Intelligent Learning Techniques for Epileptic Seizure Prediction Using EEG Signals: A Comprehensive Review
DOI:
https://doi.org/10.29304/jqcsm.2026.18.12501Keywords:
Electroencephalography-EEG, Epileptic Seizure Prediction, Machine Learning, Deep Learning, Explainable AI-XAI, PGI Trilemma, Wavelet Transform, Interpretability, EpilepsyAbstract
Epileptic seizure prediction facilitates proactive rather than reactive medical treatment, thereby promoting patient safety and improved living conditions. One of the most prominent tools employed for epileptic seizure prediction purposes is electroencephalography, considering its temporal resolution and ability to detect brain activity. This paper offers an organized and tightly focused review on new intelligent learning methods applied for epileptic seizure prediction via EEG. This review discusses publicly and privately accessible datasets, preprocessing and segmentation, feature spaces, machine learning and deep neural networks, evaluation schemes, and postprocessing. It points out major bottlenecks preventing more effective implementation, including an overdependence on a few common datasets, preprocessing conventions, cross-validation on different patients, and poor interpretation and implementation strategies. This paper proposes future developments on more reliable seizure predictors.
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. K. M. Fiest, K. M. Sauro, S. Wiebe, S. B. Patten, C.-S. Kwon, J. Dykeman, T. Pringsheim, D. L. Lorenzetti, and N. Jetté, “Prevalence and incidence of epilepsy: A systematic review and meta-analysis of international studies,” Neurology, vol. 88, no. 3, pp. 296–303, Jan. 2017.
.E. Beghi et al., “Global, regional, and national burden of epilepsy, 1990–2016: A systematic analysis for the global burden of disease study 2016,” Lancet Neurol., vol. 18, no. 4, pp. 357–375, Apr. 2019.
. R. S. Fisher, C. Acevedo, A. Arzimanoglou, A. Bogacz, J. H. Cross, C. E. Elger, J. Engel, L. Forsgren, J. A. French, M. Glynn, D. C. Hesdorffer, B. I. Lee, G. W. Mathern, S. L. Moshé, E. Perucca, I. E. Scheffer, T. Tomson, M. Watanabe, and S. Wiebe, “ILAE official report: A practical clinical definition of epilepsy,” Epilepsia, vol. 55, no. 4, pp. 475–482, Apr. 2014.
. J. J. Falco-Walter, I. E. Scheffer, and R. S. Fisher, “The new definition and classification of seizures and epilepsy,” Epilepsy Res., vol. 139, pp. 73–79, Jan. 2018.
.A. M. Pack, “Epilepsy overview and revised classification of seizures and epilepsies,” Continuum Lifelong Learn. Neurol., vol. 25, no. 2, pp. 306–321, Apr. 2019.
. T. N. Alotaiby, S. A. Alshebeili, F. M. Alotaibi, and S. R. Alshhawy, “Epileptic seizure prediction using CSP and LDA for scalp EEG signals,” Comput. Intell. Neurosci., vol. 2017, pp. 1–11, Jan. 2017.
.F. Mormann, R. G. Andrzejak, C. E. Elger, and K. Lehnertz, “Seizure prediction: The long and winding road,” Brain, vol. 130, no. 2, pp. 314–333, Feb. 2007.
. L. Kuhlmann, K. Lehnertz, M. P. Richardson, B. Schelter, and H. P. Zaveri, “Seizure prediction—Ready for a new era,” Nature Rev. Neurol., vol. 14, no. 10, pp. 618–630, Oct. 2018.
.Y. Roy, H. Banville, I. Albuquerque, A. Gramfort, T. H. Falk, and J. Faubert, “Deep learning-based electroencephalography analysis: A systematic review,” J. Neural Eng., vol. 16, no. 5, Oct. 2019, Art. no. 051001.
. D. L. Schomer and F. L. da Silva, Niedermeyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 7th ed. Oxford, U.K.: Oxford Univ. Press, 2018.
. C. Mugruza-Vassallo, “Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks,” in Proc. IEEE 13th Int. Conf. Wearable Implant. Body Sensor Netw. (BSN), Jun. 2016, pp. 260–265.
. C. Mugruza-Vassallo and D. Potter, “Context dependence signature, stimulus properties and stimulus probability as predictors of ERP amplitude variability,” Frontiers Human Neurosci., vol. 13, p. 39, Feb. 2019.
.X. Zhang, X. Zhang, Q. Huang, and F. Chen, “A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning,” Frontiers Neurosci., vol. 18, Nov. 2024, Art. no. 1468967.
. W. T. Kerr, K. N. McFarlane, and G. Figueiredo Pucci, “The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials,” Frontiers Neurol., vol. 15, Jul. 2024, Art. no. 1425490.
