Automatic Detection of Epileptic Seizures from EEG Signals Using Artificial Intelligence Methods

Abstract

Epilepsy is a neurological disorder in which involuntary contractions, sensory abnormalities, and changes occur as a result of abrupt and uncontrolled discharges in the neurons in the brain, which disrupt the systems regulated by the brain. In epilepsy, abnormal electrical impulses from cells in various brain areas are noticed. The accurate interpretation of these electrical impulses is critical in the illness diagnosis. This study aims to use different machine-learning algorithms to diagnose epileptic seizures. The frequency components of EEG data were extracted using parametric approaches. This feature extraction approach was fed into machine learning classification algorithms, including Artificial Neural Network (ANN), Gradient Boosting, and Random Forest. The ANN classifier was shown to have the most significant test performance in this investigation, with roughly 97% accuracy and a 91% F1 score in recognizing epileptic episodes. The Gradient Boosting classifier, on the other hand, performed similarly to the ANN, with 96% accuracy and a 93% F1 score.

Keywords

• Epilepsy
• EEG
• Machine learning
• Epileptic seizure
• ANN

References

1. [1] J. Engel, T. A. Pedley, and J. Aicardi, Epilepsy: a comprehensive textbook. Lippincott Williams & Wilkins, 2008.
2. [2] S. Reddy, S. Allan, S. Coghlan, and P. Cooper, "A governance model for the application of AI in health care," Journal of the American Medical Informatics Association, vol. 27, no. 3, pp. 491-497, 2020.
3. [3] WHO, " World Health Organization: Epilepsy" World Health Organization., vol. https://www.who.int/news -room/fact -sheets/detail/epilepsy, 2023.
4. [4] B. Karlık and Ş. B. Hayta, "Comparison machine learning algorithms for recognition of epileptic seizures in EEG," Proceedings IWBBIO, vol. 2014, pp. 1-12, 2014.
5. [5] L. D. Iasemidis, "Epileptic seizure prediction and control," IEEE Transactions on Biomedical Engineering, vol. 50, no. 5, pp. 549-558, 2003.
6. [6] A. Subasi, M. K. Kiymik, A. Alkan, and E. Koklukaya, "Neural network classification of EEG signals by using AR with MLE preprocessing for epileptic seizure detection," Mathematical and computational applications, vol. 10, no. 1, pp. 57-70, 2005.
7. [7] Ercelebi and Subasi, "Classification of EEG for epilepsy diagnosis in wavelet domain using artificial neural network and multi-linear regression," 2006 IEEE 14th Signal Processing and Communications Applications, pp. 1-4, 2006.
8. [8] Z. Yucel and A. B. Ozguler, "Detection of epilepsy seizures and epileptic indicators in EEG signals," in 2008 IEEE 16th Signal Processing, Communication and Applications Conference, 2008: IEEE, pp. 1-4.

