{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:17:02Z","timestamp":1776442622008,"version":"3.51.2"},"reference-count":281,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100006769","name":"Russian Science Foundation","doi-asserted-by":"publisher","award":["25-11-00021"],"award-info":[{"award-number":["25-11-00021"]}],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"DOI":"10.1007\/s10916-025-02274-0","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T06:56:11Z","timestamp":1760943371000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Automated Video-EEG Analysis in Epilepsy Studies: A Narrative Review of Advances and Challenges"],"prefix":"10.1007","volume":"49","author":[{"given":"Valerii A.","family":"Zuev","sequence":"first","affiliation":[]},{"given":"Elena G.","family":"Salmagambetova","sequence":"additional","affiliation":[]},{"given":"Stepan N.","family":"Djakov","sequence":"additional","affiliation":[]},{"given":"Lev V.","family":"Utkin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,20]]},"reference":[{"key":"2274_CR1","doi-asserted-by":"publisher","unstructured":"Beghi, E., The epidemiology of epilepsy. Neuroepidemiology 54(2):185\u2013191, 2020. https:\/\/doi.org\/10.1159\/000503831. Accessed 05 Feb 2025.","DOI":"10.1159\/000503831"},{"key":"2274_CR2","doi-asserted-by":"publisher","unstructured":"Zorg\u00f6r, G., Eren, F., G\u00fcl, G., and G\u00fcl, Z. B., A comparision of video EEG monitoring and routine EEG for diagnosis of epilepsy. Arch. Epilepsy 28(2):85\u201388, 2022. https:\/\/doi.org\/10.54614\/ArchEpilepsy.2022.38233. Accessed 27 Mar 2025.","DOI":"10.54614\/ArchEpilepsy.2022.38233"},{"key":"2274_CR3","doi-asserted-by":"publisher","unstructured":"Amin, U., and Benbadis, S. R., The role of EEG in the erroneous diagnosis of epilepsy. J. Clin. Neurophysiol. 36(4):294, 2019. https:\/\/doi.org\/10.1097\/WNP.0000000000000572. Accessed 27 Mar 2025.","DOI":"10.1097\/WNP.0000000000000572"},{"key":"2274_CR4","doi-asserted-by":"publisher","unstructured":"Xu, Y., Yang, J., Ming, W., Wang, S., and Sawan, M., Shorter latency of real-time epileptic seizure detection via probabilistic prediction. Expert Syst. Appl. 236, 2024. https:\/\/doi.org\/10.1016\/j.eswa.2023.121359. Accessed 03 Mar 2025.","DOI":"10.1016\/j.eswa.2023.121359"},{"key":"2274_CR5","doi-asserted-by":"publisher","unstructured":"Blinov, D. V., Epilepsy syndromes: the 2022 ILAE definition and classification. Epilepsy Paroxysmal Cond. 14(2):101\u2013182, 2022. https:\/\/doi.org\/10.17749\/2077-8333\/epi.par.con.2022.123. Accessed 20 Jan 2025","DOI":"10.17749\/2077-8333\/epi.par.con.2022.123"},{"key":"2274_CR6","doi-asserted-by":"publisher","unstructured":"Wirrell, E. C., Nabbout, R., Scheffer, I. E., Alsaadi, T., Bogacz, A., French, J. A., Hirsch, E., Jain, S., Kaneko, S., Riney, K., Samia, P., Snead, O. C., Somerville, E., Specchio, N., Trinka, E., Zuberi, S. M., Balestrini, S., Wiebe, S., Cross, J. H., Perucca, E., Mosh\u00e9, S. L., and Tinuper, P., Methodology for classification and definition of epilepsy syndromes with list of syndromes: Report of the ILAE task force on nosology and definitions. Epilepsia 63(6):1333\u20131348, 2022. https:\/\/doi.org\/10.1111\/epi.17237. Accessed 31 Mar 2025.","DOI":"10.1111\/epi.17237"},{"key":"2274_CR7","doi-asserted-by":"publisher","unstructured":"Fisher, R. S., Cross, J. H., French, J. A., Higurashi, N., Hirsch, E., Jansen, F. E., Lagae, L., Mosh\u00e9, S. L., Peltola, J., Roulet\u00a0Perez, E., Scheffer, I. E., and Zuberi, S. M., Operational classification of seizure types by the international league against epilepsy: Position paper of the ILAE commission for classification and terminology. Epilepsia 58(4):522\u2013530, 2017. https:\/\/doi.org\/10.1111\/epi.13670. Accessed 20 Jan 2025.","DOI":"10.1111\/epi.13670"},{"key":"2274_CR8","doi-asserted-by":"publisher","unstructured":"Kane, N., Acharya, J., Beniczky, S., Caboclo, L., Finnigan, S., Kaplan, P. W., Shibasaki, H., Pressler, R., and Putten, M. J. A. M., A revised glossary of terms most commonly used by clinical electroencephalographers and updated proposal for the report format of the EEG findings. revision 2017 2, 170\u2013185 https:\/\/doi.org\/10.1016\/j.cnp.2017.07.002. Accessed 2025-09-19.","DOI":"10.1016\/j.cnp.2017.07.002"},{"key":"2274_CR9","doi-asserted-by":"publisher","unstructured":"Wilson, S. B., and Emerson, R., Spike detection: A review and comparison of algorithms. Clin. Neurophysiol. 113(12):1873\u20131881, 2002. https:\/\/doi.org\/10.1016\/S1388-2457(02)00297-3. Accessed 05 Apr 2025.","DOI":"10.1016\/S1388-2457(02)00297-3"},{"key":"2274_CR10","doi-asserted-by":"publisher","unstructured":"Koren, J., Hafner, S., Feigl, M., and Baumgartner, C., Systematic analysis and comparison of commercial seizure-detection software. Epilepsia 62(2):426\u2013438, 2021. https:\/\/doi.org\/10.1111\/epi.16812. Accessed 20 Jan 2025.","DOI":"10.1111\/epi.16812"},{"key":"2274_CR11","doi-asserted-by":"publisher","unstructured":"Scheuer, M. L., Bagic, A., and Wilson, S. B., Spike detection: Inter-reader agreement and a statistical turing test on a large data set. Clin. Neurophysiol. 128(1):243\u2013250, 2017. https:\/\/doi.org\/10.1016\/j.clinph.2016.11.005. Accessed 20 Jan 2025.","DOI":"10.1016\/j.clinph.2016.11.005"},{"key":"2274_CR12","doi-asserted-by":"publisher","unstructured":"Reus, E. E. M., Cox, F. M. E., Dijk, J. G., and Visser, G. H., Automated spike detection: Which software package? Seizure 95:33\u201337, 2022. https:\/\/doi.org\/10.1016\/j.seizure.2021.12.012. Accessed 19 Sept 2025.","DOI":"10.1016\/j.seizure.2021.12.012"},{"key":"2274_CR13","doi-asserted-by":"publisher","unstructured":"Lee, K., Jeong, H., Kim, S., Yang, D., Kang, H. -C., and Choi, E., Real-time seizure detection using EEG: A comprehensive comparison of recent approaches under a realistic setting. arXiv, 2022. https:\/\/doi.org\/10.48550\/arXiv.2201.08780. arXiv:2201.08780 Accessed 31 Mar 2025.","DOI":"10.48550\/arXiv.2201.08780"},{"key":"2274_CR14","doi-asserted-by":"publisher","unstructured":"Mallick, S., and Baths, V., Novel deep learning framework for detection of epileptic seizures using EEG signals. Front. Comput. Neurosci. 18, 2022. https:\/\/doi.org\/10.3389\/fncom.2024.1340251. Accessed 20 Jan 2025.","DOI":"10.3389\/fncom.2024.1340251"},{"key":"2274_CR15","doi-asserted-by":"publisher","unstructured":"Furui, A., Onishi, R., Takeuchi, A., Akiyama, T., and Tsuji, T., Non-gaussianity detection of EEG signals based on a multivariate scale mixture model for diagnosis of epileptic seizures. IEEE Trans. Biomed. Eng. 68(2):515\u2013525, 2020. https:\/\/doi.org\/10.1109\/TBME.2020.3006246. Accessed 20 Mar 2025.","DOI":"10.1109\/TBME.2020.3006246"},{"key":"2274_CR16","doi-asserted-by":"publisher","unstructured":"Furui, A., Onishi, R., Akiyama, T., and Tsuji, T., Epileptic seizure detection using a recurrent neural network with temporal features derived from a scale mixture EEG model. IEEE Access 12:162814\u2013162824, 2024. https:\/\/doi.org\/10.1109\/ACCESS.2024.3487637. Accessed 13 Feb 2025.","DOI":"10.1109\/ACCESS.2024.3487637"},{"key":"2274_CR17","doi-asserted-by":"publisher","unstructured":"Ren, Z., Han, X., and Wang, B., The performance evaluation of the state-of-the-art EEG-based seizure prediction models. Front. Neurol. 13, 2022. https:\/\/doi.org\/10.3389\/fneur.2022.1016224. Accessed 20 Jan 2025.","DOI":"10.3389\/fneur.2022.1016224"},{"key":"2274_CR18","doi-asserted-by":"publisher","unstructured":"Mironov, M. B., Abramov, M. O., Kondratenko, V. V., Vafin, I. R., Smirnov, S. Y., Vaganov, S. E., and Ivanov, A. A., Artificial intelligence applied for the diagnosis of absence epilepsy with simultaneously tested patient\u2019s consciousness level in ictal event. Epilepsy Paroxysmal Cond. 16(1):8\u201317, 2024. https:\/\/doi.org\/10.17749\/2077-8333\/epi.par.con.2024.178. Accessed 20 Jan 2025.","DOI":"10.17749\/2077-8333\/epi.par.con.2024.178"},{"key":"2274_CR19","doi-asserted-by":"publisher","unstructured":"Wan, Z., Li, M., Liu, S., Huang, J., Tan, H., and Duan, W., EEGformer: A transformer\u2013based brain activity classification method using EEG signal. Front. Neurosci. 17, 2023. https:\/\/doi.org\/10.3389\/fnins.2023.1148855. Accessed 20 Jan 2025.","DOI":"10.3389\/fnins.2023.1148855"},{"key":"2274_CR20","doi-asserted-by":"publisher","unstructured":"Chen, Y., Ren, K., Song, K., Wang, Y., Wang, Y., Li, D., and Qiu, L., EEGFormer: Towards transferable and interpretable large-scale eeg foundation model. arXiv, 2024. https:\/\/doi.org\/10.48550\/arXiv.2401.10278. arXiv:2401.10278. Accessed 20 Jan 2025.","DOI":"10.48550\/arXiv.2401.10278"},{"key":"2274_CR21","doi-asserted-by":"crossref","unstructured":"Callanga, C., Ares, J. M., Becbec, J., Elladora, S., Gabucan, J., Gaylan, E., Narca, M., Quibido, J., Quimat, R. M., Taneo, J. K., and Sanchez, J. M. P., Empowering doctoral students: The role of publish or perish software in enhancing systematic reviews in science education. Internet Reference Services Quarterly. Routledge, 2024. Accessed 11 Feb 2025.","DOI":"10.1080\/10875301.2024.2384021"},{"key":"2274_CR22","doi-asserted-by":"publisher","unstructured":"Wong, S., Simmons, A., Rivera-Villicana, J., Barnett, S., Sivathamboo, S., Perucca, P., Kwan, P., Kuhlmann, L., Vasa, R., and O\u2019Brien, T. J., EEG based automated seizure detection \u2013 a survey of medical professionals. Epilepsy Behav. 149:109518, 2023. https:\/\/doi.org\/10.1016\/j.yebeh.2023.109518. Accessed 20 Aug 2025.","DOI":"10.1016\/j.yebeh.2023.109518"},{"key":"2274_CR23","doi-asserted-by":"publisher","unstructured":"Biondi, A., Dursun, E., Viana, P. F., Laiou, P., and Richardson, M. P., New wearable and portable EEG modalities in epilepsy: The views of hospital-based healthcare professionals. Epilepsy Behav. 159:109990, 2024. https:\/\/doi.org\/10.1016\/j.yebeh.2024.109990. Accessed 20 Aug 2025.","DOI":"10.1016\/j.yebeh.2024.109990"},{"key":"2274_CR24","doi-asserted-by":"publisher","unstructured":"Larsen, S. A., Terney, D., \u00d8sterkjerhuus, T., Merinder, T. V., Annala, K., Knight, A., and Beniczky, S., Automated detection of nocturnal motor seizures using an audio-video system. Brain Behav. 