. S. Wong, A. Simmons, J. Rivera-Villicana, S. Barnett, S. Sivathamboo, P. Perucca, Z. Ge, P. Kwan, L. Kuhlmann, R. Vasa, K. Mouzakis, and T. J. O’Brien, “EEG datasets for seizure detection and prediction—A review,” Epilepsia Open, vol. 8, no. 2, pp. 252–267, Jun. 2023.
.S. Shafiezadeh, G. Marco Duma, M. Pozza, and A. Testolin, “A systematic review of cross-patient approaches for EEG epileptic seizure prediction,” J. Neural Eng., vol. 21, no. 6, Dec. 2024, Art. no. 061004.
. J. S. Ong, S. N. Wong, A. Arulsamy, J. L. Watterson, and M. F. Shaikh, “Medical technology: A systematic review on medical devices utilized for epilepsy prediction and management,” Current Neuropharmacology, vol. 20, no. 5, pp. 950–964, May 2022.
. M. Saeidi, W. Karwowski, F. V. Farahani, K. Fiok, R. Taiar, P. A. Hancock, and A. Al-Juaid, “Neural decoding of EEG signals with machine learning: A systematic review,” Brain Sci., vol. 11, no. 11, p. 1525, Nov. 2021.
. M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” BMJ, vol. 372, p. 71, Mar. 2021.
. K. 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,” Comput. Biol. Med., vol. 99, pp. 24–37, Aug. 2018.
. I. Jemal, N. Mezghani, L. Abou-Abbas, and A. Mitiche, “An interpretable deep learning classifier for epileptic seizure prediction using EEG data,” IEEE Access, vol. 10, pp. 60141–60150, 2022.
.A. Ghosh and D. Dey, “Digital twin for EEG seizure prediction using time reassigned multisynchrosqueezing transform-based CNN-BiLSTM-attention mechanism model,” Biomed. Phys. Eng. Exp., vol. 11, no. 1, Jan. 2025, Art. no. 015034.
.Y. Gao, A. Liu, X. Cui, R. Qian, and X. Chen, “A general sample-weighted framework for epileptic seizure prediction,” Comput. Biol. Med., vol. 150, Nov. 2022, Art. no. 106169.
.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 J., vol. 21, no. 7, pp. 9377–9388, Apr. 2021.
. C. Cheng, B. You, Y. Liu, and Y. Dai, “Patient-specific method of sleep electroencephalography using wavelet packet transform and bi-LSTM for epileptic seizure prediction,” Biomed. Signal Process. Control, vol. 70, Sep. 2021, Art. no. 102963.
. M. Jia, W. Liu, J. Duan, L. Chen, C. L. P. Chen, Q. Wang, and Z. Zhou, “Efficient graph convolutional networks for seizure prediction using scalp EEG,” Frontiers Neurosci., vol. 16, Aug. 2022, Art. no. 967116.
. W. Qiao, X. Bi, L. Han, and Y. Zhang, “Epilepsy prediction and detection using attention-CssCDBN with dual-task learning,” Sensors, vol. 25, no. 1, p. 51, Dec. 2024.
. S. Hu, J. Liu, R. Yang, Y. Wang, A. Wang, K. Li, W. Liu, and C. Yang, “Exploring the applicability of transfer learning and feature engineering in epilepsy prediction using hybrid transformer model,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 31, pp. 1321–1332, 2023.
.A. Affes, A. Mdhaffar, C. Triki, M. Jmaiel, and B. Freisleben, “Personalized attention-based EEG channel selection for epileptic seizure prediction,” Expert Syst. Appl., vol. 206, Nov. 2022, Art. no. 117733.
. Q. Lan, B. Yao, and T. Qing, “Epileptic seizure prediction using convolutional neural networks and fusion features on scalp EEG signals,” IEICE Trans. Inf. Syst., vol. 106, no. 5, pp. 821–823, May 2023.
. Y. A. Saadoon, M. Khalil, and D. Battikh, “Predicting epileptic seizures using EfficientNet-B0 and SVMs: A deep learning methodology for EEG analysis,” Bioengineering, vol. 12, no. 2, p. 109, Jan. 2025.
.H. Daoud and M. A. Bayoumi, “Efficient epileptic seizure prediction based on deep learning,” IEEE Trans. Biomed. Circuits Syst., vol. 13, no. 5, pp. 804–813, Oct. 2019.
.X. Liu, C. Li, X. Lou, H. Kong, X. Li, Z. Li, and L. Zhong, “Epileptic seizure prediction based on EEG using pseudo-three-dimensional CNN,” Frontiers Neuroinform., vol. 18, Mar. 2024, Art. no. 1354436.
