{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T15:30:43Z","timestamp":1781537443441,"version":"3.54.5"},"reference-count":16,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Epileptic seizure prediction remains a critical challenge in neuroscience and healthcare, with profound implications for enhancing patient safety and quality of life. In this paper, we introduce a novel seizure prediction method that leverages electroencephalogram (EEG) data, combining discrete wavelet transform (DWT)-based time\u2013frequency analysis, advanced feature extraction, and deep learning using Fourier neural networks (FNNs). The proposed approach extracts essential features from EEG signals\u2014including entropy, power, frequency, and amplitude\u2014to effectively capture the brain\u2019s complex and nonstationary dynamics. We measure the method based on the broadly used CHB-MIT EEG dataset, ensuring direct comparability with prior research. Experimental results demonstrate that our DWT-FS-FNN model achieves a prediction accuracy of 98.96 with a zero false positive rate, outperforming several state-of-the-art methods. These findings underscore the potential of integrating advanced signal processing and deep learning methods for reliable, real-time seizure prediction. Future work will focus on optimizing the model for real-world clinical deployment and expanding it to incorporate multimodal physiological data, further enhancing its applicability in clinical practice.<\/jats:p>","DOI":"10.3390\/a18080492","type":"journal-article","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T08:33:06Z","timestamp":1754555586000},"page":"492","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Epileptic Seizure Prediction Using a Combination of Deep Learning, Time\u2013Frequency Fusion Methods, and Discrete Wavelet Analysis"],"prefix":"10.3390","volume":"18","author":[{"given":"Hadi Sadeghi","family":"Khansari","sequence":"first","affiliation":[{"name":"Department of Mathematics and Computer Science, Arak Branch, Islamic Azad University, Arak 3836119131, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6954-3896","authenticated-orcid":false,"given":"Mostafa","family":"Abbaszadeh","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics, Faculty of Mathematics and Computer Sciences, Amirkabir University of Technology (Tehran Polytechnic), No. 424, Hafez Ave., Tehran 15914, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gholamreza Heidary","family":"Joonaghany","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Arak Branch, Islamic Azad University, Arak 3836119131, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamidreza","family":"Mohagerani","sequence":"additional","affiliation":[{"name":"Quantum Technologies Research Center, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fardin","family":"Faraji","sequence":"additional","affiliation":[{"name":"Department of Neurology, School of Medicine, Arak University of Medical Sciences, Arak 3813873449, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1109\/TCDS.2022.3212019","article-title":"Patient-specific seizure prediction from electroencephalogram signal via multichannel feedback capsule network","volume":"15","author":"Li","year":"2022","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"110796","DOI":"10.1016\/j.chaos.2021.110796","article-title":"Predicting seizure onset based on time-frequency analysis of eeg signals","volume":"145","author":"Tamanna","year":"2021","journal-title":"Chaos Solitons Fract."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Divya, P., Devi, B., Prabakar, S., Porkumaran, K., Kannan, R., Nor, N., and Elamvazuthi, I. (2021, January 13\u201315). Identification of epileptic seizures using autoencoders and convolutional neural network. Proceedings of the 2020 8th International Conference on Intelligent and Advanced Systems (ICIAS), Kuching, Malaysia.","DOI":"10.1109\/ICIAS49414.2021.9642570"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, X., and Li, H. (2022, January 4\u20136). Patient-specific seizure prediction from scalp eeg using vision transformer. Proceedings of the 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China.","DOI":"10.1109\/ITOEC53115.2022.9734546"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1109\/TNSRE.2023.3321414","article-title":"Dynamic multi-graph convolution based channelweighted transformer feature fusion network for epileptic seizure prediction","volume":"31","author":"Wang","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2239","DOI":"10.1007\/s10586-023-04059-x","article-title":"Hybrid cuckoo finch optimisation based machine learning classifier for seizure prediction using eeg signals in iot network","volume":"27","author":"Kapoor","year":"2024","journal-title":"Clust. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.1109\/TNSRE.2022.3180155","article-title":"Patient-specific seizure prediction via adder network and supervised contrastive learning","volume":"30","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2150058","DOI":"10.1142\/S0129065721500581","article-title":"Epileptic seizure prediction using deep transformer model","volume":"32","author":"Bhattacharya","year":"2022","journal-title":"Int. J. Neural Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Torkey, H., Hashish, S., Souissi, S., Hemdan, E.E.-D., and Sayed, A. (2025). Seizure detection in medical iot: Hybrid cnn-lstm-gru model with data balancing and xai integration. Algorithms, 18.","DOI":"10.3390\/a18020077"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kalitzin, S. (2024). Topological reinforcement adaptive algorithm (toreada) application to the alerting of convulsive seizures and validation with monte carlo numerical simulations. Algorithms, 17.","DOI":"10.20944\/preprints202410.1257.v1"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, Z., Li, J., Xia, Y., Feng, P., and Feng, F. (2021). Variation trends of fractal dimension in epileptic eeg signals. Algorithms, 14.","DOI":"10.3390\/a14110316"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106401","DOI":"10.1016\/j.engappai.2023.106401","article-title":"EEG-based seizure prediction via hybrid vision transformer and data uncertainty learning","volume":"123","author":"Deng","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1186\/s12967-024-05678-7","article-title":"Epileptic seizure prediction via multidimensional transformer and recurrent neural network fusion","volume":"22","author":"Pan","year":"2024","journal-title":"J. Transl. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3422","DOI":"10.1109\/TII.2020.2995598","article-title":"Low-Complexity MIMO-FBMC Sparse Channel Parameter Estimation for Industrial Big Data Communications","volume":"17","author":"Wang","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Xue, X., Wen, F., and Wang, H. (2025, January 20\u201322). Two-Dimensional Estimation Method for Bistatic MIMO Radar Assisted by Intelligent Reflecting Surfaces. Proceedings of the 2025 IEEE 34th Wireless and Optical Communications Conference (WOCC), Taipa, Macao.","DOI":"10.1109\/WOCC63563.2025.11082209"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mansouri, A., Singh, S.P., and Sayood, K. (2019). Online eeg seizure detection and localization. Algorithms, 12.","DOI":"10.3390\/a12090176"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/8\/492\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:25:05Z","timestamp":1760034305000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/8\/492"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"references-count":16,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["a18080492"],"URL":"https:\/\/doi.org\/10.3390\/a18080492","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}