EEG Seizure Prediction with Less Samples

Document Type : Original Article

Authors

Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering (FEE), Menoufia University

Abstract

In this paper, a new proposed approach using wavelet domain statistical analysis is presented to predict epilepsy seizures. In addition, in seizure prediction systems of wearable devices, while using wireless communication technologies such as Bluetooth, the challenge encountered is the amount of data acquired. Compressive sensing has been investigated as a tool to reduce data rates needed to be transferred and processing time as well. Histograms for segments of various signal states were studied in wavelet domain utilizing different signal processing tools such as differentiator, median filtering, local mean, and local variance estimators after the compressive sensing is applied. Simulation results revealed the possibility to use compressive sensing in epileptic seizure prediction systems. This opens the door for more compact seizure prediction algorithms.

Keywords

References

[1]     https://www.who.int/news-room/fact-sheets/detail/epilepsy
[2]     S. Wang, W. A. Chaovalitwongse, S. Wong, “A Novel Reinforcement Learning Framework for Online Adaptive Seizure Prediction,” 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 499-504, December 2010.
[3]     Alotaiby, Turky N., Saleh A. Alshebeili, Tariq Alshawi, Ishtiaq Ahmad and Fathi E. Abd El-Samie. “EEG seizure detection and prediction algorithms: a survey.” EURASIP Journal on Advances in Signal Processing, Volume 2014, December 2014.
[4]     Kiral-Kornek, Isabell et al., "Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System" EBioMedicine, Volume 27, pp. 103-111, 2018.
[5]     Atlas of EEG & Seizure Semiology. B. Abou-Khalil; Musilus, K.E.; Elsevier, 2006
[6]      H Khamis, A Mohamed, S Simpson, Frequency–moment signatures: a method for automated seizure detection from scalp EEG. Clin. Neurophysiol. 124(12), pp. 2317–2327, 2013
[7]     A. Shoeb, H. Edwards, J. Connolly, B. Bourgeois, S. Ted Treves, and J. Guttag, “Patient-specific seizure onset detection,” Epilepsy Behavior, volume 5, no. 4, pp.483–498, 2004.
[8]     .R. Meier, H. Dittrich, A. Schulze-Bonhage and A. Aertsen, “Detecting epileptic seizures in long-term human EEG: A new approach to automatic online and real-time detection and classification of polymorphic seizure patterns,” J. Clin. Neurophysiol., pp.119-131, 2008.
[9]     L. Orosco, A. Garcés Correa, E. Laciar Leber, “Review: asurvey of performance and techniques for automatic epilepsy detection,” J. Med Bio l Eng. 33 (6), 2013.
[10]  W Zhou, Y Liu, Q Yuan, X Li, Epileptic seizure detection using lacunarity and Bayesian linear discriminant analysis in intracranial EEG. IEEE Trans. Biomed. Eng. 60(12), pp. 3375–3381 2013.
[11]  Y Liu, W Zhou, Q Yuan, S Chen, “Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG," IEEE Trans. Neural Syst. Rehabil. Eng. 20(6), pp. 749–755, 2012.
[12]  K. Abualsaud, M. Mahmuddin, R. Hussein, and A. Mohamed, “Performance evaluation for compression-accuracy trade-off using compressive sensing for EEG-based epileptic seizure detection in wireless tele-monitoring”, IWCMC Conf., Sardinia, Italy, pp. 231-236, 2013.
[13]  A. Procházka, J. UhlířP.J.W. Rayner, N.G. Kingsbury, Signal Analysis and Prediction, Springer Science Business Media, LLC, 1998.
[14]  Wu Y. Histogram. In: Ikeuchi K. (eds) Computer Vision. Springer, Boston, MA, 2014.
[15]  Cook, P., Wheater, P. Using Statistics to Understand the Environment. London: Routledge, 2000.
[16]  F.E.A. El-Samie, Information Security for Automatic Speaker Identification, Springer Briefs in Speech Technology, Information Security for Automatic Speaker Identification,  DOI 10.1007/978-1-4419-9698-5_1
[17]  S. M. Kuo, B. H. Lee, W. Tian, Real-Time Digital Signal Processing, Implementations and Applications, John Wiley & Sons, 2006.
[18]  L. Yin, R. Yang, M. Gabbouj, Y. Neuvo, “Weighted Median Filters: A Tutorial,” IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing, Volume 43, No. 3, pp. 157-192, 1996.
[19]  H. Rauhut, Compressive Sensing and Structured Random Matrices, De Gruyter, 2010.
[20]  P. Nahar, M. Kolte, "An introduction to compressive sensing and its application", International Journal of Scientific and Research Publications, Volume 4, Issue 6, June 2014.
[21]  CHB-MIT Scalp EEG Database, [Online]. Available: http://physionet.org/physiobank/database/chbmit/
Menoufia Journal of Electronic Engineering Research
Volume 28, ICEEM2019-Special Issue
ICEEM2019-Special Issue: 1st International Conference on Electronic Eng., Faculty of Electronic Eng., Menouf, Egypt, 7-8 Dec.
2019
Pages 220-5

Files

Share

How to cite

Statistics