Commenced in January 2007
Paper Count: 30011
Epileptic Seizure Prediction by Exploiting Signal Transitions Phenomena
Abstract: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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1110371Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
 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.
 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.
 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.
 Epilepsy Australia: Epilepsy Information. (Feb, 2015). Available: http://www.epilepsyaustralia.net/Epilepsy_Information/Epilepsy_explai ned/Epilepsy_explained.aspx
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 EEG Data Set: Epilepsy Center of the University Hospital of Freiburg. (2012, June 10). Available: http://epilepsy.uni-freiburg.de/freiburgseizure- prediction-project/eeg-database.
 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.
 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.
 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.
 S. Abe, Support vector machine for pattern classification, Springer, 2010.
 J. A. K. Suykens and J. Vandewalle, “Least Squares Support Vector Machine Classifiers,” Neural Processing Letters, vol. 9, no. 3, pp. 293- 300, 1999.