Automatic identification of epileptic EEG signals using nonlinear parameters

Journal of Mechanics in Medicine and Biology

Volumn 9, Issue 4, 2009, Pages 539-553

  Acharya, U Rajendra ^a   Chua, Chua Kuang ^a   Lim, Teik Cheng ^b   Dorithy, ^a   Suri, Jasjit S ^c,d,e  

^a DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING   (Singapore)

^b SINGAPORE UNIVERSITY OF SOCIAL SCIENCES   (Singapore)

^c IDAHO STATE UNIVERSITY   (United States)

^d Eigen Inc   (United States)

^e Biomedical Technologies   (United States)

Author keywords

Correlation dimension; EEG; Epilepsy; Fractal; GMM; Lyapunov exponent; Preictal; SVM

Indexed keywords

EID: 76449108621     PISSN: 02195194     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219519409003152     Document Type: Article

References (47)

1. Commission on Epidemiology and Prognosis, International League Against Epilepsy, Guidelines for epidemiologic studies on epilepsy. Commission on Epidemiology and Prognosis, International League Against Epilepsy, Epilepsia 34(4):592-596, 1993.
2. Blume W, Luders H, Mizrahi E, Tassinari C, van Emde Boas W, Engel J, Glossary of descriptive terminology for ictal semiology: Report of the ILAE task force on classification and terminology, Epilepsia 42(9):1212-1218, 2001. (Pubitemid 32951809)
3. Fisher R, van Emde Boas W, Blume W, Elger C, Genton P, Lee P, Engel J, Epileptic seizures and epilepsy: Definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE), Epilepsia 46(4):470-472, 2005. (Pubitemid 40470667)
4. Epilepsy: Aetiogy [sic], epidemiology and prognosis, World Health Organization. February 2001 (last accessed on 18th June 2009).
5. The National Society for Epilepsy (2009), What is Epilepsy? Available from http:// www.epilepsynse.org.uk/AboutEpilepsy/Whatisepilepsy (accessed on 15 February 2009).
6. Acharya UR, Kannathal N, Krishnan SM, Comprehensive analysis of cardiac health using heart rate signals, Physiol Meas J UK 25:1130-1151, 2004.
7. Acharya UR, Kumar A, Bhat PS, Lim CM, Iyengar SS, Kannathal N, Krishnan SM, Classification cardiac abnormalities using heart rate signals, IFMBE J Med Biol Eng Comput J UK 42(3):288-293, 2004. (Pubitemid 38786693)
8. Acharya UR, Suri JS, Spaan JAE, Krishnan SM, Advances in Cardiac Signal Processing, Springer Verlang GmbH, Berlin Heidelberg, March 2007.
9. Subha DP, Joseph KP, Acharya UR, Lim CM, EEG signal processing: A survey, J Med Syst USA, October, 2008, in press.
10. Swiderski B, Osowski S, Rysz A, Lyapunov exponent of EEG signal for epileptic seizure characterization, Proc 2005 European Conference on Circuit Theory and Design 2(28):153-156, 2005.
11. Subasi A, EEG signal classification using wavelet feature extraction and a mixture of expert model, Expert Syst Appl Int J 32(4):1084-1093, 2007.
12. Chua KC, Chandran V, Acharya UR, Lim CM, Analysis of epileptic EEG signals using higher order spectra, J Med Eng Technol UK 33(1):42-50, 2009.
13. Bai D, Qiu, Li T, The sample entropy and its application in EEG based epilepsy detection, J Biomed Eng 200-205, 2007.
14. Kannathal N, Lim CM, Acharya UR, Sadasivan PK, Entropies for detection of epilepsy in EEG, Comput Meth Programs Biomed 80(3):187-194, 2005.
15. Paivinen N, Lammi S, Pitkanen A, Nissinen J, Penttonen M, Gronfors T, Epileptic seizure detection: A nonlinear viewpoint, Comput Meth Programs Biomed 79(2):151-159, 2005.
16. Aschenbrenner-Scheibe R, Maiwald T, Winterhalder M, Voss HU, Timmer J, Schulze-Bonhage A, How well can epileptic seizures be predicted? An evaluation of a nonlinear method, Brain 126(12):2616-2626, 2003.
17. Andrzejak RG, Widman G, Lehnertz K, Rieke C, David P, Elger CE, The epileptic process as nonlinear deterministic dynamics in a stochastic environment: An evaluation on mesial temporal lobe epilepsy, Epilepsy Res 44(2):129-140, 2001.
18. Lehnertz K, Epilepsy and nonlinear dynamics, J Biol Phys 33(3-4):253-266, 2008.
19. Takens F, Detecting strange attractors in turbulence, in Rand D, Young LS (eds.), Dynamical Systems and Turbulence, Springer, Berlin, 1981.
