© The Institution of Engineering and Technology
We propose a novel approach for detecting precursors to epileptic seizures in intracranial electroencephalograms (iEEGs), which is based on the analysis of system dynamics. In the proposed scheme, the largest Lyapunov exponent (LLE) of wavelet entropy of the segmented EEG signals are considered as the discriminating features. Such features are processed by a support vector machine classifier, whose outcomes (the label and its probability for each LLE) are post-processed and fed into a novel decision function to determine whether the corresponding segment of the EEG signal contains a precursor to an epileptic seizure. The proposed scheme is applied to the Freiburg data set, and the results show that seizure precursors are detected in a time frame that unlike other existing schemes is very much convenient to patients, with the sensitivity of 100% and negligible false positive detection rates.
References
-
-
1)
-
3. Iasemidis, L.D., Javeri, H.P., Sackellares, J.C., Hood, T.W.: ‘Nonlinear dynamics of electrocorticographic data’, J. Clin. Neurophysiol., 1988, 5, pp. 339–348 (doi: 10.1097/00004691-198810000-00042).
-
2)
-
25. Suffczynski, P., Lopes da Silva, F.H., Parra, J., et al: ‘Dynamics of epileptic phenomena determined from statistics of ictal transitions’, IEEE Trans. Biomed. Eng., 2006, 53, (3), pp. 524–532 (doi: 10.1109/TBME.2005.869800).
-
3)
-
15. Quiroga, R.Q., Rosso, O.A., Basar, E., Schurmann, M.: ‘Wavelet entropy in event related potentials: a new method shows ordering of EEG oscillations’, Biol. Cybern., 2001, 84, pp. 291–299 (doi: 10.1007/s004220000212).
-
4)
-
5)
-
4. Chisci, L., Perferi, G., Sciandrone, M., Anile, C., Colicchio, G., Fuggetta, F.: ‘Real-time epileptic seizure prediction using AR models and support vector machines’, IEEE Trans. Biomed. Eng., 2010, 57, (5), pp. 1124–1131 (doi: 10.1109/TBME.2009.2038990).
-
6)
-
10. Ouyang, G., Li, X., Li, Y., Guan, X.: ‘Application of wavelet-based similarity analysis to epileptic seizure prediction’, Comput. Biol. Med., 2007, 37, (4), pp. 430–437 (doi: 10.1016/j.compbiomed.2006.08.010).
-
7)
-
9. Korn, H., Faure, P.: ‘Is there chaos in the brain? II: experimental evidence and related models’, C. R. Biol., 2003, 326, (9), pp. 787–840 (doi: 10.1016/j.crvi.2003.09.011).
-
8)
-
5. Iasimidis, L.D.: ‘Phase space topography and the Lyapunov exponent of electroencephalograms in partial seizures’, Brain Topography, 1990, 2, (3), pp. 187–201 (doi: 10.1007/BF01140588).
-
9)
-
31. Tao, Q., Wu, G.W., Wang, F.Y.: ‘Posterior probability support vector machines for unbalanced data’, IEEE Trans. Neural Netw., 2005, 16, (6), pp. 1561–1573 (doi: 10.1109/TNN.2005.857955).
-
10)
-
2. Nesaei, S., Nesaei, S.: ‘Comparison of phase synchrony information flow in human EEG through wavelet phase synchronization analysis’. Proc. Tenth Int. IEEE Signal Processing (ICSP2010), Beijing, China, 2010.
-
11)
-
21. Lopez. da. Silva, H., Blanes, W., kalitzin, S., Parra, N., Suffcznski, J.P., Velis, D.N.: ‘Dynamical disease of brain systems: different routes to epileptic seizures’, IEEE Trans. Biomed. Eng., 2003, 50, (5), pp. 616–627 (doi: 10.1109/TBME.2003.810689).
-
12)
-
13)
-
27. Panda, R., Khobragade, P.S., Jambhule, P.D., Jengthe, S.N., Pal, P.R., Gandhi, T.K.: ‘Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure detection’. Proc. Conf. Systems in Medicine and Biology, Kharagpour, India, 2010.
-
14)
-
12. Direito, B., Dourado, A., Sales, F.: ‘Combining energy and wavelet transform for epileptic seizure prediction in an advanced computational system’. Proc. First Int. Conf. Biomedical Engineering and Informatics (IEEE BMEI), Sanya, Hainan, China, May 2008, pp. 380–385.
-
15)
-
M. D'Alessandro ,
R. Esteller ,
G. Vachtsevanos ,
A. Hinson ,
J. Echauz ,
B. Litt
.
Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients.
IEEE Trans. Biomed. Eng.
