Skip to main content
SpringerLink
Log in

Hilbert Transform and Statistical Analysis for Channel Selection and Epileptic Seizure Prediction

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

This paper is concerned with Electroencephalography (EEG) seizure prediction, which means the detection of the pre-ictal state prior to ictal activity occurrence. The basic idea of the proposed approach for EEG seizure prediction is to work on the signals in the Hilbert domain. The operation in the Hilbert domain guarantees working on the low-pass spectra of EEG signal segments to avoid artifacts. Signal attributes in the Hilbert domain including amplitude, derivative, local mean, local variance, and median are analyzed statistically to perform the channel selection and seizure prediction tasks. Pre-defined prediction and false-alarm probabilities are set to select the channels, the attributes, and bins of probability density functions (PDFs) that can be useful for seizure prediction. Due to the multi-channel nature of this process, there is a need for a majority voting strategy to take a decision for each signal segment. Simulation results reveal an average prediction rate of 96.46%, an average false-alarm rate of 0.028077/h and an average prediction time of 60.1595 min for a 90-min prediction horizon.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. WHO. (2017). Programmes and projects. http://www.who.int/mediacentre/factsheets/fs999/en/. Online; Accessed 20 May 2017.

  2. Elger, C. E. (2001). Future trends in epileptology. Current Opinion in Neurology, 14(2), 185–186.

    Article  Google Scholar 

  3. Stacey, W. C., & Litt, B. (2008). Technology insight: Neuroengineering and epilepsy-designing devices for seizure control. Nature Clinical Practice Neurology, 4(4), 190–201.

    Article  Google Scholar 

  4. Cook, M. J., O’Brien, T. J., Berkovic, S. F., Murphy, M., Morokoff, A., Fabinyi, G., et al. (2013). Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: A first-in-man study. The Lancet Neurology, 12(6), 563–571.

    Article  Google Scholar 

  5. Adeli, H., Zhou, Z., & Dadmehr, N. (2003). Analysis of EEG records in an epileptic patient using wavelet transform. Journal of Neuroscience Methods, 123(1), 69–87.

    Article  Google Scholar 

  6. Alotaiby, T., Abd El-Samie, F. E., Alshebeili, S. A., & Ahmad, I. (2015). A review of channel selection algorithms for EEG signal processing. EURASIP Journal on Advances in Signal Processing, 20(1), 66.

    Article  Google Scholar 

  7. Abd El-Samie, F. E., Alotaiby, T. N., Khalid, M. I., Alshebeili, S. A., & Aldosari, S. A. (2018). A review of EEG and meg epileptic spike detection algorithms. IEEE Access, 6, 60,673–60,688.

    Article  Google Scholar 

  8. Aarabi, A., Fazel-Rezai, R., & Aghakhani, Y. (2009). EEG seizure prediction: Measures and challenges. In Annual international conference of the IEEE engineering in medicine and biology society, 2009. EMBC 2009 (pp. 1864–1867). IEEE.

  9. Mormann, F., Kreuz, T., Rieke, C., Andrzejak, R. G., Kraskov, A., David, P., et al. (2005). On the predictability of epileptic seizures. Clinical Neurophysiology, 116(3), 569–587.

    Article  Google Scholar 

  10. Park, Y., Luo, L., Parhi, K. K., & Netoff, T. (2011). Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia, 52(10), 1761–1770.

    Article  Google Scholar 

  11. Gardner, A. B., Krieger, A. M., Vachtsevanos, G., & Litt, B. (2006). One-class novelty detection for seizure analysis from intracranial EEG. Journal of Machine Learning Research, 7, 1025–1044.

    MathSciNet  MATH  Google Scholar 

  12. Zandi, A. S., Tafreshi, R., Javidan, M., & Dumont, G. A. (2010). Predicting temporal lobe epileptic seizures based on zero-crossing interval analysis in scalp EEG. In 2010 Annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 5537–5540). IEEE.

  13. Zandi, A. S., Tafreshi, R., Javidan, M., & Dumont, G. A. (2013). Predicting epileptic seizures in scalp EEG based on a variational Bayesian gaussian mixture model of zero-crossing intervals. IEEE Transactions on Biomedical Engineering, 60(5), 1401–1413.

    Article  Google Scholar 

  14. Hung, S.-H., Chao, C.-F., Wang, S.-K., Lin, B.-S., & Lin, C.-T. (2010). VLSI implementation for epileptic seizure prediction system based on wavelet and chaos theory. In TENCON 2010–2010 IEEE region 10 conference (pp. 364–368). IEEE.

  15. Chiang, C.-Y., Chang, N.-F., Chen, T.-C., Chen, H.-H., & Chen, L.-G. (2011). Seizure prediction based on classification of EEG synchronization patterns with on-line retraining and post-processing scheme. In 2011 Annual international conference of the IEEE engineering in medicine and biology society, EMBC (pp. 7564–7569). IEEE.

  16. Gadhoumi, K., Lina, J.-M., & Gotman, J. (2013). Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity. Clinical Neurophysiology, 124(9), 1745–1754.

    Article  Google Scholar 

  17. Qi, Y., Wang, Y., Zheng, X., Zhang, J., Zhu, J., & Guo, J. (2012). Efficient epileptic seizure detection by a combined IMF-VoE feature. In 2012 Annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 5170–5173). IEEE.

