MULTI-CLASS EEG-BASED EPILEPTIC SEIZURE CLASSIFICATION USING HYBRID DEEP LEARNING ARCHITECTURES
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Epilepsy affects roughly 50 million people worldwide, yet reliable automated seizure detection remains an open problem. Manual EEG interpretation is slow, subjective, and requires specialist expertise that is scarce in many clinical settings. We built an automated five-class seizure classification system and evaluated three deep learning architectures—a 1D Convolutional Neural Network (1D-CNN), a Bidirectional LSTM (BiLSTM), and a CNN-LSTM hybrid—against traditional baselines (Random Forest, SVM,
Decision Tree) on the Bonn University EEG dataset. The 1D-CNN achieved the highest accuracy at 95.5% (precision 95.6%, recall 95.5%, F1 95.5%), outperforming all other models. A Streamlit web application was developed alongside the models to support real-time EEG upload and seizure prediction. Our results suggest that convolutional feature extraction is particularly effective for this task, even when compared to architectures designed to model temporal sequences.
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Adusl, S., Jain, A., Kolhe, A., Patil, A. & Manve, A. (2026). Multi-Class EEG-Based Epileptic Seizure Classification using Hybrid Deep Learning Architectures. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.192
Adusl, Sanjivani, et al.. "Multi-Class EEG-Based Epileptic Seizure Classification using Hybrid Deep Learning Architectures." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.192.
Adusl, Sanjivani,Aditya Jain,Abhijeet Kolhe,Akshat Patil, and Arya Manve. "Multi-Class EEG-Based Epileptic Seizure Classification using Hybrid Deep Learning Architectures." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.192.
References
2.G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, "Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state," Physical Review E, vol. 64, no. 6, p. 061907, Nov. 2001.
3.Subasi, "EEG signal classification using wavelet feature extraction and a mixture of expert model," Expert Systems with Applications, vol. 32, no. 4, pp. 1084–1093, May 2007.
4.D. Übeyli, "Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines," Computers in Biology and Medicine, vol. 38, no. 1, pp. 14–22, Jan. 2008.
5.H. Shoeb and J. V. Guttag, "Application of machine learning to epileptic seizure detection," in Proc. 27th Int. Conf. Machine Learning (ICML), Haifa, Israel, Jun. 2010, pp. 975–982.
6.R. Acharya, S. V. Sree, G. Swapna, R. J. Martis, and J. S. Suri, "Automated EEG analysis of epilepsy: A review," Knowledge-Based Systems, vol. 45, pp. 147–165, Jun. 2013.
7.R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, and H. Adeli, "Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals," Computers in Biology and Medicine, vol. 100, pp. 270–278, Sep. 2018.
8.Yuan, W. Zhou, S. Li, and D. Cai, "Epileptic EEG classification based on extreme learning machine and nonlinear features," Epilepsy Research, vol. 96, no. 1–2, pp. 29–38, Sep. 2011.
9.M. Tsiouris, V. C. Pezoulas, M. Zervakis, S. Konitsiotis, D. D. Koutsouris, and D. I. Fotiadis, "A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals," Computers in Biology and Medicine, vol. 99, pp. 24–37, Aug. 2018.
10.N. Sainath, O. Vinyals, A. Senior, and H. Sak, "Convolutional, long short-term memory, fully connected deep neural networks," in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, QLD, Australia, Apr. 2015, pp. 4580–4584.
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