Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30011
Epileptic Seizure Prediction by Exploiting Signal Transitions Phenomena

Authors: Mohammad Zavid Parvez, Manoranjan Paul


A seizure prediction method is proposed by extracting global features using phase correlation between adjacent epochs for detecting relative changes and local features using fluctuation/ deviation within an epoch for determining fine changes of different EEG signals. A classifier and a regularization technique are applied for the reduction of false alarms and improvement of the overall prediction accuracy. The experiments show that the proposed method outperforms the state-of-the-art methods and provides high prediction accuracy (i.e., 97.70%) with low false alarm using EEG signals in different brain locations from a benchmark data set.

Keywords: Epilepsy, Seizure, Phase Correlation, Fluctuation, Deviation.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF


[1] S. Santaniello, S. P. Burns, A. J. Golby, J. M. Singer, W. S. Anderson, and S. V. Sarma, "Quickest detection of drug-resistant seizures: An optimal control approach," Epilepsy & Behavior, vol. 22, pp. S49-S60, 2011.
[2] T. Netoff, P. Yun, and K. Parhi, "Seizure prediction using cost-sensitive support vector machine," Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3322-3325, 2009.
[3] F. Mormann, R. G. Andrzejak, C. E. Elger, and K. Lehnertz, "Seizure prediction: the long and winding road," Brain, vol. 130, pp. 314–333, 2007.
[4] Epilepsy Australia: Epilepsy Information. (Feb, 2015). Available: ned/Epilepsy_explained.aspx
[5] M. Z. Parvez and M. Paul, “Epileptic seizure detection by exploiting temporal correlation of electroencephalogram signals,” IET Signal Processing, vol. 9, no. 6, 467-475, 2015.
[6] M. Z. Parvez and M. Paul, "Epileptic seizure detection by analyzing EEG signals using different transformation techniques," Neurocomputing, vol. 145, pp. 190-200, 12/5/ 2014.
[7] V. L. Dorr, M. Caparos, F. Wendling, J. P. Vignal, and D. Wolf, "Extraction of reproducible seizure patterns based on EEG scalp correlations," Biomedical Signal Processing and Control, vol. 2, pp. 154-162, 2007.
[8] J.R. Williamson, D.W. Bliss, D.W. Browne, and J.T. Narayanan, "Seizure prediction using EEG spatiotemporal correlation structure," Epilepsy & Behavior, vol,. 25, no. 2, pp. 230-238, 2012.
[9] L. Chisci, A. Mavino, G. Perferi, M. Sciandrone, C. Anile, G. Colicchio, and F. Fuggetta, "Real-Time Epileptic Seizure Prediction Using AR Models and Support Vector Machines," IEEE Transactions on Biomedical Engineering, vol. 57, no. 5, pp. 1124-1132, 2010.
[10] P. Mirowski, D. Madhavan, Y. LeCun, and R. Kuzniecky, " Classification of patterns of EEG synchronization for seizure prediction," Clinical Neurophysiology, vol. 120, no. 11, pp. 1927-1940, 2009.
[11] Y. Park, L. Luo, K. K. Parhi, and T. Netoff, "Seizure prediction with spectral power of EEG using cost-sensitive support vector machines," Epilepsia, vol. 52, no. 10, pp. 1761-1770, 2011.
[12] S. Li, W. Zhou, Q. Yuan, and Y. Liu, “Seizure Prediction Using Spike Rate of Intracranial EEG,” IEEE Transactions on Neural Systems and Rehabilitation, vo. 21, no. 6, pp. 880-886, 2013.
[13] J. Rasekhi, M. R. K. Mollaei, M. Bandarabadi, C. A. Teixeira, and A. Dourado, "Preprocessing effects of 22 linear univariate features on the performance of seizure prediction methods," Journal of Neuroscience Methods, vol. 217, pp. 9–16, 2013.
[14] B. Schelter, M. Winterhalder, T. Maiwald, A. Brandt, A. Schad, J. Timmer, et al., "Do False Predictions of Seizures Depend on the State of Vigilance? A Report from Two Seizure-Prediction Methods and Proposed Remedies," Epilepsia, vol. 47, no. 12, pp. 2058-2070, 2006.
[15] EEG Data Set: Epilepsy Center of the University Hospital of Freiburg. (2012, June 10). Available: prediction-project/eeg-database.
[16] M. Parvez and M. Paul, “Epileptic Seizure Prediction by Exploiting Spatiotemporal Relationship of EEG Signals using Phase Correlation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2015.
[17] M. Paul, W. Lin, C. T. Lau, and B. Lee, “Direct Intermode Selection for H.264 Video Coding Using Phase Correlation,” IEEE Transactions on Image Processing, vol. 20, no. 2, pp. 461-473, 2011.
[18] Y. Xie, Y. Ye, J. Zhang, L. Liu, and L. Liu, “A physics-based defects model and inspection algorithm for automatic visual inspection,” Optics and Lasers in Engineering, vol. 52, pp. 218-223, 2014.
[19] S. Abe, Support vector machine for pattern classification, Springer, 2010.
[20] J. A. K. Suykens and J. Vandewalle, “Least Squares Support Vector Machine Classifiers,” Neural Processing Letters, vol. 9, no. 3, pp. 293- 300, 1999.