
25. Kannathal N, Choo ML, Acharya UR, Sadasivan PK. Entropies for detec-Tion of epilepsy in EEG. Comput Methods Programs Biomed 2005; 80: 187
26. Güler I, Übeyli ED. Expert systems for time-varying biomedical signals using eigenvector methods. Expert Syst Appl 2007; 32: 1045
27. Übeyli ED, Güler I. Features extracted by eigenvector methods for detect-ing variability of EEG signals. Pattern Recognit Lett 2007; 28: 592
28. Güler NF, Übeyli ED, Güler I. Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst Appl 2005; 29: 506
29. Watters PA. Fractal structure in the electroencephalogram. Complex Int 1998; 5
30. Wornell GW. Synthesis, analysis, and processing of fractal signals. Ph.D. thesis. Massachusetts Institute of Technology; 1991
31. Goldberger AL, Amaral LAN, Hausdorff JM, Ivanov PCH, Peng C-K, Stanley HE. Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci USA 2002; 99: 2466
32. Janjarasitt S. Computational validation of fractal characterization by using the wavelet-based fractal analysis. J Korean Phys Soc 2014; 64: 780