.S. Yuan, K. Yan, S. Wang, J.-X. Liu, and J. Wang, “EEG-based seizure prediction using hybrid DenseNet–ViT network with attention fusion,” Brain Sci., vol. 14, no. 8, p. 839, Aug. 2024.
. J. Yan, J. Li, H. Xu, Y. Yu, and T. Xu, “Seizure prediction based on transformer using scalp electroencephalogram,” Appl. Sci., vol. 12, no. 9, p. 4158, Apr. 2022.
.R. I. Halawa, S. M. Youssef, and M. N. Elagamy, “An efficient hybrid model for patient-independent seizure prediction using deep learning,” Appl. Sci., vol. 12, no. 11, p. 5516, May. 2022.
.Y. Zhang, T. Xiao, Z. Wang, H. Lv, S. Wang, H. Feng, S. Zhao, and Y. Zhao, “Hybrid network for patient-specific seizure prediction from EEG data,” Int. J. Neural Syst., vol. 33, no. 11, Nov. 2023, Art. no. 2350056.
. G. Wang, D. Wang, C. Du, K. Li, J. Zhang, Z. Liu, Y. Tao, M. Wang, Z. Cao, and X. Yan, “Seizure prediction using directed transfer function and convolution neural network on intracranial EEG,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 28, no. 12, pp. 2711–2720, Dec. 2020.
.M. Qureshi, E. A. Abdallah, and M. A. B. Sid-Ahmed, “Epileptic seizure prediction using machine learning techniques on EEG signals,” IEEE Access, vol. 9, pp. 139111–139122, 2021.
.T. D. K., P. B. G., and F. Xiong, “Epileptic seizure detection and prediction using stacked bidirectional long short term memory,” Pattern Recognit. Lett., vol. 128, pp. 529–535, Dec. 2019.
. C. Li, C. Shao, R. Song, G. Xu, X. Liu, R. Qian, and X. Chen, “Spatio-temporal MLP network for seizure prediction using EEG signals,” Measurement, vol. 206, Jan. 2023, Art. no. 112278.
.X. Lu, A. Wen, L. Sun, H. Wang, Y. Guo, and Y. Ren, “An epileptic seizure prediction method based on CBAM-3D CNN-LSTM model,” IEEE J. Transl. Eng. Health Med., vol. 11, pp. 417–423, 2023.
. X. Wang, C. Zhang, T. Kärkkäinen, Z. Chang, and F. Cong, “Channel increment strategy-based 1D convolutional neural networks for seizure prediction using intracranial EEG,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 31, pp. 316–325, 2023.
. K. Rasheed, J. Qadir, T. J. O’Brien, L. Kuhlmann, and A. Razi, “A generative model to synthesize EEG data for epileptic seizure prediction,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 29, pp. 2322–2332, 2021.
. S. Toraman, “Pretotal and intertrial recognition for epileptic seizure prediction using pre-trained 2D-CNN models,” Traitement Du Signal, vol. 37, no. 6, pp. 1045–1054, Dec. 2020.
. Z. Kuang, S. Liu, J. Zhao, L. Wang, and Y. Li, “Epilepsy EEG seizure prediction based on the combination of graph convolutional neural network combined with long- and short-term memory cell network,” Appl. Sci., vol. 14, no. 24, p. 11569, Dec. 2024.
.K. Singh and J. Malhotra, “Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features,” Complex Intell. Syst., vol. 8, no. 3, pp. 2405–2418, Jun. 2022.
. K. Singh and J. Malhotra, “Predicting epileptic seizures from EEG spectral band features using convolutional neural network,” Wireless Pers. Commun., vol. 125, no. 3, pp. 2667–2684, Aug. 2022.
.I. Ahmad, M. Zhu, Z. Liu, M. Shabaz, I. Ullah, M. C. F. Tong, A. Sambas, L. Men, Y. Chen, and S. Chen, “Multi-feature fusion-based convolutional neural networks for EEG epileptic seizure prediction in consumer Internet of Things,” IEEE Trans. Commun. Electron., vol. 70, no. 3, pp. 5631–5643, Aug. 2024.
.A. Esmaeilpour, S. S. Tabaresani, and A. Niazi, “Deep learning-based seizure prediction using EEG signals: A comparative analysis of classification methods on the CHB-MIT dataset,” Eng. Rep., vol. 6, no. 11, p. 12918, Nov. 2024.
. F. E. Ibrahim, H. M. Emara, W. El-Shafai, M. Elwekeil, M. Rihan, I. M. Eldokany, T. E. Taha, A. S. El-Fishawy, E. M. El-Rabaie, E. Abdellatef, and F. E. A. El-Samie, “Deep-learning-based seizure detection and prediction from electroencephalography signals,” Int. J. Numer. Methods Biomed. Eng., vol. 38, no. 6, p. 3573, Jun. 2022.
