ºÚÁϳԹÏÍø

Home⇛Philippine Journal of Material Science and Nanotechnology⇛vol. 8 no. 1 (2022)

Recognizing and Visualizing Epileptic Seizure Based on Electroencephalogram (EEG) Using Spiking Neural Networks

Shainna A. Cosa | Maria Carla F. Manzano | Enrique M. Manzano

 

Abstract:

The application for machine learning was seen to be beneficial in the field of medicine, especially with the way current systems process electroencephalogram data (EEG) to detect and recognize seizures. This work is aimed at developing a simpler, cost-effective but equally accurate system of recognizing and visualizing epileptic seizures based on electroencephalogram data (EEG). Utilizing public EEG datasets, the study used current processes known in the field of data-science such as pre-processing the data to remove discrepancies, combined with the powerful integrated programming environment Python and external machine learning packages like Brian, the study created a Binary-classification neuron model, utilizing an leaky-integrate-and-fire model combined with an unsupervised learning algorithm called spike-time dependent plasticity (STDP) to provide the most accurate results during testing. The results exhibited a high sensitivity of the created neuron model, and it obtained an accuracy score of 94.6%. The proposed spiking neural network model has been found to be exceptionally efficient in recognizing and visualizing epileptic seizures in a binary classification example; however, multi-classification problems such as analyzing EEG data by multiple classifications will require a more complex SNN Model to be developed.



References:

