Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines

Expert Systems with Applications

Volumn 39, Issue 1, 2012, Pages 202-209

  Nicolaou, Nicoletta ^a   Georgiou, Julius ^a  

^a UNIVERSITY OF CYPRUS   (Cyprus)

Author keywords

Electroencephalogram (EEG); Epilepsy; Permutation Entropy (PE); Seizure; Support Vector Machine (SVM)

Indexed keywords

ELECTROENCEPHALOGRAM (EEG); EPILEPSY; PERMUTATION ENTROPY (PE); SEIZURE; SUPPORT VECTOR;

ELECTROENCEPHALOGRAPHY; ENTROPY;

SUPPORT VECTOR MACHINES;

EID: 81855221797     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.07.008     Document Type: Article

References (35)

1. R.G. Andrzejak, K. Lehnertz, C. Rieke, F. Mormann, P. David, and C.E. Elger 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
2. C. Bandt, and B. Pompe Permutation entropy: A natural complexity measure for time series Physical Review Letters 88 2002 174102
3. A.A. Bruzzo, B. Gesierich, M. Santi, C.A. Tassinari, N. Birbaumer, and G. Rubboli Permutation entropy to detect vigilance changes and preictal states from scalp EEG in epileptic patients. A preliminary study Neurological Science 29 2008 3 9
4. C.J.C. Burges A tutorial on support vector machines for pattern recognition U. Fayyad, Data Min Knowl Disc 1998 Kluwer Academic Publishers Boston 121 167
5. Y. Cao, W. Tung, J.B. Gao, V.A. Protopopescu, and L.M. Hively Detecting dynamical changes in time series using the permutation entropy Physical Review E 70 2004 046217
6. J.S. Duncan, J.W. Sander, S.M. Sisodiya, and M.C. Walker Adult epilepsy Lancet 367 2006 1087
7. A.J. Gabor, R.R. Leach, and F.U. Dowla Automated seizure detection using a self-organizing neural network Electroencephalography and Clinical Neurophysiology 99 1996 257 266
8. N.F. Güler, E.D. Übeyli, and. Güler Recurrent neural networks employing Lyapunov exponents for EEG signals classification Expert Systems with Applications 29 2005 506 514
9. N. Kannathal, M.L. Choo, U.R. Acharya, and P.K. Sadasivan Entropies for detection of epilepsy in EEG Computer Methods and Programs in Biomedicine 80 2005 187 194
10. V.P. Kiymik, A. Subasi, and H.R. Ozcalik Neural networks with periodogram and autoregressive spectral analysis methods in detection of epileptic seizures Journal of Medical Systems 28 2004 511 522 Klinik für Epileptologie, Universität Bonn. In
11. S.P. Kumar, N. Sriraam, P.G. Benakop, and B.C. Jinaga Entropies based detection of epileptic seizures with artificial neural network classifiers Expert Systems with Applications 37 2010 3284 3291
12. K. Lehnertz Epilepsy and nonlinear dynamics Journal of Biological Physics 34 2008 253 266
13. K. Lehnertz, and C.E. Elger Can epileptic seizures be predicted? Evidence from nonlinear time series analyses of brain electrical activity Physical Review Letters 80 1988 5019 5023
14. X. Li, S. Cui, and L.J. Voss Using Permutation Entropy to measure the electroencephalographic effects of sevoflurane Anesthesiology 109 2008 448 456
15. X. Li, G. Ouyang, and D.A. Richards Predictability analysis of absence seizures with permutation entropy Epilepsy Research 77 2007 70 74
16. P.E. McSharry, T. He, L.A. Smith, and L. Tarassenko Linear and non-linear methods for automatic seizure detection in scalp electro-encephalogram recordings Medical and Biological Engineering and Computing 40 2002 447 461
17. F. Mormann, R.G. Andrzejak, T. Kreuz, C. Rieke, P. David, and C.E. Elger Automated detection of a preseizure state based on a decrease in synchronization in intracranial electroencephalogram recordings from epilepsy patients Physical Review E 67 2003 021912
18. A.M. Murro, D.W. King, J.R. Smith, B.B. Gallagher, H.F. Flanigin, and K. Meador Computerized seizure detection of complex partial seizures Electroencephalography and Clinical Neurophysiology 1991 79
19. V.P. Nigam, and D. Graupe A neural-network-based detection of epilepsy Neurological Research 26 2004 55 60 (56)
20. E. Olofsen, J.W. Sleigh, and A. Dahan Permutation entropy of the electroencephalogram: a measure of anaesthetic drug effect British Journal of Anaesthesia 101 2008 810 821
21. N. Päivinen, S. Lammi, A. Pitkänen, J. Nissinen, M. Penttonen, and T. Grönfors Epileptic seizure detection: A nonlinear viewpoint Computer Methods and Programs in Biomedicine 79 2005 151 159
22. K. Polat, and S. Güne 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 34 2008 2039 2048
23. N. Pradhan, P.K. Sadasivan, and G.R. Arunodaya Detection of seizure activity in EEG by an artificial neural network: A preliminary study Computers and Biomedical Research 29 1996 303 313
24. H. Qu, and J. Gotman A patient-specific algorithm for the detection of seizure onset in long-term EEG monitoring: Possible use as a warning device IEEE Transactions on Biomedical Engineering 44 1997 115 122
25. R. Quian Quiroga, J. Arnhold, K. Lehnertz, and P. Grassberger Kulback-Leibler and renormalized entropies: Applications to electroencephalograms of epilepsy patients Physical Review E 62 2000 8380 8386
26. V. Srinivasan, C. Eswaran, and N. Sriraam Artificial neural network based epileptic detection using time-domain and frequency-domain features Journal of Medical Systems 29 2005 647 660
27. V. Srinivasan, C. Eswaran, and N. Sriraam Approximate entropy-based epileptic EEG detection using artificial neural networks IEEE Transactions on Information Technology in Biomedicine 11 2007 288 295
28. A. Subasi Epileptic seizure detection using dynamic wavelet network Expert Systems with Applications 29 2005 343 355
29. A.T. Tzallas, M.G. Tsipouras, and D.I. Fotiadis Automatic seizure detection based on time-frequency analysis and artificial neural networks Computational Intelligence and Neuroscience 2007 80510 (80513 p.)
30. E.D. Übeyli Combined neural network model employing wavelet coefficients for EEG signals classification Digit Signal Process 19 2009 297 308
31. W. van Drongelen, S. Nayak, D.M. Frim, M.H. Kohrman, V.L. Towle, and H.C. Lee Seizure anticipation in pediatric epilepsy: use of kolmogorov entropy Pediatric Neurology 29 2003 207 213
32. M.J.A.M. van Putten Nearest neighbor phase synchronization as a measure to detect seizure activity from scalp EEG recordings Journal of Clinical Neurophysiology 20 2003 320 325
33. W.R.S. Webber, R.P. Lesser, R.T. Richardson, and K. Wilson An approach to seizure detection using an artificial neural network (ANN) Electroencephalography and Clinical Neurophysiology 98 1996 250 272
34. K. Wilson, M.L. Scheuer, C. Plummer, B. Young, and S. Pacia Seizure detection: correlation of human experts Clinical Neurophysiology 114 2003 2156 2164
35. S.B. Wilson, M.L. Scheuer, R.G. Emerson, and A.J. Gabor Seizure detection: Evaluation of the Reveal algorithm Clinical Neurophysiology 115 2004 2280 2291