. M. H. Aslam, S. M. Usman, S. Khalid, A. Anwar, R. Alroobaea, S. Hussain, J. Almotiri, S. S. Ullah, and A. Nasir, “Classification of EEG signals for prediction of epileptic seizures,” Appl. Sci., vol. 12, no. 14, p. 7251, Jul. 2022.
. I. Assali, A. Ghazi Blaiech, A. Ben Abdallah, K. Ben Khalifa, M. Carrete, and M. Hedi Bedoui, “CNN-based classification of epileptic states for seizure prediction using combined temporal and spectral features,” Biomed. Signal Process. Control, vol. 82, Apr. 2023, Art. no. 104519.
.E. Abdellatef, H. M. Emara, M. R. Shoaib, F. E. Ibrahim, M. Elwekeil, W. El-Shafai, T. E. Taha, A. S. El-Fishawy, E.-S.-M. El-Rabaie, I. M. Eldokany, and F. E. Abd El-Samie, “Conventional diagnosis of EEG abnormalities with different classification techniques,” Med. Biol. Eng. Comput., vol. 61, no. 12, pp. 3363–3385, Dec. 2023.
. P. He, L. Wang, Y. Cui, R. Wang, and D. Wu, “Unsupervised feature learning based on autoencoder for epileptic seizures prediction,” Appl. Intell., vol. 53, no. 18, pp. 20766–20784, Sep. 2023.
.B. Feizbakhsh and H. Omranpour, “Cluster-based phase space density feature in multichannel scalp EEG for seizure prediction by deep learning,” Biomed. Signal Process. Control, vol. 86, Sep. 2023, Art. no. 105276.
. T. Meyer, C. Shultz, N. Dehak, L. Moro-Velázquez, and P. Irazoqui, “Time scale network: An efficient shallow neural network for time series data in biomedical applications,” IEEE J. Sel. Topics Signal Process., vol. 19, no. 1, pp. 129–139, Jan. 2025.
.B. C. Rebello, A. R. G. Ramirez, F. Heredia-Negron, and A. Roche-Lima, “A machine learning-based approach to epileptic seizure prediction using electroencephalographic signals,” J. Eng. Res., vol. 2, no. 8, pp. 2–9, May 2022.
.J. Liao, H. Li, C. Zhan, and F. Yang, “Construction of an epileptic seizure prediction model using a semi-supervised method of generative adversarial and long short term memory network combined with Stockwell transform,” J. Southern Med. Univ., vol. 43, no. 1, pp. 17–28, Jan. 2023.
. W. Feng, Y. Zhao, H. Peng, C. Nie, H. Lv, S. Wang, and H. Feng, “FusionXNet: Enhancing EEG-based seizure prediction with integrated convolutional and transformer architectures,” J. Neural Eng., vol. 22, no. 2, Apr. 2025, Art. no. 026067.
. A. A. Shaik Gadda, D. Vedantham, J. Thomas, Y. Rajamanickam, R. N. Menon, and J. F. Agastinose Ronickom, “Optimization of pre-ictal interval time period for epileptic seizure prediction using temporal and frequency features,” in Caring is Sharing–Exploiting the Value in Data for Health and Innovation, M. Hägglund, Ed., Amsterdam, The Netherlands: IOS Press, 2023.
. H. Ma, Y. Wu, Y. Tang, R. Chen, T. Xu, and W. Zhang, “Parallel dual-branch fusion network for epileptic seizure prediction,” Comput. Biol. Med., vol. 176, Jun. 2024, Art. no. 108565.
.U. Mohammad and F. Saeed, “Robustness of ML-based seizure prediction using noisy EEG data from limited channels,” in Proc. 20th Int. Conf. Distrib. Comput. Smart Syst. Internet Things (DCOSS-IoT), Apr. 2024, pp. 620–626.
.B. Cherouati, “Patient specific channel optimization using entropy and CNN deep learning for epileptic seizure prediction,” Przegląd Elektrotechniczny, vol. 1, no. 7, pp. 108–112, Jul. 2023.
.A. Bhattacharya, T. Baweja, and S. P. K. Karri, “Epileptic seizure prediction using deep transformer model,” Int. J. Neural Syst., vol. 32, no. 2, Feb. 2022, Art. no. 2150058.
.S. Muhammad Usman, S. Khalid, and S. Bashir, “A deep learning based ensemble learning method for epileptic seizure prediction,” Comput. Biol. Med., vol. 136, Sep. 2021, Art. no. 104710.
.Y. Gao, A. Liu, L. Wang, R. Qian, and X. Chen, “A self-interpretable deep learning model for seizure prediction using a multi-scale prototypical part network,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 31, pp. 1847–1856, 2023.