  1. L. Abbott and W. Gerstner, “Homeostasis and learning through spike-timing dependent plasticity,” Methods and Models in Neurophysics, Elsevier Science, 2004.
  2. A. Abusnaina and R. Abdullah, “Spiking neuron models: A Review,” International Journal of Digital Content Technology and its Applications, vol. 8, pp. 14-21, 2014.
  3. A. Al-Nafjan, K. Alharthi, and H. Kurdi, H., “Lightweight building of an electroencephalogram-based emotion detection system,” Brain Sciences, vol. 10(11), 781, 2020. doi:10.3390/brainsci10110781
  4. H.U. Ashid, “Epileptic seizure recognition,” version 2, 2018. Retrieved January 19, 2021 from
  5. A. Blessing, O. Abisoye, et al., “Prediction of epileptic seizure using support vector Machine and genetic algorithm,” International Engineering Conference, 2019.
  6. C. Chan, “Classical neural network: What really are nodes and layers?,” Towards Data Science, 2020.Retrieved January 26, 2021, from
  7. K.M. Dabrowski, D.J. Castaño, & J.L.Tartar, “Basic neuron model electrical equivalent circuit: an undergraduate laboratory exercise,” Journal of Undergraduate Neuroscience Education: JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience, vol. 12, no. 1, pp. A49–A52, 2013.
  8.  S. Dutta, V. Kumar, A. Shukla, et al., “Leaky integrate and fire neuron by charge-discharge dynamics in floating-body MOSFET, Scientific Reports, vol. 7, no. 8257, 2017.
  9. M. Eberlein, R. Hildebrand, et al., “Convolutional Neural Networks for Epileptic Seizure Prediction,” IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018. doi:10.1109/bibm.2018.8621225
  10. Epilepsy. (n.d.). Retrieved December 23, 2020, from
  11. C. Espinosa-Jovel, R. Toledano, et al., “Epidemiological profile of epilepsy in low income populations,” Seizure, vol. 56, pp. 67–72, 2018. doi:10.1016/j.seizure.2018.02.002
  12. B. Franswoth, “What is EEG (Electroencephalography) and How Does it Work?,” 2020. Retrieved December 27, 2020, [online] Available: .
  13. Y. Gao, B. Gao, et al., “Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification. Frontiers in Neurology, vol. 11, 375, 2020. doi:10.3389/fneur.2020.00375
  14. N. Ghani, “Izhikevich Model of Cortical Neurons,” 2018. Retrieved from:
  15. P. Goel, H. Liu, D. Brown, & A. Datta, “On the use of spiking neural network for EEG classification,” International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 12, no. 4, pp. 295-304, 2008. doi:10.3233/kes-2008-12404
  16. D. Goodman, “Brian: A simulator for spiking neural networks in Python,” Frontiers in Neuroinformatics, vol. 2, 2008. doi:10.3389/neuro.11.005.2008
  17. T. Iakymchuk, A. Rosado-Muñoz, et al., “Simplified spiking neural network architecture and STDP learning algorithm applied to image classification,” EURASIP Journal on Image and Video Processing, no. 4, 2015.
  18. M. Jaymalin, “Who: Most epileptics from poor countries lack treatment,” June 30, 2019. Retrieved from
  19. N. Kasabov and E. Capecci, “Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes,” Information Sciences, vol. 294, 565-575, 2015.doi:10.1016/j.ins.2014.06.028
  20. F. Lazzeri, “How to accelerate devops with machine learning lifecycle management,” June 7, 2019. Retrieved January 28, 2021, from
  21. J. Löfhede, et al. “Classification of burst and suppression in the neonatal EEG,” Journal of NeuralEengineering, vol. 5, no. 4, pp. 401-410, 2021.
  22. Y. Luo, Q. Fu, et al., “EEG-based emotion classification using spiking neural networks,” IEEE Access, vol. 8, pp. 46007-46016, 2020. doi:10.1109/access.2020.2978163
  23. K. Moalong, A. Espiritu, et al., “Treatment gaps and challenges in epilepsy care in the Philippines.,” Epilepsy & Behavior, no. 107491, 2020.
  24. K. Morik, J. Fürnkranz, et al., “Medicine: Applications of machine learning,” Encyclopedia of Machine Learning, pp. 654–661, 2020. doi:10.1007/978-0-387-30164-8_530
  25. S. Mannarswamy, “Everything you need to know about neural networks,” Open Source For You, April 20, 2019. Retrieved September 20, 2021, from .
  26. S. Nagel, S. (2019). “Towards a home-use BCI: fast asynchronous control and robust non-control state detection,” (Doctoral dissertation), 2019. Retrieved from
  27. K. Ousmane, I. Diop, et al., “Novel classification method of spikes morphology in EEG signal using machine learning,” Procedia Computer Science, vol. 148, pp. 70-79, 2019. doi: 10.1016/j.procs.2019.01.010.
  28. M. Pfeiffer and T. Pfeil, “Deep learning with spiking neurons: Opportunities and challenges,” Frontiers in Neuroscience, vol. 12, 2018. doi:10.3389/fnins.2018.00774
  29. K. Polat and M. Nour, “Epileptic seizure detection based on new hybrid models with electroencephalogram signals,” IRBM, vol. 41, no. 6, pp. 331-353, 2020. IRBM. doi:10.1016/j.irbm.2020.06.008
  30. V. Rangan, V., Ghosh, A. et. al. (2010). A Subthreshold aVLSI Implementation of the Izhikevich Simple Neuron Model. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 4164-7. 10.1109/IEMBS.2010.5627392.
  31. R. Rosas-Romero, E. Guevara, et al., “Prediction of epileptic seizures with convolutional neural networks and functional near-infrared spectroscopy signals,” Computers in Biology and Medicine, no. 103355, 2019. doi:10.1016/j.compbiomed.2019.103355
  32. S. A. Saeedinia, M. R. Jahed-Motlagh, A. Tafakhori, et al., “Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals,” Scientific Reports, vol. 11, no. 12064, 2021.
  33. C.H. Seng, R. Demirli, et al., “Seizure detection in EEG signals using support vector machines,” 38th Annual Northeast Bioengineering Conference (NEBEC), 2012. doi:10.1109/nebc.2012.6207048
  34. H.T. Shiao, V. Cherkassky, et al., “SVM-based system for prediction of epileptic seizures from iEEG signal,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 5, pp. 1011–1022, 2017. doi:10.1109/tbme.2016.2586475
  35. M.K. Siddiqui, R. Morales-Menendez, X. Huang, et al. “A Review of epileptic seizure detection using machine learning classifiers,” Brain Informatics, vol. 7, no. 5, 2020.
  36. C. Stafstrom, L. Carmant, “Seizures and epilepsy: an overview for neuroscientists,” Cold Spring Harbor Perspectives in Medicine, vol. 5, no. 6, a022426, 2015.
  37. S. Suresh, C. Raju, and G. Kiran, “An Empirical analysis of leaky integrate and fire neuron model,” International Journal of Engineering Research and Technology, vol. 9, no. 5, 2020. 10.17577/IJERTV9IS050734.
  38. A. Tavanaei, M. Ghodrati, S.R. Kheradpisheh, T. Masquelier, and A. Maida, “Deep learning in spiking neural networks,” Neural Networks, vol. 111, pp. 47–63, 2019.
  39. A. Temko, E. Thomas, W. Marnane, G. Lightbody, and G. Boylan, “EEG-based neonatal seizure detection with support vector machines,” Clinical Neurophysiology, vol. 122, no. 33, 464–473, 2011.
  40. T. Trappenberg, “Fundamentals of Computational Neuroscience,” Oxford University Press, 2010.
  41. N.D. Truong, D. Nguyen, et al., “Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram,” Neural Networks, vol. 105, pp. 104–111, 2018. doi:10.1016/j.neunet.2018.04.0
  42. S.M. Usman, M. Usman, and S. Fong, “Epileptic Seizures Prediction Using Machine Learning Methods,” Computational and Mathematical Methods in Medicine, vol. 2017, pp. 1-10, 2017. doi:10.1155/2017/9074759
  43. J. Vitay, H.U. Dinkelbach, F.H. Hamker, ANNarchy: a code generation approach to neural simulations on parallel hardware. Frontiers in Neuroinformatics, vol. 9, 2015.
  44. “What is a neural network?” TIBCO Software Inc. (n.d.).
  45. P. Zarrin, R. Zimmer, e. al., “Epileptic seizure detection using a neuromorphic-compatible deep spiking neural network,” Bioinformatics and Biomedical Engineering, vol. 12108, pp. 389-394, 2020. doi:10.1007/978-3-030-45385-5_34

Tools


Copyright © 2025   ºÚÁϳԹÏÍø