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Open Access
Issue
ITM Web Conf.
Volume 79, 2025
International Conference on Knowledge Engineering and Information Systems (KEIS-2025)
Article Number 01020
Number of page(s) 5
DOI https://doi.org/10.1051/itmconf/20257901020
Published online 08 October 2025
  1. M. Dastgoshadeh, Z. Rabiei, Detection of epileptic seizures through EEG signals using entropy features and ensemble learning. Front. Hum. Neurosci. 16, 1084061 (2023). https://doi.org/10.3389/fnhum.2022.1084061 [Google Scholar]
  2. D. Chen, S. Wan, J. Xiang, F.S. Bao, A highperformance seizure detection algorithm based on Discrete Wavelet Transform and EEG. PLoS One 12, e0173138 (2017). https://doi.org/10.1371/journal.pone.0173138 [Google Scholar]
  3. S. Wong, A. Simmons, J. Rivera-Villicana, S. Barnett, S. Sivathamboo, P. Perucca, Z. Ge, P. Kwan, L. Kuhlmann, R. Vasa, K. Mouzakis, EEG datasets for seizure detection and prediction – A review. Epilepsia Open 8, 252–267 (2023). https://doi.org/10.1002/epi4.12704 [Google Scholar]
  4. M. Zhou, C. Tian, R. Cao, B. Wang, Y. Niu, T. Hu, H. Guo, J. Xiang, Epileptic seizure detection based on EEG signals and CNN. Front. Neuroinform. 12, 95 (2018). https://doi.org/10.3389/fninf.2018.00095 [Google Scholar]
  5. C. Daftari, J. Shah, M. Shah, Detection of epileptic seizure disorder using EEG signals. Artif. Intell.- Based Brain-Comput. Interface, 163–188 (2022). https://doi.org/10.1016/B978-0-323-91197-9.00006-0 [Google Scholar]
  6. R. Du, J. Huang, S. Zhu, EEG-based epileptic seizure detection model using CNN feature optimization, In Proceedings of the 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), IEEE, Beijing, China, December 21 (2022), 1–6 [Google Scholar]
  7. M. Shen, F. Yang, P. Wen, A real-time epilepsy seizure detection approach based on EEG using short-time Fourier transform and Google-Net convolutional neural network. Heliyon 10, e31827 (2024). https://doi.org/10.1016/j.heliyon.2024.e31827 [Google Scholar]
  8. H. Alshaya, M. Hussain, EEG-based classification of epileptic seizure types using deep network model. Mathematics 11, 2286 (2023). https://doi.org/10.3390/math11102286 [Google Scholar]
  9. C. Gómez, P. Arbeláez, M. Navarrete, Automatic seizure detection based on imaged-EEG signals through fully convolutional networks. Sci. Rep. 10, 21833 (2020). https://doi.org/10.1038/s41598-020-78784-3 [Google Scholar]
  10. A. Shoeibi, M. Khodatara, N. Ghossemi, Epileptic seizures detection using deep learning techniques: a review. Int. J. Environ. Res. Public Health 18, 5780 (2021). https://doi.org/10.3390/ijerph18115780 [Google Scholar]
  11. U. Asif, S. Roy, J. Tang, S. Harrer, SeizureNet: Multi-spectral deep feature learning for seizure type classification. Machine Learning in Clinical Neuroimaging and Radiogenomics in Neurooncology, Springer, Cham December 31 (2020), 77–87 [Google Scholar]
  12. M.K. Alharthi, K.M. Moria, D.M. Alghazzawi, H.O. Tayeb, Epileptic disorder detection of seizures using EEG signals. Sensors 22, 6592 (2022). https://doi.org/10.3390/s22176592 [Google Scholar]
  13. A. Zarei, B. Mohammadzadeh, Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy-based features of EEG signals. Comput. Biol. Med. 131, 104250 (2021). https://doi.org/10.1016/j.compbiomed.2021.104250 [Google Scholar]
  14. E. Shoka, A. Dessouky, A. El Sayed, EEG seizure detection: concepts, techniques, challenges, and future trends. Multimed. Tools Appl. 82, 4202142051 (2023). https://doi.org/10.1007/s11042-023-15052-2 [Google Scholar]
  15. S. Saminu, G. Xu, S. Zhang, I.A. el Kader, A recent investigation on detection and classification of epileptic seizure techniques using EEG signal. Brain Sci. 11, 668 (2021). https://doi.org/10.3390/brainsci11050668 [Google Scholar]
  16. P. Boonyakitanont, A. Lekuthai, K. Krisnachal, J. Songsiri, A review of feature extraction and performance evaluation in epileptic seizure detection using EEG. Biomed. Signal Process. Control 57, 101702 (2020). https://doi.org/10.1016/j.bspc.2019.101702 [Google Scholar]
  17. L.V. Tran, H.M. Tran, T.M. Le, Application of machine learning in epileptic seizure detection. Diagnostics 12, 2879 (2022). https://doi.org/10.3390/diagnostics12112879 [Google Scholar]
  18. H. Kode, K. Elleithy, L. Almazedah, Epileptic seizure detection in EEG signals using machine learning and deep learning techniques. IEEE Access 12, 80657–80668 (2024). https://doi.org/10.1109/ACCESS.2024.3409581 [Google Scholar]
  19. D.P. Dash, M. Chakraborty, M. Khorayi, Review of machine and deep learning techniques in epileptic seizure detection using physiological signals and sentiment analysis. ACM Trans. Asian Low- Resour. Lang. Inf. Process. 23, 1–29 (2024). https://doi.org/10.1145/3552512 [Google Scholar]
  20. S. Shanmugam, S. Dharmar, Implementation of a non-linear SVM classification for seizure EEG signal analysis on FPGA. Eng. Appl. Artif. Intell. 131, 107826 (2024). https://doi.org/10.1016/j.engappai.2023.107826 [Google Scholar]

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