. N. Qi, Y. Piao, P. Yu, and B. Tan, “Predicting epileptic seizures based on EEG signals using spatial depth features of a 3D-2D hybrid CNN,” Med. Biol. Eng. Comput., vol. 61, no. 7, pp. 1845–1856, Jul. 2023.
.D. Liang, A. Liu, C. Li, J. Liu, and X. Chen, “A novel consistency-based training strategy for seizure prediction,” J. Neurosci. Methods, vol. 372, Apr. 2022, Art. no. 109557.
.C. Shao, C. Li, R. Song, X. Liu, R. Qian, and X. Chen, “Machine unlearning for seizure prediction,” IEEE Trans. Cognit. Develop. Syst., vol. 16, no. 6, pp. 1969–1981, Dec. 2024.
.L. Lu, F. Zhang, Y. Wu, S. Ma, X. Zhang, and G. Ni, “A multi-frame network model for predicting seizure based on sEEG and iEEG data,” Frontiers Comput. Neurosci., vol. 16, Nov. 2022, Art. no. 1059565.
.S. Shi and W. Liu, “B2-ViT Net: Broad vision transformer network with broad attention for seizure prediction,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 32, pp. 178–188, 2024.
.C.-L. Liu, B. Xiao, W.-H. Hsaio, and V. S. Tseng, “Epileptic seizure prediction with multi-view convolutional neural networks,” IEEE Access, vol. 7, pp. 170352–170361, 2019.
.Y. Zhao, C. Li, X. Liu, R. Qian, R. Song, and X. Chen, “Patient-specific seizure prediction via adder network and supervised contrastive learning,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 30, pp. 1536–1547, 2022.
.C. Li, Y. Zhao, R. Song, X. Liu, R. Qian, and X. Chen, “Patient-specific seizure prediction from electroencephalogram signal via multichannel feedback capsule network,” IEEE Trans. Cognit. Develop. Syst., vol. 15, no. 3, pp. 1360–1370, Sep. 2023.
.G. Yang, D. Long, K. Wang, S. Xia, and J. Zou, “Epileptic seizure prediction method based on transition network data augmentation and fuzzy granular recurrence plot,” Biomed. Signal Process. Control, vol. 107, Sep. 2025, Art. no. 107837.
.S. Liu, J. Wang, S. Li, and L. Cai, “Multi-dimensional hybrid bilinear CNN-LSTM models for epileptic seizure detection and prediction using EEG signals,” J. Neural Eng., vol. 21, no. 6, Dec. 2024, Art. no. 066045.
. I. Andrade, C. Teixeira, and M. Pinto, “On the performance of seizure prediction machine learning methods across different databases: The sample and alarm-based perspectives,” Frontiers Neurosci., vol. 18, Jul. 2024, Art. no. 1417748.
. R. Hussein, S. Lee, R. Ward, and M. J. McKeown, “Semi-dilated convolutional neural networks for epileptic seizure prediction,” Neural Netw., vol. 139, pp. 212–222, Jul. 2021.
. S. Zhao, J. Yang, and M. Sawan, “Energy-efficient neural network for epileptic seizure prediction,” IEEE Trans. Biomed. Eng., vol. 69, no. 1, pp. 401–411, Jan. 2022.
.J. S. Ra, T. Li, and Y. Li, “Epileptic seizure prediction based on synchroextracting transform and sparse representation,” IEEE Access, vol. 12, pp. 187684–187695, 2024.
. J. Qin, Z. Liu, J. Zhuang, and F. Liu, “Dual-modality transformer with time series imaging for robust epileptic seizure prediction,” Appl. Sci., vol. 15, no. 3, p. 1538, Feb. 2025.
. L. Guo, T. Yu, S. Zhao, X. Li, X. Liao, and Y. Li, “CLEP: Contrastive learning for epileptic seizure prediction using a spatio-temporal-spectral network,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 31, pp. 3915–3926, 2023.
. S. Shafiezadeh, G. M. Duma, G. Mento, A. Danieli, L. Antoniazzi, F. Del Popolo Cristaldi, P. Bonanni, and A. Testolin, “Calibrating deep learning classifiers for patient-independent electroencephalogram seizure forecasting,” Sensors, vol. 24, no. 9, p. 2863, Apr. 2024.
.S. Shafiezadeh, G. M. Duma, G. Mento, A. Danieli, L. Antoniazzi, F. Del Popolo Cristaldi, P. Bonanni, and A. Testolin, “Methodological issues in evaluating machine learning models for EEG seizure prediction: Good cross-validation accuracy does not guarantee generalization to new patients,” Appl. Sci., vol. 13, no. 7, p. 4262, Mar. 2023.