  18. Xie, S., & Krishnan, S. (2011). Signal decomposition by multi-scale PCA and its applications to long-term EEG signal classification. In 2011 IEEE/ICME international conference on complex medical engineering (CME) (pp. 532–537). IEEE.

  19. Williamson, J. R., Bliss, D. W., Browne, D. W., & Narayanan, J. T. (2012). Seizure prediction using EEG spatiotemporal correlation structure. Epilepsy and Behavior, 25(2), 230–238.

    Article  Google Scholar 

  20. Elgohary, S., Eldawlatly, S., & Khalil, M. I. (2016). Epileptic seizure prediction using zero-crossings analysis of EEG wavelet detail coefficients. In 2016 IEEE conference on computational intelligence in bioinformatics and computational biology (CIBCB) (pp. 1–6). IEEE.

  21. Ozdemir, N., & Yildirim, E. (2014). Patient specific seizure prediction system using Hilbert spectrum and Bayesian networks classifiers. In Computational and mathematical methods in medicine (Vol. 2014).

  22. Aarabi, A., & He, B. (2014). Seizure prediction in hippocampal and neocortical epilepsy using a model-based approach. Clinical Neurophysiology, 125(5), 930–940.

    Article  Google Scholar 

  23. Welch, P. (1967). The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Transactions on Audio and Electroacoustics, 15(2), 70–73.

    Article  Google Scholar 

  24. Alotaiby, T. N., Alshebeili, S. A., Alotaibi, F. M., & Alrshoud, S. R. (2017). Epileptic seizure prediction using CSP and LDA for scalp EEG signals. In Computational intelligence and neuroscience (Vol. 2017).

  25. Myers, M. H., Padmanabha, A., Hossain, G., de Jongh Curry, A. L., & Blaha, C. D. (2016). Seizure prediction and detection via phase and amplitude lock values. Frontiers in Human Neuroscience, 10, 80.

    Article  Google Scholar 

  26. Consul, S., Morshed, B. I., & Kozma, R. (2013). Hardware efficient seizure prediction algorithm. In Nanosensors, biosensors, and info-tech sensors and systems 2013 (Vol. 8691, p. 86911J). International Society for Optics and Photonics.

  27. Chu, H., Chung, C. K., Jeong, W., & Cho, K.-H. (2017). Predicting epileptic seizures from scalp EEG based on attractor state analysis. Computer Methods and Programs in Biomedicine, 143, 75–87.

    Article  Google Scholar 

  28. Sedik, A., Emara, H. M., Hamad, A., Shahin, E. M., El-Hag, N. A., Khalil, A., et al. (2019). Efficient anomaly detection from medical signals and images. International Journal of Speech Technology, 22(3), 739–767.

    Article  Google Scholar 

  29. Daoud, H., & Bayoumi, M. A. (2019). Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems, 13(5), 804–813.

    Article  Google Scholar 

  30. Yang, Y., Zhou, M., Niu, Y., Li, C., Cao, R., Wang, B., et al. (2018). Epileptic seizure prediction based on permutation entropy. Frontiers in Computational Neuroscience, 12, 55.

    Article  Google Scholar 

  31. PhysioNet. (2000). CHB-MIT scalp EEG database. https://www.physionet.org/pn6/chbmit/. Online; Accessed 1 January 2017.

  32. Poularikas, A. D. (1998). Handbook of formulas and tables for signal processing (Vol. 13). Boca Raton: CRC Press.

    Book  Google Scholar 

  33. Kuo, S. M., Lee, B. H., & Tian, W. (2013). Real-time digital signal processing: Fundamentals, implementations and applications. Chichester: Wiley.

    Google Scholar 

  34. El-Samie, F. E. A. (2011). Information security for automatic speaker identification. In Information security for automatic speaker identification (pp. 1–122). Springer.

  35. Yin, L., Yang, R., Gabbouj, M., & Neuvo, Y. (1996). Weighted median filters: A tutorial. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 43(3), 157–192.

    Article  Google Scholar 

Download references

Acknowledgements

Dr. Alshebeili acknowledges the support received by King Saud University through the Researchers Supporting Project number RSP-2020/46.

Author information

Authors and Affiliations

  1. Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Heba M. Emara, Mohamed Elwekeil, Taha E. Taha, Adel S. El-Fishawy, El-Sayed M. El-Rabaie & Fathi E. Abd El-Samie

  2. KACST, Riyadh, Kingdom of Saudi Arabia

    Turky Alotaiby

  3. Electrical Engineering Department, KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICS), King Saud University, Riyadh, Kingdom of Saudi Arabia

    Saleh A. Alshebeili

  4. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 84428, Kingdom of Saudi Arabia

    Fathi E. Abd El-Samie

Authors
  1. Heba M. Emara
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Elwekeil
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Taha E. Taha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adel S. El-Fishawy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. El-Sayed M. El-Rabaie
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Turky Alotaiby
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Saleh A. Alshebeili
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Fathi E. Abd El-Samie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Heba M. Emara.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Emara, H.M., Elwekeil, M., Taha, T.E. et al. Hilbert Transform and Statistical Analysis for Channel Selection and Epileptic Seizure Prediction. Wireless Pers Commun 116, 3371–3395 (2021). https://doi.org/10.1007/s11277-020-07857-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07857-3

Keywords

Search

Navigation