,
5 ,
603 -
615
-
16)
-
18. Zaveri, H.P., Williams, W.J., Iasemidis, L.D., Sackellares, J.C.: ‘Time-frequency representation of electrocorticograms in temporal lobe epilepsy’, IEEE Trans. Biomed. Eng., 1992, 39, pp. 502–509 (doi: 10.1109/10.135544).
-
17)
-
11. Winterhalder, M., Maiwald, T., Voss, H.U., Aschenbrenner-Scheibe, R., Timmer, J., Schulze-Bonhage, A.: ‘The seizure prediction characteristic: a general framework to asses and compare seizure prediction methods’, Epilepsy Behav., 2003, 4, (3), pp. 318–325 (doi: 10.1016/S1525-5050(03)00105-7).
-
18)
-
17. Iasemidis, L.D., Olson, L.D., Sackellares, J.C., Savit, R.: ‘Time dependencies in the occurrences of epileptic seizures: a nonlinear approach’, Epilepsy Res., 1994, 17, pp. 81–94 (doi: 10.1016/0920-1211(94)90081-7).
-
19)
-
8. Chavez, M., Quyen, M.L.V., Navarro, V., Baulac, M., Martinerie, J.: ‘Spatio-temporal dynamics prior to neocortical seizures: amplitude versus phase couplings’, IEEE Trans. Biomed. Eng., 2003, 50, (5), pp. 571–583 (doi: 10.1109/TBME.2003.810696).
-
20)
-
7. Sackellares, J.C., Shiau, D.S., Principe, J.C., et al: ‘Predictability analysis for an automated seizure prediction algorithm’, Clin. Neurophysiol., 2006, 23, (6), pp. 509–520 (doi: 10.1097/00004691-200612000-00003).
-
21)
-
16. Rajdev, P., Ward, M.P., Rickus, J., Worth, R., Irazoqui, P.P.: ‘Real-time seizure prediction from local field potentials using an adaptive Wiener algorithm’, Comput. Biol. Med., 2010, 40, (1), pp. 97–108 (doi: 10.1016/j.compbiomed.2009.11.006).
-
22)
-
29. Banbrook, M., Ushaw, G., McLaughlin, S.: ‘How to extract Lyapunov exponents from short and noisy time series’, IEEE Trans. Signal Process., 1997, 45, (5), pp. 1378–1382 (doi: 10.1109/78.575715).
-
23)
-
4. Maiwald, T., Winterhalder, M., Aschenbrenner-Scheibe, R., Voss, H., Schulze-Bonhage, A., Timmera, J.: ‘Long-term prospective on-line real-time seizure prediction. Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic’, Physica D, 2004, 194, pp. 357–368 (doi: 10.1016/j.physd.2004.02.013).
-
24)
-
6. Moser, H.R., Weber, B., Wieser, H.G., Meier, P.F.: ‘Electroencephalograms in epilepsy: analysis and seizure prediction within the framework of Lyapunov theory’, Phys. D: Nonlinear Phenom., 1999, 130, (3–4), pp. 291–305 (doi: 10.1016/S0167-2789(99)00043-3).
-
25)
-
13. Mikowski, P.W., LeCun, Y., Madhavan, D., Kuzniecky, R.: ‘Comparing SVM and convolution networks for epileptic seizure prediction from intracranial EEG’. Proc. IEEE Workshop Machine Learning Signal Processing (MLSP 2008), Concun, Mexico, October 2008, pp. 244–249.
-
26)
-
14. Lai, Y., Osorio, I., Harrison, M.A.F., Frei, M.G.: ‘Correlation-dimension and autocorrelation fluctuations in epileptic seizure dynamics’, Phys. Rev. E, 2002, 65, (3), pp. 1–5 (doi: 10.1103/PhysRevE.65.031921).
-
27)
-
H. Adeli ,
Z. Zhou ,
N. Dadmehr
.
Analysis of EEG records in an epileptic patient using wavelet transform.
J. Neurosci. Methods
,
69 -
87
-
28)
-
20. Chaovalitwangse, W.A., Prokoyev, O.A., Pardalos, P.M.: ‘Electroencephalogram (EEG) time series classification: application in epilepsy’, Ann. Oper. Res., 2006, 148, pp. 227–250 (doi: 10.1007/s10479-006-0076-x).
-
29)
-
28. Zhao, J., Zhou, W., Liu, K., Cai, K.: ‘Application of SVM and wavelet analysis in EEG classification’, Medline, 2011, 28, (2), pp. 77–79.
-
30)
-
30. Cao, L.: ‘Determining minimum embedding dimension from scalar time series’, in Soofi, A., Cao, L. (Eds.): ‘Modelling and forecasting financial data’ (Springer US, 2002), pp. 43–60.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2013.0297
Related content
content/journals/10.1049/iet-spr.2013.0297
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Errata
An Erratum has been published for this content:
Erratum