20. Kennel MB, Brown R, Abarbanel HDI, Determining embedding dimension for phase- space reconstruction using a geometrical construction, Phys Rev A 45:3403, 1992.
21. Fraser AM, Swinney HL, Independent coordinates for strange attractors from mutual information, Phys Rev A 33:1134-1140, 1986.
22. Pincus SM, Keefe DL, Quantification of hormone pulsatility via an approximate entropy algorithm, Am J Physiol 262:E741-E754, 1992.
23. Kannathal N, Lim CM, Acharya UR, Sadasivan PK, Entropies in the detection of Epilepsy in EEG, Comput Meth Programs Biomed Sweden 80(3):187-194, 2005.
24. Rosenstien M, Colins JJ, De Luca CJ, A practical method for calculating largest Lyapunov exponents from small data sets, Physica D 65:117-134, 1993.
25. Dangel S, Meier PF, Moser HR, Plibersek S, Shen Y, Time series analysis of sleep EEG, Comput Assist Phys 93-95, 1999.
26. Mandelbrot BB, Geometry of nature, Freeman, San Francisco, 1983.
27. Acharya UR, Faust O, Kannathal N, Chua TJ, Laxminarayan S, Dynamical analysis of EEG signals at various sleep stages, Comput Meth Programs Biomed 80(1):37-45, 2005.
28. Higuchi T, Approach to an irregular time series on the basis of the fractal theory, Physica D 31:277-283, 1988.
29. Reynolds DA, Quatieri TF, Dunn RB, Speaker verification using adapted Gaussian mixture models, Digital Signal Process 10:19-41, 2000.
30. Seo C, Lee KY, Lee J, GMM based on local PCA for speaker identification, Electron Lett 37:1486-1488, 2001.
31. Bilmes JA, A Gentle Tutorial of the EM Algorithm and Its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models, International Computer Science Institute, 1998.
32. DeLeo JM, Receiver Operating Characteristic Laboratory (ROCLAB): Software for developing decision strategies that account for uncertainty management in artificial neural network decision-making, Proc Second Int Symp Uncertainty Modeling and Analysis, pp. 141-144, 1993.
33. Downey TJ, Meyer DJ, Price RK, Spitznagel EL, Using the receiver operating characteristic to assess the performance of neural classifiers, Neural Networks 5:3642-3646, 1999.
34. Petitmengin C, Baulac M, Navarro V, Seizure anticipation: Are neurophenomenological approaches able to detect pre-ictal symptoms? Epilepsy Behav 9(2):298-306, 2006.
35. Sleigh JW, Olofsen E, Dahan A, Goede de J, Steyn-Ross A, Entropies of the EEG: The effects of general anaesthesia, Proc 5th Int Conf Memory, Awareness and Consciousness, USA, 2001.
36. Chua KC, Chandran V, Acharya UR, Lim CM, Automatic identification of epileptic EEG signals using higher order spectra, Int J Eng Med 223(4):485-495, 2009.
37. Kannathal N, Acharya UR, Lim CM, Weiming Q, Hidayat M, Sadasivan PK, Characterization of EEG: A comparative study, Comput Meth Programs Biomed Sweden 80(1):17-23, 2005.
38. Nigam VP, Graupe D, A neural-network-based detection of epilepsy, Neurol Res 26(1):55-60, 2004.
39. Srinivasan V, Eswaran C, Sriraam N, Artificial neural network based epileptic detection using time-domain and frequency-domain features, J Med Syst 29(6):647-660, 2005.
40. Aslan K, Bozdemir H, S?ahin C, O?gulata SN, Ero R, A radial basis function neural network model for classification of epilepsy using EEG signals 32(5):403-408, 2008.
41. Srinivasan V, Eswaran C, Sriraam N, Approximate entropy-based epileptic EEG detection using artificial neural networks, IEEE Trans Inform Technol Biomed 11(3):288-295, 2007. (Pubitemid 46758846)
42. Tzallas AT, Tsipouras MG, Fotiadis DI, Automatic seizure detection based on timefrequency analysis and artificial neural networks, Comput Intell Neurosci 2007:13, 2007.
43. Ocak H, Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy, Expert Syst Appl Int J 36(2):2027-2036, 2009.
44. Theiler J, Eubank S, Longtin A, Galdrikian B, Farmer JD, Testing for nonlinearity in time series: The method of surrogate data, Physica D 58:77-94, 1992.
45. Sprott JC, Rowlands G, Chaos Data Analyzer: The Professional Version, American Institute of Physics, USA, 1995.
46. EEG time series database, http://www.meb.unibonn.de/epileptologie/ science/ physik/eegdata.
47. http://www.medcalc.be/(last accessed on 11 August 2009).