Online Epileptic Seizure Prediction Using Phase Synchronization and Two Time Characteristics: SOP and SPH
International Clinical Neuroscience Journal,
Vol. 7 No. 1 (2020),
23 December 2019
,
Page 16-25
Abstract
Background: The successful prediction of epileptic seizures will significantly improve the living conditions of patients with refractory epilepsy. A proper warning impending seizure system should be resulted not only in high accuracy and low false-positive alarms but also in suitable prediction time.
Methods: In this research, the mean phase coherence index used as a reliable indicator for identifying the preictal period of the 14-patient Freiburg EEG dataset. In order to predict the seizures on-line, an adaptive Neuro-fuzzy model named ENFM (evolving neuro-fuzzy model) was used to classify the extracted features. The ENFM trained by a new class labeling method based on the temporal properties of a prediction characterized by two time intervals, seizure prediction horizon (SPH), and seizure occurrence period (SOP), which subsequently applied in the evaluation method. It is evident that an increase in the duration of the SPH can be more useful for the subject in preventing the irreparable consequences of the seizure, and provides adequate time to deal with the seizure. Also, a reduction in duration of the SOP can reduce the patient’s stress in the SOP interval. In this study, the optimal SOP and SPH obtained for each patient using Mamdani fuzzy inference system considering sensitivity, false-positive rate (FPR), and the two mentioned points, which generally ignored in most studies.
Results: The results showed that last seizure, as well as 14-hour interictal period of each patient, were predicted on-line without false negative alarms: the average yielding of sensitivity by 100%, the average FPR by 0.13 per hour and the average prediction time by 30 minutes.
Conclusion: Based on the obtained results, such a data-labeling method for ENFM showed promising seizure prediction for online machine learning using epileptic seizure data. Apart from that, the proposed fuzzy system can consider as an evaluation method for comparing the results of studies.
- Online seizure prediction
- Mamdani fuzzy inference system
- Neuro-fuzzy model
- Phase synchronization
How to Cite
References
Litt B, Lehnertz K. Seizure prediction and the preseizure period. Current opinion in neurology. 2002;15:173-7. doi: 10.1097/00019052-200204000-00008.
Mormann F, Andrzejak RG, Elger CE, Lehnertz K. Seizure prediction: the long and winding road. Brain. 2006;130(2):314-33. doi: 10.1093/brain/awl241.
Bandarabadi M, Rasekhi J, Teixeira CA, Karami MR, Dourado A. On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior. 2015;46:158-66. doi: https://doi.org/10.1016/j.yebeh.2015.03.010.
Chiang C, Chang N, Chen T, Chen H, Chen L, editors. Seizure prediction based on classification of EEG synchronization patterns with on-line retraining and post-processing scheme. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2011 30 Aug.-3 Sept. 2011.
Schelter B, Winterhalder M, Maiwald T, Brandt A, Schad A, Schulze-Bonhage A, et al. Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction. Chaos (Woodbury, NY). 2006;16:013108. doi: 10.1063/1.2137623.
Schelter B, Winterhalder M, Maiwald T, Brandt A, Schad A, Timmer J, et al. Do False Predictions of Seizures Depend on the State of Vigilance? A Report from Two Seizure-Prediction Methods and Proposed Remedies. Epilepsia. 2007;47:2058-70. doi: 10.1111/j.1528-1167.2006.00848.x.
Wang S, Chaovalitwongse WA, Wong S. Online Seizure Prediction Using an Adaptive Learning Approach. IEEE Transactions on Knowledge and Data Engineering. 2013;25(12):2854-66. doi: 10.1109/TKDE.2013.151.
McSharry PE, Smith LA, Tarassenko L. Prediction of epileptic seizures: are nonlinear methods relevant? Nature Medicine. 2003;9(3):241-2. doi: 10.1038/nm0303-241.
Zheng Y, Wang G, Li K, Bao G, Wang J. Epileptic seizure prediction using phase synchronization based on bivariate empirical mode decomposition. Clinical Neurophysiology. 2014;125(6):1104-11. doi: https://doi.org/10.1016/j.clinph.2013.09.047.
Carney PR, Myers S, Geyer JD. Seizure prediction: Methods. Epilepsy & Behavior. 2011;22:S94-S101. doi: 10.1016/j.yebeh.2011.09.001.
Mormann F, Lehnertz K, David P, E. Elger C. Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Physica D: Nonlinear Phenomena. 2000;144(3):358-69. doi: https://doi.org/10.1016/S0167-2789(00)00087-7.
Arnhold J, Grassberger P, Lehnertz K, Elger CE. A robust method for detecting interdependences: application to intracranially recorded EEG. Physica D: Nonlinear Phenomena. 1999;134(4):419-30. doi: https://doi.org/10.1016/S0167-2789(99)00140-2.