12(9), 2022. https:\/\/doi.org\/10.1002\/brb3.2737. Accessed 31 Mar 2025.","DOI":"10.1002\/brb3.2737"},{"key":"2274_CR25","doi-asserted-by":"publisher","unstructured":"Atwood, A. C., and Drees, C. N., Seizure detection devices. Neurol. Clin. Pract. 11(5):367\u2013371, 2021. https:\/\/doi.org\/10.1212\/CPJ.0000000000001044. Wolters Kluwer. Accessed 31 Mar 2025.","DOI":"10.1212\/CPJ.0000000000001044"},{"key":"2274_CR26","doi-asserted-by":"publisher","unstructured":"Yang, Y., Sarkis, R. A., Atrache, R. E., Loddenkemper, T., and Meisel, C., Video-based detection of generalized tonic-clonic seizures using deep learning. IEEE J. Biomed. Health Inform. 25(8):2997\u20133008, 2021. https:\/\/doi.org\/10.1109\/JBHI.2021.3049649. Accessed 20 Jan 2025.","DOI":"10.1109\/JBHI.2021.3049649"},{"key":"2274_CR27","doi-asserted-by":"publisher","unstructured":"Venkatesh, A., and Sajini, S., An epileptic seizure detection system based on mediapipe and deep learning. In: 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), vol. 2, pp. 1\u20136 (2024). https:\/\/doi.org\/10.1109\/IATMSI60426.2024.10503295. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10503295 Accessed 03 Mar 2025.","DOI":"10.1109\/IATMSI60426.2024.10503295"},{"key":"2274_CR28","doi-asserted-by":"publisher","unstructured":"Xu, Y., Wang, J., Chen, Y. -H., Yang, J., Ming, W., Wang, S., and Sawan, M., VSViG: Real-time video-based seizure detection via skeleton-based spatiotemporal ViG. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2311.14775. arXiv:2311.14775. Accessed 20 Jan 2025.","DOI":"10.48550\/arXiv.2311.14775"},{"key":"2274_CR29","doi-asserted-by":"publisher","unstructured":"Ahmedt-Aristizabal, D., Armin, M. A., Hayder, Z., Garcia-Cairasco, N., Petersson, L., Fookes, C., Denman, S., and McGonigal, A., Deep learning approaches for seizure video analysis: A review. Epilepsy Behav. 154, 2024. https:\/\/doi.org\/10.1016\/j.yebeh.2024.109735. Accessed 20 Jan 2025.","DOI":"10.1016\/j.yebeh.2024.109735"},{"key":"2274_CR30","doi-asserted-by":"publisher","unstructured":"Andersson, F. K., Gauffin, H., Lindehammar, H., and Vigren, P., Video-based automatic seizure detection in pharmacoresistant epilepsy: A prospective exploratory study. Epilepsy Behav. 161, 2024. https:\/\/doi.org\/10.1016\/j.yebeh.2024.110118. Accessed 20 Jan 2025.","DOI":"10.1016\/j.yebeh.2024.110118"},{"key":"2274_CR31","doi-asserted-by":"publisher","unstructured":"Aghaei, H., Kiani, M. M., and Aghajan, H., Epileptic seizure detection based on video and EEG recordings, pp. 1\u20134. IEEE. https:\/\/doi.org\/10.1109\/BIOCAS.2017.8325156. http:\/\/ieeexplore.ieee.org\/document\/8325156\/. Accessed 20 Jan 2025.","DOI":"10.1109\/BIOCAS.2017.8325156"},{"key":"2274_CR32","doi-asserted-by":"publisher","unstructured":"Wu, Y., Hu, D., Jiang, T., Gao, F., and Cao, J., Multi-modal signal based childhood rolandic epilepsy detection. In: Sun, F., Hu, D., Wermter, S., Yang, L., Liu, H., and Fang, B. (Eds.), Cognitive Systems and Information Processing: 6th International Conference, ICCSIP 2021, Suzhou, China, November 20\u201321, 2021, Revised Selected Papers 6. Communications in Computer and Information Science, pp. 495\u2013510. Springer, 2022. https:\/\/doi.org\/10.1007\/978-981-16-9247-5. https:\/\/link.springer.com\/10.1007\/978-981-16-9247-5. Accessed 20 Jan 2025.","DOI":"10.1007\/978-981-16-9247-5"},{"key":"2274_CR33","doi-asserted-by":"publisher","unstructured":"Cao, J., Fang, Y., Cui, X., Zheng, R., Jiang, T., and Gao, F., Synchronized video and EEG based childhood epilepsy seizure detection. IEEE Trans. Emerg. Top. Comput. Intell. 8(6):3742\u20133753, 2024. https:\/\/doi.org\/10.1109\/TETCI.2024.3372387. Accessed 20 Jan 2025.","DOI":"10.1109\/TETCI.2024.3372387"},{"key":"2274_CR34","doi-asserted-by":"publisher","unstructured":"Lin, N., Gao, W., Li, L., Chen, J., Liang, Z., Yuan, G., Sun, H., Liu, Q., Chen, J., Jin, L., Huang, Y., Zhou, X., Zhang, S., Hu, P., Dai, C., He, H., Dong, Y., Cui, L., and Lu, Q., vEpiNet: A multimodal interictal epileptiform discharge detection method based on video and electroencephalogram data. Neural Netw. 175, 2024. https:\/\/doi.org\/10.1016\/j.neunet.2024.106319. Accessed 31 Mar 2025.","DOI":"10.1016\/j.neunet.2024.106319"},{"key":"2274_CR35","doi-asserted-by":"publisher","unstructured":"Yin, K., Shin, H.-B., Li, D., and Lee, S.-W., EEG-based multimodal representation learning for emotion recognition. arXiv, 2025. https:\/\/doi.org\/10.48550\/arXiv.2411.00822. arXiv:2411.00822. Accessed 20 Jan 2025.","DOI":"10.48550\/arXiv.2411.00822"},{"key":"2274_CR36","doi-asserted-by":"publisher","unstructured":"Regalia, G., Onorati, F., Lai, M., Caborni, C., and Picard, R. W., Multimodal wrist-worn devices for seizure detection and advancing research: Focus on the empatica wristbands. Epilepsy Res. 153:79\u201382, 2019. https:\/\/doi.org\/10.1016\/j.eplepsyres.2019.02.007. Accessed 31 Mar 2025.","DOI":"10.1016\/j.eplepsyres.2019.02.007"},{"key":"2274_CR37","doi-asserted-by":"publisher","unstructured":"Istiaq\u00a0Ahsan, M. N., Kertesz, C., Mesaros, A., Heittola, T., Knight, A., and Virtanen, T., Audio-based epileptic seizure detection. In: 2019 27th European Signal Processing Conference (EUSIPCO), pp. 1\u20135, 2019. https:\/\/doi.org\/10.23919\/EUSIPCO.2019.8902840","DOI":"10.23919\/EUSIPCO.2019.8902840"},{"key":"2274_CR38","doi-asserted-by":"publisher","unstructured":"Fern\u00e1ndez-Mart\u00edn, R., Feys, O., Juven\u00e9, E., Aeby, A., Urbain, C., De\u00a0Ti\u00e8ge, X., and Wens, V., Towards the automated detection of interictal epileptiform discharges with magnetoencephalography. J. Neurosci. Methods 403, 2024. https:\/\/doi.org\/10.1016\/j.jneumeth.2023.110052. Accessed 31 Mar 2025.","DOI":"10.1016\/j.jneumeth.2023.110052"},{"key":"2274_CR39","doi-asserted-by":"publisher","unstructured":"Stevenson, N. J., Tapani, K., Lauronen, L., and Vanhatalo, S., A dataset of neonatal EEG recordings with seizure annotations. Sci. Data 6(1):190039, 2019. https:\/\/doi.org\/10.1038\/sdata.2019.39. Nature Publishing Group. Accessed 31 Mar 2025.","DOI":"10.1038\/sdata.2019.39"},{"key":"2274_CR40","doi-asserted-by":"publisher","unstructured":"Wong, S., Simmons, A., Rivera-Villicana, J., Barnett, S., Sivathamboo, S., Perucca, P., Ge, Z., Kwan, P., Kuhlmann, L., Vasa, R., Mouzakis, K., and O\u2019Brien, T. J., Eeg datasets for seizure detection and prediction \u2013 a review. Epilepsia Open 8(2):252\u2013267, 2023. https:\/\/doi.org\/10.1002\/epi4.12704. Accessed 20 Jan 2025","DOI":"10.1002\/epi4.12704"},{"key":"2274_CR41","doi-asserted-by":"publisher","unstructured":"Miron, G., Halimeh, M., Tietze, S., Holtkamp, M., and Meisel, C., Detection of epileptic spasms using foundational AI and smartphone videos: A novel diagnostic approach for a rare neurological disorder. Neurology. https:\/\/doi.org\/10.1101\/2024.10.28.24316130. http:\/\/medrxiv.org\/lookup\/doi\/10.1101\/2024.10.28.24316130. Accessed 20 Jan 2025.","DOI":"10.1101\/2024.10.28.24316130"},{"key":"2274_CR42","doi-asserted-by":"crossref","unstructured":"Kirpichenko, S., Utkin, L., Konstantinov, A., and Muliukha, V., BENK: The Beran estimator with neural Kernels for estimating the heterogeneous treatment effect. Algorithms 17(1). Accessed 20 Jan 2025.","DOI":"10.3390\/a17010040"},{"key":"2274_CR43","doi-asserted-by":"publisher","unstructured":"Konstantinov, A. V., Kirpichenko, S. R., and Utkin, L. V., Generating survival interpretable trajectories and data. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2402.12331. arXiv:2402.12331. Accessed 20 Jan 2025.","DOI":"10.48550\/arXiv.2402.12331"},{"key":"2274_CR44","doi-asserted-by":"publisher","unstructured":"K\u00fcnzel, S. R., Sekhon, J. S., Bickel, P. J., and Yu, B., Metalearners for estimating heterogeneous treatment effects using machine learning. Proc. Nat. Acad. Sci. 116(10):4156\u20134165, 2019. https:\/\/doi.org\/10.1073\/pnas.1804597116. Proceedings of the National Academy of Sciences. Accessed 05 Apr 2025.","DOI":"10.1073\/pnas.1804597116"},{"key":"2274_CR45","doi-asserted-by":"publisher","unstructured":"Ma, J., Wang, S., Raubertas, R., and Svetnik, V., Statistical methods to estimate treatment effects from multichannel electroencephalography (EEG) data in clinical trials. J. Neurosci. Methods 190(2):248\u2013257, 2010. https:\/\/doi.org\/10.1016\/j.jneumeth.2010.05.013. Accessed 04 Apr 2025.","DOI":"10.1016\/j.jneumeth.2010.05.013"},{"key":"2274_CR46","doi-asserted-by":"publisher","unstructured":"Watts, D., Pulice, R. F., Reilly, J., Brunoni, A. R., Kapczinski, F., and Passos, I. C., Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis. Transl. Psychiatry 12(1):1\u201318, 2022. https:\/\/doi.org\/10.1038\/s41398-022-02064-z. Nature Publishing Group. Accessed 05 Apr 2025.","DOI":"10.1038\/s41398-022-02064-z"},{"key":"2274_CR47","doi-asserted-by":"publisher","unstructured":"Ojanen, P., Zabihi, M., Knight, A., Roivainen, R., Lamusuo, S., and Peltola, J., Feasibility of video\/audio monitoring in the analysis of motion and treatment effects on night-time seizures \u2013 interventional study. Epilepsy Res. 184, 2022. https:\/\/doi.org\/10.1016\/j.eplepsyres.2022.106949. Accessed 05 Apr 2025.","DOI":"10.1016\/j.eplepsyres.2022.106949"},{"key":"2274_CR48","doi-asserted-by":"publisher","unstructured":"Poeta, E., Ciravegna, G., Pastor, E., Cerquitelli, T., and Baralis, E., Concept-based explainable artificial intelligence: A survey. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2312.12936. arXiv:2312.12936. Accessed 05 Apr 2025.","DOI":"10.48550\/arXiv.2312.12936"},{"key":"2274_CR49","doi-asserted-by":"publisher","unstructured":"Dumaev, R. I., Molodyakov, S. A., and Utkin, L. V., Concept-based explainable malignancy scoring on pulmonary nodules in CT images. https:\/\/doi.org\/10.48550\/arXiv.2405.17483. arXiv:2405.17483. Accessed 20 Jan 2025.","DOI":"10.48550\/arXiv.2405.17483"},{"key":"2274_CR50","doi-asserted-by":"publisher","unstructured":"Konstantinov, A. V., and Utkin, L. V., Incorporating expert rules into neural networks in the framework of concept-based learning. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2402.14726. arXiv:2402.14726. Accessed 20 Jan 2025.","DOI":"10.48550\/arXiv.2402.14726"},{"key":"2274_CR51","doi-asserted-by":"publisher","unstructured":"Utkin, L. V., Konstantinov, A. V., and Kirpichenko, S. R., FI-CBL: A probabilistic method for concept-based learning with expert rules. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2406.19897. arXiv:2406.19897. Accessed 20 Jan 2025.","DOI":"10.48550\/arXiv.2406.19897"},{"key":"2274_CR52","unstructured":"Knispel, F., Interpretable machine learning in EEG abnormality detection. PhD thesis, RWTH Aachen University, Germany, 2022."},{"key":"2274_CR53","doi-asserted-by":"publisher","unstructured":"Madsen, A. G., Lehn-Schi\u00f8ler, W. T., J\u00f3nsd\u00f3ttir, \u00c1., Arnard\u00f3ttir, B., and Hansen, L. K., Concept-based explainability for an EEG transformer model, pp. 1\u20136. https:\/\/doi.org\/10.1109\/MLSP55844.2023.10285992. ISSN: 2161-0371. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10285992. Accessed 27 Mar 2025.","DOI":"10.1109\/MLSP55844.2023.10285992"},{"key":"2274_CR54","doi-asserted-by":"publisher","unstructured":"Brenner, A., Knispel, F., Fischer, F.P., Rossmanith, P., Weber, Y., Koch, H., R\u00f6hrig, R., Varghese, J., and Kutafina, E., Concept-based AI interpretability in physiological time-series data: Example of abnormality detection in electroencephalography. Comput. Methods Prog. Biomed. 257, 2024. https:\/\/doi.org\/10.1016\/j.cmpb.2024.108448. Accessed 05 Apr 2025.","DOI":"10.1016\/j.cmpb.2024.108448"},{"key":"2274_CR55","doi-asserted-by":"publisher","unstructured":"Jeyakumar, J. V., Dickens, L., Garcia, L., Cheng, Y. -H., Echavarria, D. R., Noor, J., Russo, A., Kaplan, L., Blasch, E., and Srivastava, M., Automatic concept extraction for concept bottleneck-based video classification. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2206.10129. arXiv:2206.10129. Accessed 28 Jan 2025.","DOI":"10.48550\/arXiv.2206.10129"},{"key":"2274_CR56","doi-asserted-by":"publisher","unstructured":"Xu, Y., Yang, J., and Sawan, M., Multichannel synthetic preictal EEG signals to enhance the prediction of epileptic seizures. IEEE Trans. Biomed. Eng. 69(11):3516\u20133525, 2022. https:\/\/doi.org\/10.1109\/TBME.2022.3171982. Accessed 11 Mar 2025.","DOI":"10.1109\/TBME.2022.3171982"},{"key":"2274_CR57","doi-asserted-by":"publisher","unstructured":"Wei, L., and Mooney, C., Pediatric and adolescent seizure detection: A machine learning approach exploring the influence of age and sex in electroencephalogram analysis. BioMedInformatics 4(1), 2024. https:\/\/doi.org\/10.3390\/biomedinformatics4010044. Number: 1 Publisher: Multidisciplinary Digital Publishing Institute. Accessed 03 Feb 2025.","DOI":"10.3390\/biomedinformatics4010044"},{"key":"2274_CR58","doi-asserted-by":"publisher","unstructured":"Ahmed, S., Momin, M., Ren, J., Lee, H., AlMahmood, B., Huang, L. -P., Pandiyan, A., Veeramuthu, L., Kuo, C. -C., and Zhou, T., Stick-and-play bioadhesive hairlike electrodes for chronic EEG recording on human. Npj Biomed. Innov. 2(1):1\u201310, 2025. https:\/\/doi.org\/10.1038\/s44385-025-00009-x. Publisher: Nature Publishing Group. Accessed 31 Mar 2025.","DOI":"10.1038\/s44385-025-00009-x"},{"key":"2274_CR59","doi-asserted-by":"publisher","unstructured":"Ku\u010dikien\u0117, D., Rajkumar, R., Timpte, K., Heckelmann, J., Neuner, I., Weber, Y., and Wolking, S., EEG microstates show different features in focal epilepsy and psychogenic nonepileptic seizures. https:\/\/doi.org\/10.1111\/epi.17897. Accessed 04 Feb 2025.","DOI":"10.1111\/epi.17897"},{"key":"2274_CR60","doi-asserted-by":"publisher","unstructured":"Ito, Y., Nakatsukasa, H., Toyoma, Y., Nagata, S., and Oguni, H., Differentiating non-epileptic seizures from epileptic seizures in glut1 deficiency syndrome. Dev. Med. Child Neurol. 66(11):1466\u20131475, 2024. https:\/\/doi.org\/10.1111\/dmcn.15942. Accessed 07 Apr 2025.","DOI":"10.1111\/dmcn.15942"},{"key":"2274_CR61","doi-asserted-by":"publisher","unstructured":"Fussner, S., Boyne, A., Han, A., Nakhleh, L. A., and Haneef, Z., Differentiating epileptic and psychogenic non-epileptic seizures using machine learning analysis of EEG plot images. Sensors 24(9), 2024. https:\/\/doi.org\/10.3390\/s24092823. Number: 9 Publisher: Multidisciplinary Digital Publishing Institute. Accessed 04 Feb 2025.","DOI":"10.3390\/s24092823"},{"key":"2274_CR62","doi-asserted-by":"publisher","unstructured":"Shama, D. M., and Venkataraman, A., Uncertainty-aware Bayesian deep learning with noisy training labels for epileptic seizure detection. In: Sudre, C. H., Mehta, R., Ouyang, C., Qin, C., Rakic, M., and Wells, W. M. (Eds.), Uncertainty for safe utilization of machine learning in medical imaging, pp. 3\u201313. Springer, Cham, 2025. https:\/\/doi.org\/10.1007\/978-3-031-73158-7_1","DOI":"10.1007\/978-3-031-73158-7_1"},{"key":"2274_CR63","doi-asserted-by":"publisher","unstructured":"Wang, Z., Li, S., and Wu, D., Canine EEG helps human: Cross-species and cross-modality epileptic seizure detection via multi-space alignment. [eess], 2025. https:\/\/doi.org\/10.48550\/arXiv.2412.17842. arXiv:2412.17842. Accessed 03 Mar 2025.","DOI":"10.48550\/arXiv.2412.17842"},{"key":"2274_CR64","unstructured":"Han, J., SSSVE_Dataset. IEEE. https:\/\/ieee-dataport.org\/documents\/sssvedataset. Accessed 05 Mar 2025."},{"key":"2274_CR65","doi-asserted-by":"publisher","unstructured":"Herrera-Fortin, T., Bou\u00a0Assi, E., Gagnon, M. -P., and Nguyen, D. K., Seizure detection devices: A survey of needs and preferences of patients and caregivers. Epilepsy Behav. 114:107607, 2021. https:\/\/doi.org\/10.1016\/j.yebeh.2020.107607. Accessed 20 Aug 2025.","DOI":"10.1016\/j.yebeh.2020.107607"},{"key":"2274_CR66","doi-asserted-by":"publisher","unstructured":"Goel, A., Seri, S., Agrawal, S., Kumar, R., Sudarsanam, A., Carr, B., Lawley, A., Macpherson, L., Oates, A. J., Williams, H., Walsh, A. R., Lo, W. B., and Pepper, J., The utility of Multicentre Epilepsy Lesion Detection (MELD) algorithm in identifying epileptic activity and predicting seizure freedom in MRI lesion-negative paediatric patients. Epilepsy Rese. 206:107429, 2024. https:\/\/doi.org\/10.1016\/j.eplepsyres.2024.107429. Accessed 13 Feb 2025.","DOI":"10.1016\/j.eplepsyres.2024.107429"},{"key":"2274_CR67","doi-asserted-by":"publisher","unstructured":"Dharani, V., and Lakshmanan, L., Integrative approach for automated epileptic seizure detection: EEG-MRI hybrid model utilizing deep learning techniques. In: 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI), pp. 829\u2013834, 2024. https:\/\/doi.org\/10.1109\/ICDICI62993.2024.10810967. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10810967. Accessed 13 Feb 2025.","DOI":"10.1109\/ICDICI62993.2024.10810967"},{"key":"2274_CR68","doi-asserted-by":"publisher","unstructured":"Du, Y., Ren, Y., Wong, N., and Ngai, E. C. H., Hyperdimensional computing with multiscale local binary patterns for scalp EEG-based epileptic seizure detection. IEEE Internet Things J. 11(15):26046\u201326061, 2024. https:\/\/doi.org\/10.1109\/JIOT.2024.3395496. Accessed 13 Feb 2025.","DOI":"10.1109\/JIOT.2024.3395496"},{"key":"2274_CR69","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Xiao, M., Ji, T., Jiang, Y., Lin, T., Zhou, X., and Lin, Z., Efficient and generalizable cross-patient epileptic seizure detection through a spiking neural network. Front. Neurosci. 17, 2024. https:\/\/doi.org\/10.3389\/fnins.2023.1303564. Publisher: Frontiers. Accessed 03 Mar 2025.","DOI":"10.3389\/fnins.2023.1303564"},{"key":"2274_CR70","doi-asserted-by":"publisher","unstructured":"Gallou, O., Bartels, J., Ghosh, S., Schindler, K., Sarnthein, J., and Indiveri, G., Online epileptic seizure detection in long-term iEEG recordings using mixed-signal neuromorphic circuits. In: 2024 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1\u20135, 2024. https:\/\/doi.org\/10.1109\/BioCAS61083.2024.10798236. ISSN: 2766-4465. https:\/\/ieeexplore.ieee.org\/document\/10798236. Accessed 13 Feb 2025.","DOI":"10.1109\/BioCAS61083.2024.10798236"},{"key":"2274_CR71","doi-asserted-by":"publisher","unstructured":"Breakspear, M., Dynamic models of large-scale brain activity. Nat. Neurosci. 20(3):340\u2013352, 2017. https:\/\/doi.org\/10.1038\/nn.4497. Publisher: Nature Publishing Group. Accessed 18 Mar 2025.","DOI":"10.1038\/nn.4497"},{"key":"2274_CR72","doi-asserted-by":"publisher","unstructured":"Liu, Y., Soto-Breceda, A., Cook, M. J., Karoly, P., Grayden, D. B., Kuhlmann, L., Freestone, D. R., and Schmidt, D., Forecasting events in multidimensional electroencephalographic brain data: Application to epileptic seizure prediction. In: 2024 27th International Conference on Information Fusion (FUSION), pp. 1\u20138, 2024. https:\/\/doi.org\/10.23919\/FUSION59988.2024.10706283. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10706283. Accessed 03 Mar 2025.","DOI":"10.23919\/FUSION59988.2024.10706283"},{"key":"2274_CR73","doi-asserted-by":"publisher","unstructured":"Li, C., Gan, Z., Yang, Z., Yang, J., Li, L., Wang, L., and Gao, J., Multimodal foundation models: From specialists to general-purpose assistants. Found. Trends\u00ae Comput. Graph. Vision 16(1-2):1\u2013214, 2024. https:\/\/doi.org\/10.1561\/0600000110. Accessed 31 Mar 2025","DOI":"10.1561\/0600000110"},{"key":"2274_CR74","doi-asserted-by":"publisher","unstructured":"Han, J., Zhang, S., Men, A., and Chen, Q., Cross-modal contrastive hashing retrieval for infrared video and EEG. Sensors 22(22), 2022. https:\/\/doi.org\/10.3390\/s22228804. Number: 22 Publisher: Multidisciplinary Digital Publishing Institute. Accessed 06 Mar 2025.","DOI":"10.3390\/s22228804"},{"key":"2274_CR75","doi-asserted-by":"publisher","unstructured":"Han, J., Zhang, S., Men, A., Liu, Y., Yao, Z., Yan, Y., and Chen, Q., Seeing your sleep stage: Cross-modal distillation from EEG to infrared video. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2208.05814. arXiv:2208.05814. Accessed 06 Mar 2025.","DOI":"10.48550\/arXiv.2208.05814"},{"key":"2274_CR76","doi-asserted-by":"publisher","unstructured":"Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., and Elger, C. E., Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys. Rev. E 64(6), 2001. https:\/\/doi.org\/10.1103\/PhysRevE.64.061907. Accessed 29 Jan 2025.","DOI":"10.1103\/PhysRevE.64.061907"},{"key":"2274_CR77","doi-asserted-by":"publisher","unstructured":"Abdallah, T., Jrad, N., Hajjar, S. E., Abdallah, F., Humeau-Heurtier, A., Howayek, E. E., and Van\u00a0Bogaert, P., Deep clustering for epileptic seizure detection. IEEE Trans. Biomed. Eng. 72(2):480\u2013492, 2025. https:\/\/doi.org\/10.1109\/TBME.2024.3458177. Accessed 12 Feb 2025.","DOI":"10.1109\/TBME.2024.3458177"},{"key":"2274_CR78","doi-asserted-by":"publisher","unstructured":"Wu, Q., and Fokoue, E., Epileptic seizure recognition data set. https:\/\/doi.org\/10.13140\/RG.2.2.33336.03843","DOI":"10.13140\/RG.2.2.33336.03843"},{"key":"2274_CR79","doi-asserted-by":"publisher","unstructured":"Winterhalder, M., Maiwald, T., Voss, H. U., Aschenbrenner-Scheibe, R., Timmer, J., and Schulze-Bonhage, A., The seizure prediction characteristic: A general framework to assess and compare seizure prediction methods. Epilepsy Behav. 