.F. Asharindavida, M. Shamim Hossain, A. Thacham, H. Khammari, I. Ahmed, F. Alraddady, and M. Masud, “A forecasting tool for prediction of epileptic seizures using a machine learning approach,” Concurrency Comput., Pract. Exper., vol. 32, no. 1, p. 5111, Jan. 2020.
.H. Chen, T. Ji, X. Zhan, X. Liu, G. Yu, W. Wang, Y. Jiang, and X.-H. Zhou, “An explainable statistical method for seizure prediction using brain functional connectivity from EEG,” Comput. Intell. Neurosci., vol. 2022, pp. 1–8, Dec. 2022.
.S. Rao, M. Liu, Y. Huang, H. Yang, J. Liang, J. Lu, Y. Niu, and B. Wang, “Anchoring temporal convolutional networks for epileptic seizure prediction,” J. Neural Eng., vol. 21, no. 6, Dec. 2024, Art. no. 066008.
. P. Peng, L. Xie, and H. Wei, “A deep Fourier neural network for seizure prediction using convolutional neural network and ratios of spectral power,” Int. J. Neural Syst., vol. 31, no. 8, Aug. 2021, Art. no. 2150022.
.N. D. Truong, A. D. Nguyen, L. Kuhlmann, M. R. Bonyadi, J. Yang, S. Ippolito, and O. Kavehei, “Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram,” Neural Netw., vol. 105, pp. 104–111, Sep. 2018.
.M. A. Ozdemir, O. K. Cura, and A. Akan, “Epileptic EEG classification by using time-frequency images for deep learning,” Int. J. Neural Syst., vol. 31, no. 8, Aug. 2021, Art. no. 2150026.
.C. Lucasius, V. Grigorovsky, H. Nariai, A. S. Galanopoulou, J. Gursky, S. L. Moshé, and B. L. Bardakjian, “Biomimetic deep learning networks with applications to epileptic spasms and seizure prediction,” IEEE Trans. Biomed. Eng., vol. 71, no. 3, pp. 1056–1067, Mar. 2024.
.H. Khan, L. Marcuse, M. Fields, K. Swann, and B. Yener, “Focal onset seizure prediction using convolutional networks,” IEEE Trans. Biomed. Eng., vol. 65, no. 9, pp. 2109–2118, Sep. 2018.
. X. Dong, L. He, H. Li, Z. Liu, W. Shang, and W. Zhou, “Deep learning based automatic seizure prediction with EEG time-frequency representation,” Biomed. Signal Process. Control, vol. 95, Sep. 2024, Art. no. 106447.
.H. Li, J. Liao, H. Wang, C. A. Zhan, and F. Yang, “EEG power spectra parameterization and adaptive channel selection towards semi-supervised seizure prediction,” Comput. Biol. Med., vol. 175, Jun. 2024, Art. no. 108510.
.T. Dissanayake, T. Fernando, S. Denman, S. Sridharan, and C. Fookes, “Geometric deep learning for subject independent epileptic seizure prediction using scalp EEG signals,” IEEE J. Biomed. Health Informat., vol. 26, no. 2, pp. 527–538, Feb. 2022.
.X. Jiang, X. Liu, Y. Liu, Q. Wang, B. Li, and L. Zhang, “Epileptic seizures detection and the analysis of optimal seizure prediction horizon based on frequency and phase analysis,” Frontiers Neurosci., vol. 17, May 2023, Art. no. 1191683.
.B. Kapoor, B. Nagpal, P. K. Jain, A. Abraham, and L. A. Gabralla, “Epileptic seizure prediction based on hybrid seek optimization tuned ensemble classifier using EEG signals,” Sensors, vol. 23, no. 1, p. 423, Dec. 2022.
.I. Jemal, L. Abou-Abbas, K. Henni, A. Mitiche, and N. Mezghani, “Domain adaptation for EEG-based, cross-subject epileptic seizure prediction,” Frontiers Neuroinform., vol. 18, Feb. 2024, Art. no. 1303380.
.J. Xiang, Y. Li, X. Wu, Y. Dong, X. Wen, and Y. Niu, “Synchronization-based graph spatio-temporal attention network for seizure prediction,” Sci. Rep., vol. 15, no. 1, p. 4080, Feb. 2025.
.A. Pandey, S. K. Singh, S. S. Udmale, and K. K. Shukla, “An intelligent optimized deep learning model to achieve early prediction of epileptic seizures,” Biomed. Signal Process. Control, vol. 84, Jul. 2023, Art. no. 104798.
.Z. Georgis-Yap, M. R. Popovic, and S. S. Khan, “Supervised and unsupervised deep learning approaches for EEG seizure prediction,” J. Healthcare Informat. Res., vol. 8, no. 2, pp. 286–312, Jun. 2024.