9. [9] A. T. Tzallas, M. G. Tsipouras, and D. I. Fotiadis, "Epileptic seizure detection in EEGs using time–frequency analysis," IEEE Transactions on information technology in biomedicine, vol. 13, no. 5, pp. 703-710, 2009.
10. [10] P. W. Mirowski, Y. LeCun, D. Madhavan, and R. Kuzniecky, "Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG," in 2008 IEEE workshop on machine learning for signal processing, 2008: IEEE, pp. 244-249.
11. [11] L. Chisci et al., "Real-time epileptic seizure prediction using AR models and support vector machines," IEEE Transactions on Biomedical Engineering, vol. 57, no. 5, pp. 1124-1132, 2010.
12. [12] M.-P. Hosseini, H. Soltanian-Zadeh, K. Elisevich, and D. Pompili, "Cloud-based deep learning of big EEG data for epileptic seizure prediction," in 2016 IEEE Global Conference on signal and information processing (GlobalSIP), 2016: IEEE, pp. 1151-1155.
13. [13] M. H. Cılasun and H. Yalçın, "A deep learning approach to EEG based epilepsy seizure determination," in 2016 24th Signal Processing and Communication Application Conference (SIU), 2016: IEEE, pp. 1573-1576.
14. [14] A. R. Özcan and S. Ertürk, "Epileptic seizure prediction with recurrent convolutional neural networks," in 2017 25th Signal Processing and Communications Applications Conference (SIU), 2017: Ieee, pp. 1-4.
15. [15] B. KARAKAYA, K. Turgay, and A. GULTEN, "FPGA-based ANN design for detecting epileptic seizure in EEG signal," Balkan Journal of Electrical and Computer Engineering, vol. 6, no. 2, pp. 83-87, 2018.
16. [16] H. Daoud and M. A. Bayoumi, "Efficient epileptic seizure prediction based on deep learning," IEEE Transactions on Biomedical Circuits and Systems, vol. 13, no. 5, pp. 804-813, 2019.
17. [17] M. Savadkoohi, T. Oladunni, and L. Thompson, "A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) Signal," Biocybernetics and Biomedical Engineering, vol. 40, no. 3, pp. 1328-1341, 2020.
18. [18] X. Wang, G. Gong, N. Li, and S. Qiu, "Detection analysis of epileptic EEG using a novel random forest model combined with grid search optimization," Frontiers in human neuroscience, vol. 13, p. 52, 2019.
19. [19] Z. Chen, G. Lu, Z. Xie, and W. Shang, "A unified framework and method for EEG-based early epileptic seizure detection and epilepsy diagnosis," IEEE Access, vol. 8, pp. 20080-20092, 2020.
20. [20] T. Dissanayake, T. Fernando, S. Denman, S. Sridharan, and C. Fookes, "Deep learning for patient-independent epileptic seizure prediction using scalp EEG signals," IEEE Sensors Journal, vol. 21, no. 7, pp. 9377-9388, 2021.
21. [21] P. K. Sethy, M. Panigrahi, K. Vijayakumar, and S. K. Behera, "Machine learning based classification of EEG signal for detection of child epileptic seizure without snipping," International Journal of Speech Technology, pp. 1-12, 2021.
22. [22] N. M. POUR and Y. ÖZBEK, "Epileptic Seizure Detection based on EEG Signal using Boosting Classifiers," Erzincan University Journal of Science and Technology, vol. 14, no. 1, pp. 159-167, 2021.
23. [23] B. ÇAĞLIYAN and K. Utku, "Epilepsi EEG Verilerinin Makine Öğrenmesi Teknikleriyle Sınıflandırılması," Avrupa Bilim ve Teknoloji Dergisi, no. 23, pp. 163-172, 2021.
24. [24] F. Manzouri, S. Heller, M. Dümpelmann, P. Woias, and A. Schulze-Bonhage, "A comparison of machine learning classifiers for energy-efficient implementation of seizure detection," Frontiers in systems neuroscience, vol. 12, p. 43, 2018.
25. [25] T. Jayalakshmi and A. Santhakumaran, "Statistical normalization and backpropagation for classification," International Journal of Computer Theory and Engineering, vol. 3, no. 1, pp. 1793-8201, 2011.
26. [26] P. Werbos, "Beyond regression: New tools for prediction and analysis in the behavior science," Ph.D. thesis, Harvard University, 1974.
27. [27] D. E. Rumelhart, J. L. McClelland, and C. PDP Research Group, Parallel distributed processing: Explorations in the microstructure of cognition, Vol. 1: Foundations. MIT Press, 1986.
28. [28] A. Öter, O. Aydoğan, and D. Tuncel, "Automatic sleep stage classification using Artificial Neural Networks with Wavelet Transform," Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, vol. 8, no. 1, pp. 59-68, 2019.
29. [29] C. D. Lewis, "Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting," (No Title), 1982.
30. [30] S. F. Witt and C. A. Witt, Modeling and forecasting demand in tourism. Academic Press Ltd., 1992.
31. [31] S. R. Dhole, A. Kashyap, A. N. Dangwal, and R. Mohan, "A novel helmet design and implementation for drowsiness and fall detection of workers on-site using EEG and Random-Forest Classifier," Procedia Computer Science, vol. 151, pp. 947-952, 2019.
32. [32] R. B. Messaoud and M. Chavez, "Random Forest classifier for EEG-based seizure prediction," arXiv preprint arXiv:2106.04510, 2021.
33. [33] J. Son, I. Jung, K. Park, and B. Han, "Tracking-by-segmentation with online gradient boosting decision tree," in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 3056-3064.
34. [34] O. E. Karpov et al., "Evaluation of Unsupervised Anomaly Detection Techniques in Labelling Epileptic Seizures on Human EEG," Applied Sciences, vol. 13, no. 9, p. 5655, 2023.
35. [35] Z. Mohammadpoory, M. Nasrolahzadeh, and S. A. Amiri, "Classification of healthy and epileptic seizure EEG signals based on different visibility graph algorithms and EEG time series," Multimedia Tools and Applications, pp. 1-22, 2023.