4(3):318\u2013325, 2003. https:\/\/doi.org\/10.1016\/S1525-5050(03)00105-7. Accessed 11 Mar 2025.","DOI":"10.1016\/S1525-5050(03)00105-7"},{"key":"2274_CR80","doi-asserted-by":"publisher","unstructured":"Maiwald, T., Winterhalder, M., Aschenbrenner-Scheibe, R., Voss, H. U., Schulze-Bonhage, A., and Timmer, J., Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic. Phys. D: Nonlinear Phenom. 194(3):357\u2013368, 2004. https:\/\/doi.org\/10.1016\/j.physd.2004.02.013. Accessed 11 Mar 2025.","DOI":"10.1016\/j.physd.2004.02.013"},{"key":"2274_CR81","unstructured":"Frei, M., Osorio, I., and Frei, A., The FHS publicly available ECoG database. https:\/\/aesnet.org\/abstractslisting\/the-fhs-publicly-available-epilepsy-ecog-database. Accessed 11 Mar 2025."},{"key":"2274_CR82","doi-asserted-by":"publisher","unstructured":"Duun-Henriksen, J., Kjaer, T. W., Madsen, R. E., Remvig, L. S., Thomsen, C. E., and Sorensen, H. B. D., Channel selection for automatic seizure detection. Clin. Neurophysiol. 123(1):84\u201392, 2012. https:\/\/doi.org\/10.1016\/j.clinph.2011.06.001. Accessed 11 Mar 2025.","DOI":"10.1016\/j.clinph.2011.06.001"},{"key":"2274_CR83","unstructured":"Shoeb, A. H., Application of machine learning to epileptic seizure onset detection and treatment. Thesis, Massachusetts Institute of Technology. https:\/\/dspace.mit.edu\/handle\/1721.1\/54669 Accessed 16 Sept 2025."},{"key":"2274_CR84","doi-asserted-by":"publisher","unstructured":"Zwoli\u0144ski, P., Roszkowski, M., \u017bygierewicz, J., Haufe, S., Nolte, G., and Durka, P. J., Open database of epileptic EEG with MRI and postoperational assessment of foci\u2013a real world verification for the EEG inverse solutions. Neuroinformatics 8(4):285\u2013299, 2010. https:\/\/doi.org\/10.1007\/s12021-010-9086-6. Accessed 20 Mar 2025.","DOI":"10.1007\/s12021-010-9086-6"},{"key":"2274_CR85","doi-asserted-by":"publisher","unstructured":"Andrzejak, R. G., Schindler, K., and Rummel, C., Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev. E 86(4), 2012. https:\/\/doi.org\/10.1103\/PhysRevE.86.046206. Publisher: American Physical Society. Accessed 13 Mar 2025.","DOI":"10.1103\/PhysRevE.86.046206"},{"key":"2274_CR86","doi-asserted-by":"publisher","unstructured":"Ihle, M., Feldwish-Drentrup, H., Teixeira, C., Witon, A., Schelter, B., Timmer, J., and Schulze-Bonhage, A., EPILEPSIAE \u2013 a European epilepsy database. Comput. Methods Prog. Biomed. 106(3):127\u2013138, 2012. https:\/\/doi.org\/10.1016\/j.cmpb.2010.08.011. Publisher: Elsevier. Accessed 11 Mar 2025.","DOI":"10.1016\/j.cmpb.2010.08.011"},{"key":"2274_CR87","doi-asserted-by":"publisher","unstructured":"Selvaraj, T. G., Ramasamy, B., Jeyaraj, S. J., and Suviseshamuthu, E. S., EEG database of seizure disorders for experts and application developers. Clin. EEG Neurosci. 2014. https:\/\/doi.org\/10.1177\/1550059413500960. Publisher: SAGE PublicationsSage CA: Los Angeles, CA. Accessed 10 Mar 2025.","DOI":"10.1177\/1550059413500960"},{"key":"2274_CR88","doi-asserted-by":"publisher","unstructured":"Brinkmann, B. H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S. C., Chen, M., Tieng, Q. M., He, J., Mu\u00f1oz-Almaraz, F. J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E. E., Litt, B., and Worrell, G. A., Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139(6):1713\u20131722, 2016. https:\/\/doi.org\/10.1093\/brain\/aww045. Accessed 11 Mar 2025.","DOI":"10.1093\/brain\/aww045"},{"key":"2274_CR89","doi-asserted-by":"publisher","unstructured":"Temko, A., Sarkar, A., and Lightbody, G., Detection of seizures in intracranial EEG: UPenn and mayo clinic\u2019s seizure detection challenge, pp. 6582\u20136585. https:\/\/doi.org\/10.1109\/EMBC.2015.7319901. ISSN: 1558-4615. https:\/\/ieeexplore.ieee.org\/document\/7319901. Accessed 27 Mar 2025.","DOI":"10.1109\/EMBC.2015.7319901"},{"key":"2274_CR90","doi-asserted-by":"publisher","unstructured":"Obeid, I., and Picone, J., The temple university hospital EEG data corpus. Front. Neurosci. 10, 2016. https:\/\/doi.org\/10.3389\/fnins.2016.00196. Publisher: Frontiers. Accessed 31 Mar 2025.","DOI":"10.3389\/fnins.2016.00196"},{"key":"2274_CR91","doi-asserted-by":"publisher","unstructured":"Swami, P., Panigrahi, B. K., Nara, S., Bhatia, M., and Gandhi, T. K., EEG epilepsy datasets. https:\/\/doi.org\/10.13140\/RG.2.2.14280.32006","DOI":"10.13140\/RG.2.2.14280.32006"},{"key":"2274_CR92","doi-asserted-by":"publisher","unstructured":"Swami, P., Gandhi, T. K., Panigrahi, B. K., Tripathi, M., and Anand, S., A novel robust diagnostic model to detect seizures in electroencephalography. Expert Syst. Appl. 56:116\u2013130, 2016. https:\/\/doi.org\/10.1016\/j.eswa.2016.02.040. Accessed 11 Mar 2025.","DOI":"10.1016\/j.eswa.2016.02.040"},{"key":"2274_CR93","doi-asserted-by":"publisher","unstructured":"Cook, M. J., O\u2019Brien, T. J., Berkovic, S. F., Murphy, M., Morokoff, A., Fabinyi, G., D\u2019Souza, W., Yerra, R., Archer, J., Litewka, L., Hosking, S., Lightfoot, P., Ruedebusch, V., Sheffield, W. D., Snyder, D., Leyde, K., and Himes, D., Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. Lancet Neurol. 12(6):563\u2013571, 2013. https:\/\/doi.org\/10.1016\/S1474-4422(13)70075-9. Accessed 18 Sept 2025.","DOI":"10.1016\/S1474-4422(13)70075-9"},{"key":"2274_CR94","doi-asserted-by":"publisher","unstructured":"Kuhlmann, L., Karoly, P., Freestone, D. R., Brinkmann, B. H., Temko, A., Barachant, A., Li, F., Titericz, G. Jr., Lang, B. W., Lavery, D., Roman, K., Broadhead, D., Dobson, S., Jones, G., Tang, Q., Ivanenko, I., Panichev, O., Proix, T., N\u00e1hl\u00edk, M., Grunberg, D. B., Reuben, C., Worrell, G., Litt, B., Liley, D. T. J., Grayden, D. B., and Cook, M. J., Epilepsyecosystem.org: crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG. Brain 141(9):2619\u20132630, 2018. https:\/\/doi.org\/10.1093\/brain\/awy210. Accessed 19 Mar 2025.","DOI":"10.1093\/brain\/awy210"},{"key":"2274_CR95","doi-asserted-by":"publisher","unstructured":"Burrello, A., Schindler, K., Benini, L., and Rahimi, A., One-shot learning for iEEG seizure detection using end-to-end binary operations: Local binary patterns with hyperdimensional computing, pp. 1\u20134. https:\/\/doi.org\/10.1109\/BIOCAS.2018.8584751. ISSN: 2163-4025. https:\/\/ieeexplore.ieee.org\/abstract\/document\/8584751. Accessed 11 Mar 2025.","DOI":"10.1109\/BIOCAS.2018.8584751"},{"key":"2274_CR96","doi-asserted-by":"publisher","unstructured":"Detti, P., Vatti, G., and Zabalo Manrique De\u00a0Lara, G., EEG synchronization analysis for seizure prediction: A study on data of noninvasive recordings. Processes 8(7), 2020. https:\/\/doi.org\/10.3390\/pr8070846. Accessed Jan 29 2025.","DOI":"10.3390\/pr8070846"},{"key":"2274_CR97","doi-asserted-by":"publisher","unstructured":"Khati, R., Epileptic seizure detection using machine learning techniques. https:\/\/doi.org\/10.17632\/k2mzn5zvyg.1. Publisher: Mendeley Data.","DOI":"10.17632\/k2mzn5zvyg.1"},{"key":"2274_CR98","doi-asserted-by":"publisher","unstructured":"Kural, M. A., Duez, L., Sejer\u00a0Hansen, V., Larsson, P. G., Rampp, S., Schulz, R., Tankisi, H., Wennberg, R., Bibby, B. M., Scherg, M., and Beniczky, S., Criteria for defining interictal epileptiform discharges in EEG. Neurology 94(20), 2020. https:\/\/doi.org\/10.1212\/WNL.0000000000009439. Publisher: Wolters Kluwer. Accessed 02 Apr 2025.","DOI":"10.1212\/WNL.0000000000009439"},{"key":"2274_CR99","doi-asserted-by":"publisher","unstructured":"Nasreddine, W., Epileptic EEG dataset 1. https:\/\/doi.org\/10.17632\/5pc2j46cbc.1. Publisher: Mendeley Data. Accessed 20 Mar 2025.","DOI":"10.17632\/5pc2j46cbc.1"},{"key":"2274_CR100","doi-asserted-by":"publisher","unstructured":"Deepa, B., and Ramesh, K., Preprocessed EEG dataset with epileptic seizure from SNMC Bagalkot, India. https:\/\/doi.org\/10.21227\/q6y0-e695. https:\/\/dx.doi.org\/10.21227\/q6y0-e695. Accessed 13 Mar 2025.","DOI":"10.21227\/q6y0-e695"},{"key":"2274_CR101","doi-asserted-by":"publisher","unstructured":"Shin, Y., Hwang, S., Lee, S. -B., Son, H., Chu, K., Jung, K. -Y., Lee, S. K., Park, K. -I., and Kim, Y. -G., Using spectral and temporal filters with EEG signal to predict the temporal lobe epilepsy outcome after antiseizure medication via machine learning. Sci. Rep. 13(1), 22532, 2023. https:\/\/doi.org\/10.1038\/s41598-023-49255-2. Publisher: Nature Publishing Group. Accessed 10 Mar 2025.","DOI":"10.1038\/s41598-023-49255-2"},{"key":"2274_CR102","doi-asserted-by":"publisher","unstructured":"Das, S., Mumu, S. A., Akhand, M. a. H., Salam, A., and Kamal, M. A. S., Epileptic seizure detection from decomposed EEG signal through 1D and 2D feature representation and convolutional neural network. Information 15(5):256, 2024. https:\/\/doi.org\/10.3390\/info15050256. Number: 5 Publisher: Multidisciplinary Digital Publishing Institute. Accessed 13 Feb 2025.","DOI":"10.3390\/info15050256"},{"key":"2274_CR103","doi-asserted-by":"publisher","unstructured":"Lin, N., Zheng, M., Li, L., Hu, P., Gao, W., Sun, H., Xu, C., Yuan, G., Liang, Z., Dong, Y., He, H., Cui, L., and Lu, Q., An EEG dataset for interictal epileptiform discharge with spatial distribution information. Sci. Data 12(1):229, 2025. https:\/\/doi.org\/10.1038\/s41597-025-04572-1. Publisher: Nature Publishing Group. Accessed 02 Apr 2025.","DOI":"10.1038\/s41597-025-04572-1"},{"key":"2274_CR104","doi-asserted-by":"publisher","unstructured":"Salini, G. I., and Sowmy, I., Adadelta-CSA: Adadelta-Chameleon swarm algorithm for EEG-based epileptic seizure detection. Int. J. Comput. Intell. Appl. 24(01):2450030, 2025. https:\/\/doi.org\/10.1142\/S1469026824500305. Publisher: World Scientific Publishing Co. Accessed 03 Mar 