.L. Xia, R. Wang, H. Ye, B. Jiang, G. Li, C. Ma, and Z. Gao, “Hybrid LSTM–transformer model for the prediction of epileptic seizure using scalp EEG,” IEEE Sensors J., vol. 24, no. 13, pp. 21123–21131, Jul. 2024.
.Z. Yu, L. Albera, R. Le Bouquin Jeannes, A. Kachenoura, A. Karfoul, C. Yang, and H. Shu, “Epileptic seizure prediction using deep neural networks via transfer learning and multi-feature fusion,” Int. J. Neural Syst., vol. 32, no. 7, Jul. 2022, Art. no. 2250032.
.K. Khalil, A. Kumar, and M. Bayoumi, “Accurate hardware predictor for epileptic seizure,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 72, no. 5, pp. 1–14, May 2025.
.Y. Shi, S. Tang, Y. Li, Z. He, S. Tang, R. Wang, W. Zheng, Z. Chen, and Y. Zhou, “Continual learning for seizure prediction via memory projection strategy,” Comput. Biol. Med., vol. 181, Oct. 2024, Art. no. 109028.
.X. Ding, W. Nie, X. Liu, X. Wang, and Q. Yuan, “Compact convolutional neural network with multi-headed attention mechanism for seizure prediction,” Int. J. Neural Syst., vol. 33, no. 3, Mar. 2023, Art. no. 2350014.
.[108] J. West, Z. Dasht Bozorgi, J. Herron, H. J. Chizeck, J. D. Chambers, and L. Li, “Machine learning seizure prediction: One problematic but accepted practice,” J. Neural Eng., vol. 20, no. 1, Feb. 2023, Art. no. 016008.
.M. Sun, F. Wang, T. Min, T. Zang, and Y. Wang, “Prediction for high risk clinical symptoms of epilepsy based on deep learning algorithm,” IEEE Access, vol. 6, pp. 77596–77605, 2018.
.V. R. Karuppiah Ramachandran, H. J. Alblas, D. V. Le, and N. Meratnia, “Towards an online seizure advisory system—An adaptive seizure prediction framework using active learning heuristics,” Sensors, vol. 18, no. 6, p. 1698, May 2018.
.O. Ouichka, A. Echtioui, and H. Hamam, “Deep learning models for predicting epileptic seizures using iEEG signals,” Electronics, vol. 11, no. 4, p. 605, Feb. 2022.
.R. Chen and K. K. Parhi, “Seizure prediction using convolutional neural networks and sequence transformer networks,” in Proc. 43rd Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), Nov. 2021, pp. 6483–6486.
.X. Wu, T. Zhang, L. Zhang, and L. Qiao, “Epileptic seizure prediction using successive variational mode decomposition and transformers deep learning network,” Frontiers Neurosci., vol. 16, Sep. 2022, Art. no. 982541.
.J. Müller, H. Yang, M. Eberlein, G. Leonhardt, O. Uckermann, L. Kuhlmann, and R. Tetzlaff, “Coherent false seizure prediction in epilepsy, coincidence or providence?” Clin. Neurophysiol., vol. 133, pp. 157–164, Jan. 2022.
.G. Segal, N. Keidar, R. M. Lotan, Y. Romano, M. Herskovitz, and Y. Yaniv, “Utilizing risk-controlling prediction calibration to reduce false alarm rates in epileptic seizure prediction,” Frontiers Neurosci., vol. 17, Sep. 2023, Art. no. 1184990.
.P. Detti, G. Vatti, and G. Zabalo Manrique de Lara, “EEG synchronization analysis for seizure prediction: A study on data of noninvasive recordings,” Processes, vol. 8, no. 7, p. 846, Jul. 2020.
.C.-Y. Yang, P.-C. Chen, and W.-C. Huang, “Cross-domain transfer of EEG to EEG or ECG learning for CNN classification models,” Sensors, vol. 23, no. 5, p. 2458, Feb. 2023.
.E. D. Pontes, M. Pinto, F. Lopes, and C. Teixeira, “Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction,” Sci. Rep., vol. 14, no. 1, p. 8204, Apr. 2024.
.N. H. Hassoon, M. H. Ali, M. M. Jaber, S. K. Abd, A. S. Abosinnee, and Z. H. Kareem, “Prediction and analysis of chronic epilepsy using electroencephalographic signals on medical Internet of Things platform,” Intell. Data Anal., vol. 27, pp. 65–82, Nov. 2023.
.C. Meisel and K. A. Bailey, “Identifying signal-dependent information about the preictal state: A comparison across ECoG, EEG and EKG using deep learning,” EBioMedicine, vol. 45, pp. 422–431, Jul. 2019.
