Transactions on Machine Intelligence

Transactions on Machine Intelligence

Comparative Prediction of Epileptic Seizures Based on Phase Synchronization in EEG

Document Type : Original Article

Authors
Faculty of Engineering, Islamic Azad University, Mashhad branch, Mashhad, Iran
Abstract
Epileptic seizure prediction has garnered significant attention in recent years due to its potential to improve patient outcomes and reduce the burden of epilepsy. The integration of advanced machine learning techniques, particularly deep learning, into seizure prediction models has opened new avenues for research. Currently, epilepsy patients who have not achieved complete seizure control face the challenge of sudden and unpredictable epileptic seizures. A method capable of predicting seizure onset could significantly enhance the quality of life for these individuals. The fundamental basis of seizure prediction lies in distinguishing the preictal phase dynamics from other phases. In this study, phase synchronization is employed as an indicator for analyzing interactions between different brain regions and as a reliable metric for identifying the preictal period. Furthermore, seizure prediction horizon and seizure onset time are two critical factors in evaluating seizure prediction methods. Although previous studies have primarily used these temporal parameters for assessment, this paper incorporates them into a neuro-fuzzy model, allowing for adaptive seizure prediction based on patient feedback. The implementation of the proposed model on intracranial EEG signals demonstrated that, across various time window values, sensitivity and specificity exceeded 70%.
Keywords

[1]    Litt, B., & Lehnertz, K. (2002). Seizure prediction and the preseizure period. Current Opinion in Neurology, 15(2), 173–177. https://doi.org/10.1097/00019052-200204000-00008
[2]    Mormann, F., Andrzejak, R. G., Elger, C. E., & Lehnertz, K. (2007). Seizure prediction: The long and winding road. Brain, 130(2), 314–333. https://doi.org/10.1093/brain/awl241
[3]    Schelter, B., Winterhalder, M., Maiwald, T., Brandt, A., Schad, A., Timmer, J., & Schulze-Bonhage, A. (2006). Do false predictions of seizures depend on the state of vigilance? A report from two seizure-prediction methods and proposed remedies. Epilepsia, 47(12), 2058–2070. https://doi.org/10.1111/j.1528-1167.2006.00848.x
[4]    Mirowski, P., et al. (2009). Classification of patterns of EEG synchronization for seizure prediction. Clinical Neurophysiology, 120(11), 1927–1940. https://doi.org/10.1016/j.clinph.2009.09.002
[5]    Carney, P. R., et al. (2011). Seizure prediction: Methods. Epilepsy & Behavior, 22(Suppl. 1), S94–S101. https://doi.org/10.1016/j.yebeh.2011.09.001
[6]    Soleimani-B, H., et al. (2012). Adaptive prediction of epileptic seizures from intracranial recordings. Biomedical Signal Processing and Control, 7(5), 456–464. https://doi.org/10.1016/j.bspc.2011.11.007
[7]    Maniyath, S. R., Vinod, V. P., Niveditha, M., Pooja, R., Bhat, N. P., Shashank, N., & Hebbar, R. (2018). Plant disease detection using machine learning. In 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C) (pp. 41–45). https://doi.org/10.1109/ICDI3C.2018.00017
[8]    Crawford, M., Khoshgoftaar, T., Prusa, J. D., Richter, A. N., & Najada, H. A. (2015). Survey of review spam detection using machine learning techniques. Journal of Big Data, 2, 1–24. https://doi.org/10.1186/s40537-015-0029-9
[9]    Wang, L., Xue, W., Li, Y., Luo, M.-L., Huang, J., Cui, W., & Huang, C. (2017). Automatic epileptic seizure detection in EEG signals using multi-domain feature extraction and nonlinear analysis. Entropy, 19(6), 222. https://doi.org/10.3390/e19060222
[10]    Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Breitenbacher, D., & Elovici, Y. (2017). Detection of unauthorized IoT devices using machine learning techniques. arXiv preprint, arXiv:1709.04647.
[11]    Sultana, N., Chilamkurti, N., Peng, W., & Alhadad, R. (2018). Survey on SDN based network intrusion detection system using machine learning approaches. Peer-to-Peer Networking and Applications, 12, 493–501. https://doi.org/10.1007/s12083-017-0630-0
[12]    Islam, M. R., Kabir, M. A., Ahmed, A., Kamal, A., Wang, H., & Ulhaq, A. (2018). Depression detection from social network data using machine learning techniques. Health Information Science and Systems, 6. https://doi.org/10.1007/s13755-018-0046-0
[13]    Mullen, T., Kothe, C., Chi, Y., Ojeda, A., Kerth, T., Makeig, S., Jung, T., & Cauwenberghs, G. (2015). Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE Transactions on Biomedical Engineering, 62, 2553–2567. https://doi.org/10.1109/TBME.2015.2481482
[14]    Schelter, B., Winterhalder, M., Maiwald, T., Brandt, A., Schad, A., Schulze-Bonhage, A., & Timmer, J. (2006). Testing statistical significance of multivariate time series methods for epileptic seizure prediction. Chaos, 16(1), 013108. https://doi.org/10.1063/1.2137623
[15]    Ihle, M., Feldwisch-Drentrup, H., Teixeira, C. A., Witon, A., Schelter, B., Timmer, J., & Schulze-Bonhage, A. (2012). EPILEPSIAE: A European epilepsy database. Computer Methods and Programs in Biomedicine, 106(3), 127–138. https://doi.org/10.1016/j.cmpb.2010.08.011
[16]    Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N. C., Tung, C. C., & Liu, H. H. (1998). 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, 454, 903–995. https://doi.org/10.1098/rspa.1998.0193
[17]    Nasiri, S., & Sharafat, A. (2010). Prediction of epileptic seizures using phase synchronization analysis in time-frequency domain. Proceedings of the 19th Iranian Conference on Electrical Engineering, Amirkabir University of Technology, Tehran, Iran, 6.
[18]    Rosenblum, M., et al. (2001). Chapter 9: Phase synchronization: From theory to data analysis. In Handbook of Biological Physics. https://doi.org/10.1016/S1383-8121(01)80012-9
[19]    Mormann, F., et al. (2000). Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Physica D: Nonlinear Phenomena, 144(3), 358–369. https://doi.org/10.1016/S0167-2789(00)00087-7
[20]    Daniel, W. W. (1990). Spearman rank correlation coefficient. In Applied Nonparametric Statistics (2nd ed., pp. 358–365). Boston: PWS-Kent. ISBN 0-534-91976-6.
Volume 2, Issue 4
Autumn 2019
Pages 235-245

  • Receive Date 20 June 2019
  • Revise Date 29 July 2019
  • Accept Date 23 December 2019