2025.","DOI":"10.1142\/S1469026824500305"},{"key":"2274_CR105","doi-asserted-by":"publisher","unstructured":"Sadiq, M., Kadhim, M. N., Al-Shammary, D., and Milanova, M., Novel EEG feature selection based on Hellinger distance for epileptic seizure detection. Smart Health 35:100536, 2025. https:\/\/doi.org\/10.1016\/j.smhl.2024.100536. Accessed 04 Mar 2025.","DOI":"10.1016\/j.smhl.2024.100536"},{"key":"2274_CR106","doi-asserted-by":"publisher","unstructured":"Qin, J., Liu, Z., Zhuang, J., and Liu, F., Dual-modality transformer with time series imaging for robust epileptic seizure prediction. Appl. Sci. 15(3):1538, 2025. https:\/\/doi.org\/10.3390\/app15031538. Number: 3 Publisher: Multidisciplinary Digital Publishing Institute. Accessed 04 Mar 2025.","DOI":"10.3390\/app15031538"},{"key":"2274_CR107","doi-asserted-by":"crossref","unstructured":"Ozer, E., K\u0131natas, A. F., Yigit, E., Demir, H. B., ..., Evaluating different hybrid learning algorithms based grid search algorithm for epileptic seizure zone detection. Publisher: papers.ssrn.com, 2025. https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5085424","DOI":"10.2139\/ssrn.5085424"},{"key":"2274_CR108","doi-asserted-by":"publisher","unstructured":"Mu\u00f1oz, F., Asenjo\u00a0Plaza, R., and Navarro, A., PaFESD: Patterns augmented by features epileptic seizure detection. IEEE Trans. Biomed. Eng. 72(1):137\u2013151, 2025. https:\/\/doi.org\/10.1109\/TBME.2024.3441090. Accessed 03 Mar 2025.","DOI":"10.1109\/TBME.2024.3441090"},{"key":"2274_CR109","doi-asserted-by":"publisher","unstructured":"Mohammadpoory, Z., Nasrolahzadeh, M., and Amiri, S. A., Patient-independent epileptic seizure detection using weighted visibility graph features and wavelet decomposition. Multimed. Tools Appl. 84(6):3197\u20133221, 2025. https:\/\/doi.org\/10.1007\/s11042-025-20594-8. Accessed 04 Mar 2025.","DOI":"10.1007\/s11042-025-20594-8"},{"key":"2274_CR110","doi-asserted-by":"publisher","unstructured":"Mekruksavanich, S., Phaphan, W., Jitpattanakul, A., Mekruksavanich, S., Phaphan, W., and Jitpattanakul, A., Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism. Math. Biosci. Eng. 22(1):73\u2013105, 2025. https:\/\/doi.org\/10.3934\/mbe.2025004. Accessed 04 Mar 2025.","DOI":"10.3934\/mbe.2025004"},{"key":"2274_CR111","doi-asserted-by":"publisher","unstructured":"Liu, Y., Liu, G., Wu, S., and Tin, C., Phase spectrogram of EEG from S-transform Enhances epileptic seizure detection. Expert Syst. Appl. 262:125621, 2025. https:\/\/doi.org\/10.1016\/j.eswa.2024.125621. Accessed 04 Mar 2025.","DOI":"10.1016\/j.eswa.2024.125621"},{"key":"2274_CR112","doi-asserted-by":"publisher","unstructured":"Liu, Y., Xu, C., Wen, Z., and Dong, Y., Trust EEG epileptic seizure detection via evidential multi-view learning. Inf. Sci. 694:121699, 2025. https:\/\/doi.org\/10.1016\/j.ins.2024.121699. Accessed 04 Mar 2025.","DOI":"10.1016\/j.ins.2024.121699"},{"key":"2274_CR113","doi-asserted-by":"publisher","unstructured":"Li, Z., Chen, B., Zhu, N., Li, W., Liu, T., Guo, L., Han, J., Zhang, T., and Yan, Z., Epileptic seizure detection in SEEG signals using a signal embedding temporal-spatial\u2013spectral transformer model. IEEE Trans. Instrum. Meas. 74:1\u201311, 2025. https:\/\/doi.org\/10.1109\/TIM.2025.3527489. Accessed 04 Mar 2025.","DOI":"10.1109\/TIM.2025.3527489"},{"key":"2274_CR114","doi-asserted-by":"publisher","unstructured":"Li, J. W., Feng, G. Y., Lv, J. J., Chen, R. J., Wang, L. J., Zeng, X. X., Yuan, J., Hu, X. L., Zhao, H. M., and Lu, X., A rhythmic encoding approach based on EEG time-frequency image for epileptic seizure detection. Biomed. Signal Process. Control 99:106824, 2025. https:\/\/doi.org\/10.1016\/j.bspc.2024.106824. Accessed 04 Mar 2025.","DOI":"10.1016\/j.bspc.2024.106824"},{"key":"2274_CR115","doi-asserted-by":"publisher","unstructured":"Jang, D., Jung, K. -Y., Jeon, Y. -G., Kim, T. -J., Lee, S. K., and Min, K., Diminished Mobilenet: A lightweight architecture for epileptic seizure prediction using single-channel EEG. Soc. Sci. Res. Netw.. https:\/\/doi.org\/10.2139\/ssrn.5097314. https:\/\/papers.ssrn.com\/abstract=5097314. Accessed 07 Apr 2025.","DOI":"10.2139\/ssrn.5097314"},{"key":"2274_CR116","doi-asserted-by":"publisher","unstructured":"Jain, S., Gupta, V. V., Bist, A. S., Joshi, M., and Garg, A., Automated epileptic seizure detection of EEG signals using machine learning. In: Gon\u00e7alves, P. J. S., Singh, P. K., Tanwar, S., and Epiphaniou, G. (Eds.), Proceedings of fifth international conference on computing, communications, and cyber-security, pp. 349\u2013357. Springer, Singapore, 2025. https:\/\/doi.org\/10.1007\/978-981-97-7371-8_27.","DOI":"10.1007\/978-981-97-7371-8_27"},{"key":"2274_CR117","doi-asserted-by":"publisher","unstructured":"El-Dajani, N., Wilhelm, T. F. L., Baumann, J., Surges, R., and Meyer, B. T., Patient-independent epileptic seizure detection with reduced EEG channels and deep recurrent neural networks. Information 16(1):20, 2025. https:\/\/doi.org\/10.3390\/info16010020. Number: 1 Publisher: Multidisciplinary Digital Publishing Institute. Accessed 04 Mar 2025.","DOI":"10.3390\/info16010020"},{"key":"2274_CR118","doi-asserted-by":"publisher","unstructured":"Chalil, B. V., Vellaiswamy, S., and Mathilakath, S., A deep learning paradigm with residual networks for enhanced epileptic seizure prediction. AIP Conference Proceedings 3253(1), 2025. https:\/\/doi.org\/10.1063\/5.0248386. Accessed Mar 04 2025.","DOI":"10.1063\/5.0248386"},{"key":"2274_CR119","doi-asserted-by":"publisher","unstructured":"Akshita, A. B., and Sharma, V., EEG-based epileptic seizure prediction using variants of the long short term memory algorithm. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 17:13, 2025. https:\/\/doi.org\/10.70917\/ijcisim-2025-0001. Accessed 04 Mar 2025.","DOI":"10.70917\/ijcisim-2025-0001"},{"key":"2274_CR120","doi-asserted-by":"publisher","unstructured":"Zhu, R., Pan, W.-X., Liu, J.-X., and Shang, J. -L., Epileptic seizure prediction via multidimensional transformer and recurrent neural network fusion. J. Transl. Med. 22(1):895, 2024. https:\/\/doi.org\/10.1186\/s12967-024-05678-7. Accessed 04 Feb 2025.","DOI":"10.1186\/s12967-024-05678-7"},{"key":"2274_CR121","doi-asserted-by":"publisher","unstructured":"Zhu, L., Wang, W., Huang, A., Ying, N., Xu, P., and Zhang, J., An efficient channel recurrent Criss-cross attention network for epileptic seizure prediction. Med. Eng. Phys. 130:104213, 2024. https:\/\/doi.org\/10.1016\/j.medengphy.2024.104213. Accessed 04 Mar 2025.","DOI":"10.1016\/j.medengphy.2024.104213"},{"key":"2274_CR122","doi-asserted-by":"publisher","unstructured":"Zhao, W., Wang, W. -F., Patnaik, L. M., Zhang, B. -C., Weng, S. -J., Xiao, S. -X., Wei, D. -Z., and Zhou, H. -F., Residual and bidirectional LSTM for epileptic seizure detection. Front. Comput. Neurosci. 18, 2024. https:\/\/doi.org\/10.3389\/fncom.2024.1415967. Publisher: Frontiers. Accessed 04 Feb 2025.","DOI":"10.3389\/fncom.2024.1415967"},{"key":"2274_CR123","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Ji, T., Xiao, M., Wang, W., Yu, G., Lin, T., Jiang, Y., Zhou, X., and Lin, Z., Cross-patient automatic epileptic seizure detection using patient-adversarial neural networks with spatio-temporal EEG augmentation. Biomed. Signal Process. Control 89:105664, 2024. https:\/\/doi.org\/10.1016\/j.bspc.2023.105664. Accessed 03 Mar 2025.","DOI":"10.1016\/j.bspc.2023.105664"},{"key":"2274_CR124","doi-asserted-by":"publisher","unstructured":"Zhang, M., Qiu, L., Zhu, Z., and Wang, L., Epileptic seizure detection based on channel attention mechanism. icicelb.org (7):733\u2013740, 2024. https:\/\/doi.org\/10.24507\/icicelb.15.07.733","DOI":"10.24507\/icicelb.15.07.733"},{"key":"2274_CR125","doi-asserted-by":"publisher","unstructured":"Zhang, J., Zheng, S., Chen, W., Du, G., Fu, Q., and Jiang, H., A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction. Sci. Rep. 14(1):16916, 2024. https:\/\/doi.org\/10.1038\/s41598-024-67855-4. Publisher: Nature Publishing Group. Accessed 04 Feb 2025.","DOI":"10.1038\/s41598-024-67855-4"},{"key":"2274_CR126","doi-asserted-by":"publisher","unstructured":"Yousif, M. A. A., and Ozturk, M., ConceFT-based epileptic seizure detection via transfer learning. Signal Image Video Process. 18(5):4349\u20134361, 2024. https:\/\/doi.org\/10.1007\/s11760-024-03077-5. Accessed 03 Mar 2025.","DOI":"10.1007\/s11760-024-03077-5"},{"key":"2274_CR127","unstructured":"Yahia, S., Mahjoub, C., Ejbali, R., and Abdelkrim, M. N., Efficient epileptic seizure detection method based on EEG images: The reduced descriptor patterns. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 16(2):16\u201316, 2024. Number: 2. Accessed 03 Mar 2025."},{"key":"2274_CR128","doi-asserted-by":"publisher","unstructured":"Yadav, A., Rathee, S., Shalu, Sheoran, D., and Kumar, P., Advanced epileptic seizure detection using deep learning and Bayesian optimization. SN Comput. Sci. 5(7):854, 2024. https:\/\/doi.org\/10.1007\/s42979-024-03201-9. Accessed 03 Mar 2025.","DOI":"10.1007\/s42979-024-03201-9"},{"key":"2274_CR129","doi-asserted-by":"publisher","unstructured":"Xu, T., Wu, Y., Tang, Y., Zhang, W., and Cui, Z., Dynamic functional connectivity neural network for epileptic seizure prediction using multi-channel EEG signal. IEEE Signal Process. Lett. 31:1499\u20131503, 2024. https:\/\/doi.org\/10.1109\/LSP.2024.3400037. Accessed 03 Mar 2025.","DOI":"10.1109\/LSP.2024.3400037"},{"key":"2274_CR130","doi-asserted-by":"publisher","unstructured":"Xia, L., Wang, R., Ye, H., Jiang, B., Li, G., Ma, C., and Gao, Z., Hybrid LSTM\u2013transformer model for the prediction of epileptic seizure using scalp EEG. IEEE Sensors J. 24(13):21123\u201321131, 2024. https:\/\/doi.org\/10.1109\/JSEN.2024.3401771. Accessed 03 Mar 2025.","DOI":"10.1109\/JSEN.2024.3401771"},{"key":"2274_CR131","doi-asserted-by":"publisher","unstructured":"Wu, Y., Liu, J., Yuan, Y., Ren, H., Dai, C., and Guo, Y., Enhancing epileptic seizure detection with random input selection in graph-wave networks. In: 2024 46th Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1\u20134, 2024. https:\/\/doi.org\/10.1109\/EMBC53108.2024.10782839. ISSN: 2694-0604. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10782839. Accessed 03 Mar 2025.","DOI":"10.1109\/EMBC53108.2024.10782839"},{"key":"2274_CR132","doi-asserted-by":"publisher","unstructured":"Wang, X., Gao, Z., Zhang, M., Wang, Y., Yang, L., Lin, J., K\u00e4rkk\u00e4inen, T., and Cong, F., Combination of channel reordering strategy and dual CNN-LSTM for epileptic seizure prediction using three iEEG datasets. IEEE J. Biomed. Health Inform. 28(11):6557\u20136567, 2024. https:\/\/doi.org\/10.1109\/JBHI.2024.3438829. Accessed 03 Mar 2025.","DOI":"10.1109\/JBHI.2024.3438829"},{"key":"2274_CR133","doi-asserted-by":"publisher","unstructured":"Wang, G., Lei, X., Li, W., Lee, W. H., Huang, L., Zhu, J., Jia, S., Wang, D., Zheng, Y., Zhang, H., Chen, B., and Wang, G., Channel-selection-based temporal convolutional network for patient-specific epileptic seizure detection. IEEE Trans. Cogn. Dev. Syst. 17(1):179\u2013188, 2024. https:\/\/doi.org\/10.1109\/TCDS.2024.3433551. Accessed 03 Mar 2025.","DOI":"10.1109\/TCDS.2024.3433551"},{"key":"2274_CR134","doi-asserted-by":"publisher","unstructured":"Vidya, J., Rani, P. P., Shaik, E. K., Inkollu, T., Gurram, M., Bommina, K., and Sri, K., Automatic epileptic seizure detection using SVM techniques with EEG signals, pp. 876\u2013883. Atlantis Press (2024). https:\/\/doi.org\/10.2991\/978-94-6463-471-6_83. ISSN: 2352-538X. https:\/\/www.atlantis-press.com\/proceedings\/icciet-24\/126001977. Accessed 03 Mar 2025.","DOI":"10.2991\/978-94-6463-471-6_83"},{"key":"2274_CR135","doi-asserted-by":"publisher","unstructured":"Vaithilingam, S. D., and Regulagedda, P., The deep learning based epileptic seizure detection using 2-layer convolutional network with long short-term memory. Int. J. Intell. Eng. Syst., 2024. https:\/\/doi.org\/10.22266\/ijies2024.1231.43.","DOI":"10.22266\/ijies2024.1231.43"},{"key":"2274_CR136","doi-asserted-by":"publisher","unstructured":"Timothy\u00a0Aboyeji, S., Wang, X., Chen, Y., Ahmad, I., Li, L., Liu, Z., Yao, C., Zhao, G., Zhang, Y., Li, G., and Chen, S., Automatic detection of epileptic seizure based on one dimensional cascaded convolutional autoencoder with adaptive window-thresholding. J. Neural Eng. 21(5):056040, 2024. https:\/\/doi.org\/10.1088\/1741-2552\/ad883a. Publisher: IOP Publishing. Accessed 12 Feb 2025.","DOI":"10.1088\/1741-2552\/ad883a"},{"key":"2274_CR137","unstructured":"Teixeira, C. A. D., Prediction of epileptic seizure based on image information derived from focal electrodes and deep classifiers. https:\/\/estudogeral.uc.pt\/retrieve\/277458\/Tese-3.pdf."},{"key":"2274_CR138","doi-asserted-by":"publisher","unstructured":"Tang, Y., Wu, Q., Mao, H., and Guo, L., Epileptic seizure detection based on path signature and Bi-LSTM network with attention mechanism. IEEE Trans. Neural Syst. Rehabil. Eng. 32:304\u2013313, 2024. https:\/\/doi.org\/10.1109\/TNSRE.2024.3350074. Accessed 03 Mar 2025.","DOI":"10.1109\/TNSRE.2024.3350074"},{"key":"2274_CR139","doi-asserted-by":"publisher","unstructured":"Tang, L., and Zhao, M., Epileptic seizure detection in neonatal EEG using a multi-band graph neural network model. Appl. Sci. 14(21):9712, 2024. https:\/\/doi.org\/10.3390\/app14219712. Number: 21 Publisher: Multidisciplinary Digital Publishing Institute. Accessed 03 Mar 2025.","DOI":"10.3390\/app14219712"},{"key":"2274_CR140","doi-asserted-by":"publisher","unstructured":"Suzui, R., Natsume, J., Saito, T., and Fujiwara, K., Long-short term memory autoencoder using delta with beta bands of EEG enables highly accurate prediction of seizure outcome in Infantile Epileptic Spasms Syndrome of unknown etiology, pp. 1\u20134 (2024). https:\/\/doi.org\/10.1109\/EMBC53108.2024.10782809. ISSN: 2694-0604. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10782809. Accessed 03 Mar 2025.","DOI":"10.1109\/EMBC53108.2024.10782809"},{"key":"2274_CR141","doi-asserted-by":"publisher","unstructured":"Suryakala, S. V., Vidya, T. R. S., and Ramakrishnans, S. H., Federated machine learning for epileptic seizure detection using EEG. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 15(4), 2024. https:\/\/doi.org\/10.14569\/IJACSA.2024.01504106. Number: 4 Publisher: The Science and Information (SAI) Organization Limited. Accessed 03 Mar 2025.","DOI":"10.14569\/IJACSA.2024.01504106"},{"key":"2274_CR142","doi-asserted-by":"publisher","unstructured":"Sun, J., Xiang, J., Dong, Y., Wang, B., Zhou, M., Ma, J., and Niu, Y., Deep learning for epileptic seizure detection using a causal-spatio-temporal model based on transfer entropy. Entropy 26(10):853, 2024. https:\/\/doi.org\/10.3390\/e26100853. Number: 10 Publisher: Multidisciplinary Digital Publishing Institute. Accessed 03 Mar 2025.","DOI":"10.3390\/e26100853"},{"key":"2274_CR143","doi-asserted-by":"publisher","unstructured":"Sonawane, P. S., and Helonde, J. B., Smart societal optimization-based deep learning convolutional neural network model for epileptic seizure prediction. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 12(1):2280551, 2024. https:\/\/doi.org\/10.1080\/21681163.2023.2280551. Accessed 03 Mar 2025.","DOI":"10.1080\/21681163.2023.2280551"},{"key":"2274_CR144","doi-asserted-by":"publisher","unstructured":"Slama, K., Riffi, J., Mahraz, M. A., Yahyaouy, A., and Tairi, H., EEG data classification by hybrid deep learning for epileptic seizure prediction. In: 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS), pp. 1\u20137, 2024. https:\/\/doi.org\/10.1109\/ICDS62089.2024.10756451. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10756451. Accessed 03 Mar 2025.","DOI":"10.1109\/ICDS62089.2024.10756451"},{"key":"2274_CR145","doi-asserted-by":"publisher","unstructured":"Sivasankari, K., and Karunanithy, K., Epileptic seizure detection using posterior probability-based convolutional neural network classifier. Multimedia Tools Appl. 83(1):551\u2013574, 2024. https:\/\/doi.org\/10.1007\/s11042-023-15816-w. Accessed 03 Mar 2025.","DOI":"10.1007\/s11042-023-15816-w"},{"key":"2274_CR146","doi-asserted-by":"publisher","unstructured":"Sharmila, K. S., Bhavya, P., Manaswi, P., Sri, S. N. S., Vasavi, V. M. S. S., and Rakshitha, V., Integrative approach for epileptic seizure detection: A comparative analysis. In: 2024 International Conference on Emerging Technologies in Computer Science for Interdisciplinary Applications (ICETCS), pp. 1\u20136, 2024. https:\/\/doi.org\/10.1109\/ICETCS61022.2024.10543465. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10543465. Accessed 03 Mar 2025.","DOI":"10.1109\/ICETCS61022.2024.10543465"},{"key":"2274_CR147","doi-asserted-by":"publisher","unstructured":"Sharma, R., and Meena, H. K., Enhanced epileptic seizure detection through graph spectral analysis of EEG signals. Circ. Syst. Signal Process. 43(8):5288\u20135308, 2024. https:\/\/doi.org\/10.1007\/s00034-024-02715-0. Accessed 03 Mar 2025.","DOI":"10.1007\/s00034-024-02715-0"},{"key":"2274_CR148","unstructured":"Sharma, A., and Singh, V. K., Machine learning-based feature extraction techniques for epileptic seizure detection using EEG bio-signals. Naturalista Campano 28(1):1336\u20131346, 2024. Number: 1. Accessed 03 Mar 2025."},{"key":"2274_CR149","doi-asserted-by":"publisher","unstructured":"Sharma, A., and Singh, V. K., Exploring supervised machine learning classifiers for epileptic seizure detection over two distinct preprocessed datasets. In: 2024 IEEE 9th International Conference for Convergence in Technology (I2CT), pp. 1\u20137, 2024. https:\/\/doi.org\/10.1109\/I2CT61223.2024.10543393. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10543393. Accessed 03 Mar 2025.","DOI":"10.1109\/I2CT61223.2024.10543393"},{"key":"2274_CR150","doi-asserted-by":"publisher","unstructured":"Shanmugam, D., and Nallasamy, V., Epileptic seizure prediction based on Convolutional neural networks and optimization techniques. Research Square. ISSN: 2693-5015, 2024. https:\/\/doi.org\/10.21203\/rs.3.rs-3917100\/v1. https:\/\/www.researchsquare.com\/article\/rs-3917100\/v1. Accessed 03 Mar 2025.","DOI":"10.21203\/rs.3.rs-3917100\/v1"},{"key":"2274_CR151","doi-asserted-by":"publisher","unstructured":"Shankar, A., Chakraborty, D., Dandapat, S., Barma, S., and Saikia, M. J., Attention-based deep learning for epileptic seizure type detection. In: 2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), pp. 1\u20134, 2024. https:\/\/doi.org\/10.1109\/ASSIC60049.2024.10507948. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10507948. Accessed 03 Mar 2025.","DOI":"10.1109\/ASSIC60049.2024.10507948"},{"key":"2274_CR152","doi-asserted-by":"publisher","unstructured":"Sadam, S. S. P., and Nalini, N. J., Epileptic seizure detection using scalogram-based hybrid CNN model on EEG signals. Signal Image Video Process. 18(2):1577\u20131588, 2024. https:\/\/doi.org\/10.1007\/s11760-023-02871-x. Accessed 03 Mar 2025.","DOI":"10.1007\/s11760-023-02871-x"},{"key":"2274_CR153","doi-asserted-by":"publisher","unstructured":"Rizki, I., Subekti, S., Indriyanto, S., Rizal, A., Triwiyanto, T., and Ziani, S., Epileptic seizure detection using DWT based on MRMR feature selection method. In: 2024 International Conference on Electrical and Information Technology (IEIT), pp. 84\u201389, 2024. https:\/\/doi.org\/10.1109\/IEIT64341.2024.10763220. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10763220. Accessed 03 Mar 2025.","DOI":"10.1109\/IEIT64341.2024.10763220"},{"key":"2274_CR154","doi-asserted-by":"publisher","unstructured":"Ravi, S., and Radhakrishnan, A., A hybrid 1D CNN-BiLSTM model for epileptic seizure detection using multichannel EEG feature fusion. Biomed. Phys. Eng. Express 10(3):035040, 2024. https:\/\/doi.org\/10.1088\/2057-1976\/ad3afd. Publisher: IOP Publishing. Accessed 03 Mar 2025.","DOI":"10.1088\/2057-1976\/ad3afd"},{"key":"2274_CR155","doi-asserted-by":"publisher","unstructured":"Ravi, S., and Radhakrishnan, A., A compact spatial attention model for automated epileptic seizure detection using multichannel EEG. In: 2024 Tenth International Conference on Bio Signals, Images, and Instrumentation (ICBSII), pp. 1\u20134, 2024. https:\/\/doi.org\/10.1109\/ICBSII61384.2024.10564088. ISSN: 2768-6450. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10564088. Accessed 03 Mar 2025.","DOI":"10.1109\/ICBSII61384.2024.10564088"},{"key":"2274_CR156","doi-asserted-by":"publisher","unstructured":"Rao, S., Liu, M., Huang, Y., Yang, H., Liang, J., Lu, J., Niu, Y., and Wang, B., Anchoring temporal convolutional networks for epileptic seizure prediction. J. Neural Eng. 21(6):066008, 2024. https:\/\/doi.org\/10.1088\/1741-2552\/ad8bf3. Publisher: IOP Publishing. Accessed 03 Mar 2025.","DOI":"10.1088\/1741-2552\/ad8bf3"},{"key":"2274_CR157","doi-asserted-by":"crossref","unstructured":"Rani, T. J., and Kavitha, D., Effective epileptic seizure detection using enhanced Salp swarm algorithm-based long short-term memory network. IETE J. Res. 70(2):1538\u20131555, 2024. Accessed 03 Mar 2025.","DOI":"10.1080\/03772063.2022.2153090"},{"key":"2274_CR158","doi-asserted-by":"publisher","unstructured":"Ramkumar, M., Jamaesha, S. S., Gowtham, M. S., and Kumar, C. S., IoT and cloud computing-based automated epileptic seizure detection using optimized Siamese convolutional sparse autoencoder network. Signal Image Video Process. 18(4):3509\u20133525, 2024. https:\/\/doi.org\/10.1007\/s11760-024-03017-3. Accessed 03 Mar 2025.","DOI":"10.1007\/s11760-024-03017-3"},{"key":"2274_CR159","doi-asserted-by":"publisher","unstructured":"Ra, J. S., Li, T., and Li, Y., Epileptic seizure prediction based on synchroextracting transform and sparse representation. IEEE Access 12:187684\u2013187695, 2024. https:\/\/doi.org\/10.1109\/ACCESS.2024.3514859. Accessed 03 Mar 2025.","DOI":"10.1109\/ACCESS.2024.3514859"},{"key":"2274_CR160","doi-asserted-by":"publisher","unstructured":"Quadri, Z. F., Saqib\u00a0Akhoon, M., and Loan, S. A., Epileptic seizure prediction using stacked CNN-BiLSTM: A novel approach. IEEE Trans. Artif. Intell. 5(11):5553\u20135560, 2024. https:\/\/doi.org\/10.1109\/TAI.2024.3410928. Accessed 03 Mar 2025.","DOI":"10.1109\/TAI.2024.3410928"},{"key":"2274_CR161","doi-asserted-by":"publisher","unstructured":"Qi, N., Piao, Y., Wang, Q., Li, X., and Wang, Y., Semi-supervised seizure prediction based on deep pairwise representation alignment of epileptic EEG signals. IEEE Access 12:119056\u2013119071, 2024. https:\/\/doi.org\/10.1109\/ACCESS.2024.3447901. Accessed 03 Mar 2025.","DOI":"10.1109\/ACCESS.2024.3447901"},{"key":"2274_CR162","doi-asserted-by":"publisher","unstructured":"Priyanka, R., Sathesh, M., Tamil\u00a0Selvi, T., Saikumar, P. J., Venkatachalam, K., and Raja, M., Efficient approach for epileptic seizure classification and detection based on genetic algorithm with CNN-RNN classifier. In: 2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), pp. 1\u20137 (2024). https:\/\/doi.org\/10.1109\/ACCAI61061.2024.10602166. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10602166. Accessed 03 Mar 2025.","DOI":"10.1109\/ACCAI61061.2024.10602166"},{"key":"2274_CR163","doi-asserted-by":"publisher","unstructured":"Pouryosef, M., Abedini-Nassab, R., and Akrami, S. M. R., A novel framework for epileptic seizure detection using electroencephalogram signals based on the bat feature selection algorithm. Neuroscience 541:35\u201349, 2024. https:\/\/doi.org\/10.1016\/j.neuroscience.2024.01.014. Accessed 03 Mar 2025.","DOI":"10.1016\/j.neuroscience.2024.01.014"},{"key":"2274_CR164","doi-asserted-by":"publisher","unstructured":"Pedram, R., Farzanehkari, P., Heydarloo, M. M., Chaibakhsh, A., Kordestani, M., and Saif, M., Enhanced epileptic seizure detection based on information fusion techniques. In: Arai, K., (Ed.), Intelligent systems and applications, pp. 713\u2013725. Springer, Cham, 2024. https:\/\/doi.org\/10.1007\/978-3-031-66428-1_45.","DOI":"10.1007\/978-3-031-66428-1_45"},{"key":"2274_CR165","unstructured":"Pawar, N., Saxena, A., and Singh, A. P., A robust machine learning method for real-time epileptic seizure detection in EEG. ijirtm.com, 2024. Type: PDF."},{"key":"2274_CR166","doi-asserted-by":"publisher","unstructured":"Parani, P., Mohammad, U., and Saeed, F., Utilizing pretrained vision transformers and large language models for epileptic seizure prediction. bioRxiv. Pages: 2024.11.03.621742 Section: New Results, 2024.https:\/\/doi.org\/10.1101\/2024.11.03.621742. https:\/\/www.biorxiv.org\/content\/10.1101\/2024.11.03.621742. Accessed 03 Mar 2025.","DOI":"10.1101\/2024.11.03.621742"},{"key":"2274_CR167","doi-asserted-by":"publisher","unstructured":"Pan, Y., Dong, F., Yao, W., Meng, X., and Xu, Y., Empirical mode decomposition for deep learning-based epileptic seizure detection in few-shot scenario. IEEE Access 12:86583\u201386595, 2024. https:\/\/doi.org\/10.1109\/ACCESS.2024.3415716. Accessed 03 Mar 2025.","DOI":"10.1109\/ACCESS.2024.3415716"},{"key":"2274_CR168","doi-asserted-by":"publisher","unstructured":"Nour, M., Arabac\u0131, B., \u00f6cal, H., and Polat, K., New approaches to epileptic seizure prediction based on EEG signals using hybrid CNNs. Int. J. Intell. Eng. Inform. 12(1):85\u2013102, 2024. https:\/\/doi.org\/10.1504\/IJIEI.2024.137706. Publisher: Inderscience Publishers. Accessed 03 Mar 2025.","DOI":"10.1504\/IJIEI.2024.137706"},{"key":"2274_CR169","doi-asserted-by":"publisher","unstructured":"Nithya, S., Ramakrishnan, S., Murugavel, A. S. M., Ponni\u00a0sathya, S., Meenachi, L., and Rajakumari, R. G., Detection of epileptic seizure from EEG signals using majority rule based local binary pattern. Wirel. Pers. Commun. 134(2):721\u2013734, 2024. https:\/\/doi.org\/10.1007\/s11277-024-10916-8. Accessed 03 Mar 2025.","DOI":"10.1007\/s11277-024-10916-8"},{"key":"2274_CR170","doi-asserted-by":"publisher","unstructured":"Nabila, Y., and Zakaria, H., Epileptic seizure prediction from EEG signal recording using energy and dispersion entropy with SVM classifier. In: 2024 International Conference on Information Technology Research and Innovation (ICITRI), pp. 1\u20136, 2024. https:\/\/doi.org\/10.1109\/ICITRI62858.2024.10699014. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10699014. Accessed 03 Mar 2025.","DOI":"10.1109\/ICITRI62858.2024.10699014"},{"key":"2274_CR171","doi-asserted-by":"publisher","unstructured":"Mourad, R., Diab, A., Merhi, Z., Khalil, M., and Jeann\u00e9s, R. L. B., Epileptic seizure detection using energy thresholding. In: 2024 International Conference on Smart Systems and Power Management (IC2SPM), pp. 12\u201316, 2024. https:\/\/doi.org\/10.1109\/IC2SPM62723.2024.10841348. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10841348. Accessed 03 Mar 2025.","DOI":"10.1109\/IC2SPM62723.2024.10841348"},{"key":"2274_CR172","doi-asserted-by":"publisher","unstructured":"Mohapatra, S. K., Swain, K., Mallik, P., Mali, S., and Ali, F. A., Hybridization of sparrow search algorithm and sine cosine algorithm for epileptic seizure detection. In: 2024 3rd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON), pp. 1\u20135, 2024. https:\/\/doi.org\/10.1109\/ODICON62106.2024.10797567. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10797567. Accessed 03 Mar 2025.","DOI":"10.1109\/ODICON62106.2024.10797567"},{"key":"2274_CR173","doi-asserted-by":"publisher","unstructured":"Meng, K., Wang, D., Zhang, D., Guo, K., Lu, K., Lu, J., Yu, R., Zhang, L., Hu, Y., Zhang, R., and Chen, M., Real-Time epileptic seizure prediction method with spatio-temporal information transfer learning. IEEE J. Biomed. Health Inform. 1\u201312, 2024. https:\/\/doi.org\/10.1109\/JBHI.2024.3509959. Accessed 03 Mar 2025.","DOI":"10.1109\/JBHI.2024.3509959"},{"key":"2274_CR174","doi-asserted-by":"publisher","unstructured":"Meesala, G., Soni, B., Pandey, M., and Khare, N., Epileptic seizure detection using quantum support vector classifier. In: 2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST), pp. 1\u20134, 2024. https:\/\/doi.org\/10.1109\/ICCIGST60741.2024.10717615. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10717615. Accessed 03 Mar 2025.","DOI":"10.1109\/ICCIGST60741.2024.10717615"},{"key":"2274_CR175","doi-asserted-by":"publisher","unstructured":"Meesala, G., Kumar, L., Pandey, M., and Khare, N., Epileptic seizure detection using variational quantum classifier. In: 2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST), pp. 1\u20135, 2024. https:\/\/doi.org\/10.1109\/ICCIGST60741.2024.10717599. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10717599. Accessed 03 Mar 2025.","DOI":"10.1109\/ICCIGST60741.2024.10717599"},{"key":"2274_CR176","doi-asserted-by":"publisher","unstructured":"Mazurek, S., Blanco, R., Falc\u00f3-Roget, J., and Crimi, A., Explainable graph neural networks for EEG classification and seizure detection in epileptic patients. In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1\u20135, 2024. https:\/\/doi.org\/10.1109\/ISBI56570.2024.10635821. ISSN: 1945-8452. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10635821. Accessed 03 Mar 2025.","DOI":"10.1109\/ISBI56570.2024.10635821"},{"key":"2274_CR177","doi-asserted-by":"publisher","unstructured":"Mallik, P., Nayak, A. K., Mohapatra, S. K., and Swain, K. P., SH-OSP: A hybrid algorithm using spotted Hyena optimizer enabled with optimal stochastic process for epileptic seizure detection. SN Comput. Sci. 5(8):1168, 2024. https:\/\/doi.org\/10.1007\/s42979-024-03488-8. Accessed 03 Mar 2025.","DOI":"10.1007\/s42979-024-03488-8"},{"key":"2274_CR178","doi-asserted-by":"publisher","unstructured":"Ma, H., Wu, Y., Tang, Y., Chen, R., Xu, T., and Zhang, W., Parallel dual-branch fusion network for epileptic seizure prediction. Comput. Biol. Med. 176:108565, 2024. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108565. Accessed 03 Mar 2025.","DOI":"10.1016\/j.compbiomed.2024.108565"},{"key":"2274_CR179","doi-asserted-by":"publisher","unstructured":"Lou, J., Zhang, J., Li, Z., and Feng, E., MTL-SSU: A multi-task self-supervised learning framework for epileptic seizure prediction. In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 3565\u20133570, 2024. https:\/\/doi.org\/10.1109\/BIBM62325.2024.10821748. ISSN: 2156-1133. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10821748. Accessed 03 Mar 2025.","DOI":"10.1109\/BIBM62325.2024.10821748"},{"key":"2274_CR180","doi-asserted-by":"publisher","unstructured":"Liu, X., Li, C., Lou, X., Kong, H., Li, X., Li, Z., and Zhong, L., Epileptic seizure prediction based on EEG using pseudo-three-dimensional CNN. Front. Neuroinform. 