Epileptic State Detection: Pre-ictal, Inter-ictal, Ictal

Abstract

Epileptic seizure detection and prediction from electroencephalography (EEG) is a vital area of research. In this study, Second-Order Difference Plot (SODP) is used to extract features based on consecutive difference of time domain values from three states of EEG (pre-ictal, ictal and inter-ictal), and Multi-Layer Neural Network classifier is used to classify these three classes. The proposed technique is tested on a publicly available EEG database and classified with Naive Bayes and k-nearest neighbor classifiers. As a result, it is shown that overall accuracy of 98.70% can be achieved by using the proposed system with Neural Network classifier.

Keywords

• Epileptic State Detection; Second-Order Difference Plot; Neural Network

References

1. V.. Vukkadala, Srinath, Vijayapriya.S (2009). Automated Detection Of Epileptic EEG Using Approximate Entropy In Elman Networks, Int. J. Recent Trends Eng. 1 307–312.
2. M. Ghanbari, M. Askaripour, N. Behboodiyan (2012). Detection of Epilepsy from EEG Signal during Seizure Using Heuristic Algorithm of Fixed Point Iterations, Res. J. Appl. Sci. Eng. Technol. 4 3584–3587.
3. F. Mormann, R.G. Andrzejak, C.E. Elger, K. Lehnertz (2007). Seizure prediction: the long and winding road., Brain. 130 314–33. doi:10.1093/brain/awl241.
4. S. Sanei, J.A. Chambers 2007. EEG Signal Processing, Willey, England.
5. C.-P. Shen, C.-M. Chan, F.-S. Lin, M.-J. Chiu, J.-W. Lin, J.-H. Kao, et al. (2011) . Epileptic Seizure Detection for Multichannel EEG Signals with Support Vector Machines, 2011 IEEE 11th Int. Conf. Bioinforma. Bioeng. 39–43. doi:10.1109/BIBE.2011.13.
6. Z. Zainuddin, L.K. Huong, O. Pauline (2012). Reliable Epileptic Seizure Detection Using an Improved Wavelet Neural Network, Australas Med. J. 33–44.
7. S. Vollala, K. Gulla (2012). Automatic Detection of Epilepsy EEG Using Neural Networks, Int. J. Internet Comput. 506009 68–72.
8. M.S. Mercy (2012). Performance Analysis of Epileptic Seizure Detection Using DWT & ICA with Neural Networks, Int. J. Comput. Eng. Res. 2 1109–1113.

9. M. Bayram (2013). EEG sınıflandırma amaçlı bir kompozit sistem, Dicle Univ. J. Eng. Cilt 4, Sayı 1,5-2. 30 5–12.
10. A.L. Goldberger and coworkers (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals, Circ. 101(23)e215-e220. http://circ.ahajournals.org/cgi/content/full/101/23/e215.
11. D.L.H. and P.C.D. Maurice E.Cohen (1996). Applying Continuous chaotic Modeling to Cardiac Signal Analysis, Eng. Med. Biol. 97–102.
12. C. Kamath (2012). A new approach to detect congestive heart failure using Teager energy nonlinear scatter plot of R-R interval series., Med. Eng. Phys. 34 841–8. doi:10.1016/j.medengphy.2011.09.026.
13. C. Bishop (1996)., Neural networks for pattern recognition., 1st ed. NY, USA: Oxford Univ. Press.
14. S. Haykin (1996). Neural networks: a comprehensive foundation., 2nd ed. New Jersey: Prentice Hall.
15. S.D. Duda RO, Hart PE( 2000). Pattern classification. 2nd ed. Wiley-Interscience.
16. R. Kumar, A. Indrayan (2011). Receiver operating characteristic (ROC) curve for medical researchers., Indian Pediatr. 48 277–87. http://www.ncbi.nlm.nih.gov/pubmed/21532099.
17. M. Fauzi, T. Moh, S. Yau, A.B.N. (2007). Classifier, Comparison of Different Classification Techniques Using WEKA for Breast Cancer, IFMBE Proc. Vol. 15. 15 520–523.
18. G. Ngai, E.C.-H.; Gelenbe, E.; Humber, Inf. ormation-aware traffic reduction for wireless sensor networks, in: Local Comput. Networks, Zurich, n.d. pp. 451 – 458.
19. Freiburg EEG dataset, (n.d.). https://epilepsy.uni-freiburg.de/freiburg-seizureprediction-project/eeg-database/ (accessed December 12, 2013).
20. P. Mirowski, D. Madhavan, Y. Lecun, R. Kuzniecky (2009)., Classification of Patterns of EEG Synchronization for Seizure Prediction, Work. ach. Learn. Signal Process.
21. R.B. Pachori, S. Patidar (2014). Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions., Comput. Methods Programs Biomed. 113 494–502. doi:10.1016/j.cmpb.2013.11.014.
22. E.C. Andrzejak RG, Lehnertz K, Rieke C, Mormann F, David P (2001). 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.