Mormann F, Kreuz T, Andrzejak RG, David P, Lehnertz K, Elger CE. Epileptic seizures are preceded by a decrease in synchronization. Epilepsy Research. 2003;53(3):173-85. doi: https://doi.org/10.1016/S0920-1211(03)00002-0.
Aarabi A, He B. Seizure prediction in patients with focal hippocampal epilepsy. Clinical Neurophysiology. 2017;128(7):1299-307. doi: https://doi.org/10.1016/j.clinph.2017.04.026.
Winterhalder M, Maiwald T, Voss HU, Aschenbrenner-Scheibe R, Timmer J, Schulze-Bonhage A. The seizure prediction characteristic: a general framework to assess and compare seizure prediction methods. Epilepsy & Behavior. 2003;4(3):318-25. doi: 10.1016/S1525-5050(03)00105-7.
Wang L, Wang C, Fu F, Yu X, Guo H, Xu C, et al. Temporal lobe seizure prediction based on a complex Gaussian wavelet. Clinical Neurophysiology. 2011;122(4):656-63. doi: https://doi.org/10.1016/j.clinph.2010.09.018.
Front Matter. In: Mallat S, editor. A Wavelet Tour of Signal Processing. 3 ed. Boston: Academic Press; 2009. p. iii.
Sharabaty H, Martin J, Jammes B, Esteve D, editors. Alpha and Theta Wave Localisation using Hilbert-Huang Transform: Empirical Study of the Accuracy. 2006 2nd International Conference on Information & Communication Technologies; 2006 24-28 April 2006.
Losonczi L, Bako L, Brassai ST, Márton LF, editors. HILBERT-HUANG TRANSFORM USED FOR EEG SIGNAL ANALYSIS. The 6th edition of the Interdisciplinarity in Engineering International Conference 2012; “Petru Maior” University of Tîrgu Mureş, Romania.
Huang N, Shen Z, Long SR, Wu MLC, Shih HH, Zheng Q, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences. 1998;454:903-95. doi: 10.1098/rspa.1998.0193.
Baghdadi G, Motie Nasrabadi A. EEG phase synchronization during hypnosis induction. Journal of Medical Engineering & Technology. 2012;36(4):222-229. doi: 10.3109/03091902.2012.668262.
Rosenblum M, Pikovsky A, Kurths J, Schäfer C, Tass PA. Chapter 9 Phase synchronization: From theory to data analysis. In: Moss F, Gielen S, editors. Handbook of Biological Physics. 4: North-Holland; 2001. p. 279-321.
Daniel WW. Applied nonparametric statistics. 2 ed. the University of Michigan: PWS-Kent Publ; 1990.
Soleimani-B H, Lucas C, N. Araabi B, Schwabe L. Adaptive prediction of epileptic seizures from intracranial recordings. Biomedical Signal Processing and Control. 2012;7(5):456-64. doi: https://doi.org/10.1016/j.bspc.2011.11.007.
Gath I, Geva AB. Unsupervised optimal fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1989;11(7):773-80. doi: 10.1109/34.192473.
Soleimani-B H, Lucas C, Araabi BN, editors. Recursive Gath-Geva clustering as a basis for evolving neuro-fuzzy modeling. International Conference on Fuzzy Systems; 2010 18-23 July 2010.
Li S, Zhou W, Yuan Q, Liu Y. Seizure Prediction Using Spike Rate of Intracranial EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2013;21(6):880-6. doi: 10.1109/TNSRE.2013.2282153.
Jang y-SR, Sun C-T, Mizutani E. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. 1 ed: Pearson; 1997.
Leekwijck WV, Kerre EE. Defuzzification: criteria and classification. Fuzzy Sets and Systems. 1999;108(2):159-78. doi: https://doi.org/10.1016/S0165-0114(97)00337-0.
Arthurs S, Zaveri HP, Frei MG, Osorio I. Patient and caregiver perspectives on seizure prediction. Epilepsy & Behavior. 2010;19(3):474-7. doi: https://doi.org/10.1016/j.yebeh.2010.08.010.
Gadhoumi K, Lina J-M, Gotman J. Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity. Clinical Neurophysiology. 2013;124(9):1745-54. doi: https://doi.org/10.1016/j.clinph.2013.04.006.
Zhang Y, Zhou W, Yuan Q, Wu Q. A low computation cost method for seizure prediction. Epilepsy Research. 2014;108(8):1357-66. doi: https://doi.org/10.1016/j.eplepsyres.2014.06.007.
Hung S, Chao C, Wang S, Lin B, Lin C, editors. VLSI implementation for Epileptic Seizure Prediction System based on wavelet and chaos theory. TENCON 2010 - 2010 IEEE Region 10 Conference; 2010 21-24 Nov. 2010.
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