.J. Batista, M. F. Pinto, M. Tavares, F. Lopes, A. Oliveira, and C. Teixeera, “EEG epilepsy seizure prediction: The post-processing stage as a chronology,” Sci. Rep., vol. 14, no. 1, p. 407, Jan. 2024.
.M. Pinto, T. Coelho, A. Leal, F. Lopes, A. Dourado, P. Martins, and C. Teixeira, “Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm,” Sci. Rep., vol. 12, no. 1, p. 4420, Mar. 2022.
.M. F. Pinto, A. Leal, F. Lopes, A. Dourado, P. Martins, and C. A. Teixeira, “A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction,” Sci. Rep., vol. 11, no. 1, p. 3415, Feb. 2021.
.B. Sarvi Zargar, M. R. Karami Mollaei, F. Ebrahimi, and J. Rasekhi, “Generalizable epileptic seizures prediction based on deep transfer learning,” Cognit. Neurodynamics, vol. 17, no. 1, pp. 119–131, Feb. 2023.
.Q. Lian, Y. Qi, G. Pan, and Y. Wang, “Learning graph in graph convolutional neural networks for robust seizure prediction,” J. Neural Eng., vol. 17, no. 3, Jun. 2020, Art. no. 035004.
.M. H. Abdullah, H. Ibrahim, J. M. Abdullah, and M. Z. Abdullah, “Improved seizure prediction using discrete hidden Markov model and Wilks’ lambda analysis of the electroencephalographic signals,” Current Med. Imag. Rev., vol. 14, no. 3, pp. 407–415, May 2018.
.[127] Y. M. Massoud, A. A. Ahmad, M. Abdelzaher, L. Kuhlmann, and M. A. Abd El Ghany, “Hardware implementation of deep neural network for seizure prediction,” AEU - Int. J. Electron. Commun., vol. 172, Dec. 2023, Art. no. 154961.
.S. Lu, L. Liu, J. Li, J. Chambers, M. J. Cook, and D. B. Grayden, “Leveraging channel coherence in long-term iEEG data for seizure prediction,” IEEE J. Biomed. Health Informat., vol. 29, no. 8, pp. 5541–5548, Aug. 2025.
.C. Reuben et al., “Ensembling crowdsourced seizure prediction algorithms using long-term human intracranial EEG,” Epilepsia, vol. 61, no. 2, pp. e7–e12, Feb. 2020.
.M. Ravindranath, K. S. Candan, M. L. Sapino, and B. Appavu, “MMA: Metadata supported multi-variate attention for onset detection and prediction,” Data Mining Knowl. Discovery, vol. 38, no. 4, pp. 1545–1588, Jul. 2024.
.L. Cousyn, R. B. Messaoud, K. Lehongre, V. Frazzini, V. Lambrecq, C. Adam, B. Mathon, V. Navarro, and M. Chavez, “Daily resting-state intracranial EEG connectivity for seizure risk forecasts,” Epilepsia, vol. 64, no. 2, pp. e23–e29, Feb. 2023.
.P. Parani, U. Mohammad, and F. Saeed, “Lightweight transformer exhibits comparable performance to LLMs for seizure prediction: A case for light-weight models for EEG data,” in Proc. IEEE Int. Conf. Big Data (BigData), Dec. 2024, pp. 4941–4945.
.M.-P. Hosseini, T. X. Tran, D. Pompili, K. Elisevich, and H. Soltanian-Zadeh, “Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing,” Artif. Intell. Med., vol. 104, Apr. 2020, Art. no. 101813.
.D. Zambrana-Vinaroz, J. M. Vicente-Samper, J. Manrique-Cordoba, and J. M. Sabater-Navarro, “Wearable epileptic seizure prediction system based on machine learning techniques using ECG, PPG and EEG signals,” Sensors, vol. 22, no. 23, p. 9372, Dec. 2022.
.X. Wei, L. Zhou, Z. Zhang, Z. Chen, and Y. Zhou, “Early prediction of epileptic seizures using a long-term recurrent convolutional network,” J. Neurosci. Methods, vol. 327, Nov. 2019, Art. no. 108395.
.P. F. Viana, T. P. Attia, M. Nasseri, J. Dunn-Henriksen, A. Biondi, J. S. Winston, I. P. Martins, E. S. Nurse, M. Dümpelmann, A. Schulze-Bonhage, D. R. Freestone, T. W. Kjaer, M. P. Richardson, and B. H. Brinkmann, “Seizure forecasting using minimally invasive, ultra-long-term subcutaneous electroencephalography: Individualized intrapatient models,” Epilepsia, vol. 64, no. 54, pp. S124–S133, Dec. 2023.
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