18, 2024. https:\/\/doi.org\/10.3389\/fninf.2024.1354436. Publisher: Frontiers. Accessed 03 Mar 2025.","DOI":"10.3389\/fninf.2024.1354436"},{"key":"2274_CR181","doi-asserted-by":"publisher","unstructured":"Liu, S., Wang, J., Li, S., and Cai, L., Multi-dimensional hybrid bilinear CNN-LSTM models for epileptic seizure detection and prediction using EEG signals. J. Neural Eng. 21(6):066045, 2024. https:\/\/doi.org\/10.1088\/1741-2552\/ada0e5. Publisher: IOP Publishing. Accessed 03 Mar 2025.","DOI":"10.1088\/1741-2552\/ada0e5"},{"key":"2274_CR182","doi-asserted-by":"publisher","unstructured":"Li, Z., Li, W., Zhu, N., Han, J., Liu, T., Chen, B., Yan, Z., and Zhang, T., Epileptic seizure detection in SEEG signals using a unified multi-scale temporal-spatial-spectral transformer model. In: Linguraru, M. G., Dou, Q., Feragen, A., Giannarou, S., Glocker, B., Lekadir, K., and Schnabel, J. A. (Eds.), Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024, pp. 254\u2013264. Springer, Cham, 2024. https:\/\/doi.org\/10.1007\/978-3-031-72120-5_24.","DOI":"10.1007\/978-3-031-72120-5_24"},{"key":"2274_CR183","doi-asserted-by":"publisher","unstructured":"Li, C., Liu, X., Zhong, L., and Li, Z., Epileptic seizure detection using a lightweight network based on style-controlling normalization. In: 2024 5th International seminar on Artificial Intelligence, Networking and Information Technology (AINIT), pp. 2301\u20132304, 2024. https:\/\/doi.org\/10.1109\/AINIT61980.2024.10581785. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10581785. Accessed 03 Mar 2025.","DOI":"10.1109\/AINIT61980.2024.10581785"},{"key":"2274_CR184","doi-asserted-by":"publisher","unstructured":"Kunekar, P., Gupta, M. K., and Gaur, P., Detection of epileptic seizure in EEG signals using machine learning and deep learning techniques. J. Eng. Appl. Sci. 71(1):21, 2024. https:\/\/doi.org\/10.1186\/s44147-023-00353-y. Accessed 03 Mar 2025.","DOI":"10.1186\/s44147-023-00353-y"},{"key":"2274_CR185","doi-asserted-by":"publisher","unstructured":"Koutsouvelis, P., Chybowski, B., Gonzalez-Sulser, A., Abdullateef, S., and Escudero, J., Preictal period optimization for deep learning-based epileptic seizure prediction. J. Neural Eng. 21(6):066040, 2024. https:\/\/doi.org\/10.1088\/1741-2552\/ad9ad0. Publisher: IOP Publishing. Accessed 03 Mar 2025.","DOI":"10.1088\/1741-2552\/ad9ad0"},{"key":"2274_CR186","doi-asserted-by":"publisher","unstructured":"Kouka, N., Fourati, R., Baghdadi, A., Siarry, P., and Adel, M., A mutual information-based many-objective optimization method for EEG channel selection in the epileptic seizure prediction task. Cogn. Comput. 16(3):1268\u20131286, 2024. https:\/\/doi.org\/10.1007\/s12559-024-10261-9. Accessed 03 Mar 2025.","DOI":"10.1007\/s12559-024-10261-9"},{"key":"2274_CR187","doi-asserted-by":"publisher","unstructured":"Kode, H., Elleithy, K., and Almazaydeh, L., Epileptic seizure detection in EEG signals using machine learning and deep learning techniques. IEEE Access 12:80657\u201380668, 2024. https:\/\/doi.org\/10.1109\/ACCESS.2024.3409581. Accessed 03 Mar 2025.","DOI":"10.1109\/ACCESS.2024.3409581"},{"key":"2274_CR188","doi-asserted-by":"publisher","unstructured":"Kiran, M., Naik, M. S., Yashwanth, J., Humse, K. K., Chaitra, S. N., Deepa, T., and Sunilkumar, D. S., Improved feature space for EEG-based epileptic seizure detection using signal processing techniques. Research Square. ISSN: 2693-5015, 2024. https:\/\/doi.org\/10.21203\/rs.3.rs-3869119\/v1. https:\/\/www.researchsquare.com\/article\/rs-3869119\/v1. Accessed 03 Mar 2025.","DOI":"10.21203\/rs.3.rs-3869119\/v1"},{"key":"2274_CR189","doi-asserted-by":"publisher","unstructured":"Khan, F. A., Umar, Z., Jolfaei, A., and Tariq, M., Explainable AI for epileptic seizure detection in Internet of Medical Things. Digit. Commun. Netw., 2024. https:\/\/doi.org\/10.1016\/j.dcan.2024.08.013. Accessed 03 Mar 2025.","DOI":"10.1016\/j.dcan.2024.08.013"},{"key":"2274_CR190","doi-asserted-by":"publisher","unstructured":"Kavya, B. S., and Prasad, S. N., Feature fusion for epileptic seizure detection from EEG data: A CNN- based approach with high precision. Research Square. ISSN: 2693-5015, 2024. https:\/\/doi.org\/10.21203\/rs.3.rs-3909688\/v1. https:\/\/www.researchsquare.com\/article\/rs-3909688\/v1. Accessed 03 Mar 2025.","DOI":"10.21203\/rs.3.rs-3909688\/v1"},{"key":"2274_CR191","doi-asserted-by":"publisher","unstructured":"Kasthuri, N., Ramyea, R., Arunprasshath, V. S., Abhineeth, S., and Bharathraj, S., EEG conformer model based epileptic seizure prediction using deep learning. In: 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1\u20137, 2024. https:\/\/doi.org\/10.1109\/ICCCNT61001.2024.10726097. ISSN: 2473-7674. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10726097. Accessed 03 Mar 2025.","DOI":"10.1109\/ICCCNT61001.2024.10726097"},{"key":"2274_CR192","doi-asserted-by":"publisher","unstructured":"Kantipudi, M. V. V. P., Kumar, N. S. P., Aluvalu, R., Selvarajan, S., and Kotecha, K., An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection. Sci. Rep. 14(1):843, 2024. https:\/\/doi.org\/10.1038\/s41598-024-51337-8. Publisher: Nature Publishing Group. Accessed 03 Mar 2025.","DOI":"10.1038\/s41598-024-51337-8"},{"key":"2274_CR193","doi-asserted-by":"publisher","unstructured":"Kalpana, C., and Mohanbabu, G., Integrated TSVM-TSK fusion for enhanced EEG-based epileptic seizure detection: Robust classifier with competitive learning. Biomed. Signal Process. Control 96:106440, 2024. https:\/\/doi.org\/10.1016\/j.bspc.2024.106440. Accessed 03 Mar 2025.","DOI":"10.1016\/j.bspc.2024.106440"},{"key":"2274_CR194","doi-asserted-by":"publisher","unstructured":"Kalita, D., Dash, S., and Mirza, K. B., EpiNET: An optimized, resource rfficient deep GRU-LSTM network for epileptic seizure prediction. Biomed. Eng. Appl. Basis Commun. 36(04):2450021, 2024. https:\/\/doi.org\/10.4015\/S1016237224500212. Publisher: National Taiwan University. Accessed 03 Mar 2025.","DOI":"10.4015\/S1016237224500212"},{"key":"2274_CR195","doi-asserted-by":"publisher","unstructured":"Joshi, S., Rout, P. K., Samanta, I. S., Cherukuri, M., and Swain, K., Epileptic Seizure Detection using Denoising Autoencoder. In: 2024 International Conference on Emerging Systems and Intelligent Computing (ESIC), pp. 5\u201310, 2024. https:\/\/doi.org\/10.1109\/ESIC60604.2024.10481560. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10481560. Accessed 03 Mar 2025.","DOI":"10.1109\/ESIC60604.2024.10481560"},{"key":"2274_CR196","doi-asserted-by":"publisher","unstructured":"Jibon, F. A., Chowdhury, A. R. J., Miraz, M. H., Jin, H. H., Khandaker, M. U., Sultana, S., Nur, S., Siddiqui, F. H., Kamal, A. H. M., Salman, M., and Youssef, A. A. F., Sequential graph convolutional network and DeepRNN based hybrid framework for epileptic seizure detection from EEG signal. 2024. https:\/\/doi.org\/10.1177\/20552076241249874. Publisher: SAGE PublicationsSage UK: London, England. Accessed 03 Mar 2025.","DOI":"10.1177\/20552076241249874"},{"key":"2274_CR197","doi-asserted-by":"publisher","unstructured":"Ji, D., He, L., Dong, X., Li, H., Zhong, X., Liu, G., and Zhou, W., Epileptic seizure prediction using spatiotemporal feature fusion on EEG. Int. J. Neural Syst. 34(08):2450041, 2024. https:\/\/doi.org\/10.1142\/S0129065724500412. Publisher: World Scientific Publishing Co. Accessed 03 Mar 2025.","DOI":"10.1142\/S0129065724500412"},{"key":"2274_CR198","doi-asserted-by":"publisher","unstructured":"Jemal, I., Abou-Abbas, L., Henni, K., Mitiche, A., and Mezghani, N., Domain adaptation for EEG-based, cross-subject epileptic seizure prediction. Front. Neuroinform. 18, 2024. https:\/\/doi.org\/10.3389\/fninf.2024.1303380. Publisher: Frontiers. Accessed 13 Feb 2025.","DOI":"10.3389\/fninf.2024.1303380"},{"key":"2274_CR199","doi-asserted-by":"publisher","unstructured":"Jamunadevi, C., and Arul, P., Adaptive Bi-LSTM-based epileptic seizure prediction from EEG signals using deep learning algorithm. Mapana J. Sci. 23(2):65\u201382, 2024. https:\/\/doi.org\/10.12723\/mjs.69.4. Number: 2. Accessed 13 Feb 2025.","DOI":"10.12723\/mjs.69.4"},{"key":"2274_CR200","doi-asserted-by":"publisher","unstructured":"Himalyan, S., and Gupta, V., Support Vector machine-based epileptic seizure detection using EEG signals. Eng. Proc. 18(1):73, 2024. https:\/\/doi.org\/10.3390\/ecsa-11-20506. Number: 1 Publisher: Multidisciplinary Digital Publishing Institute. Accessed 13 Feb 2025.","DOI":"10.3390\/ecsa-11-20506"},{"key":"2274_CR201","doi-asserted-by":"publisher","unstructured":"Hermawan, A. T., Zaeni, I. A. E., Wibawa, A. P., Gunawan, G., Hendrawan, W. H., and Kristian, Y., A multi representation deep learning approach for epileptic seizure detection. J. Robot. Control (JRC) 5(1):187\u2013204, 2024. https:\/\/doi.org\/10.18196\/jrc.v5i1.20870. Number: 1. Accessed 13 Feb 2025.","DOI":"10.18196\/jrc.v5i1.20870"},{"key":"2274_CR202","doi-asserted-by":"publisher","unstructured":"Henni, K., Abou-Abbas, L., Jmal, I., Mitiche, A., and Mezghani, N., Imbalance-aware machine learning for epileptic seizure detection. In: 2024 IEEE 12th International Symposium on Signal, Image, Video and Communications (ISIVC), pp. 1\u20135, 2024. https:\/\/doi.org\/10.1109\/ISIVC61350.2024.10577877. ISSN: 2832-8337. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10577877. Accessed 13 Feb 2025.","DOI":"10.1109\/ISIVC61350.2024.10577877"},{"key":"2274_CR203","unstructured":"Helgesen, M., Epileptic seizure prediction from EEG signals using machine learning. researchgate.net, 2024. https:\/\/www.researchgate.net\/publication\/388218540_Epileptic_Seizure_Prediction_from_EEG_Signals_using_Machine_Learning."},{"key":"2274_CR204","doi-asserted-by":"publisher","unstructured":"He, Z., Yang, J., Alroobaea, R., and Yee\u00a0Por, L., SeizureLSTM: An optimal attention-based trans-LSTM network for epileptic seizure detection using optimal weighted feature integration. Biomed. Signal Process. Control 96:106603, 2024. https:\/\/doi.org\/10.1016\/j.bspc.2024.106603. Accessed 13 Feb 2025.","DOI":"10.1016\/j.bspc.2024.106603"},{"key":"2274_CR205","doi-asserted-by":"publisher","unstructured":"He, C., Ma, P., Shi, J., Qu, C., Wang, Q., Yao, C., and Hao, Y., A lightweight 1D-CNN-GRU model for epileptic seizure prediction. Research Square. ISSN: 2693-5015, 2024. https:\/\/doi.org\/10.21203\/rs.3.rs-4681232\/v1. https:\/\/www.researchsquare.com\/article\/rs-4681232\/v1. Accessed 13 Feb 2025.","DOI":"10.21203\/rs.3.rs-4681232\/v1"},{"key":"2274_CR206","doi-asserted-by":"publisher","unstructured":"Hassan, M. M., Haque, R., Islam, S. M. S., Meshref, H., Alroobaea, R., Masud, M., Bairagi, A. K., Hassan, M. M., Haque, R., Islam, S. M. S., Meshref, H., Alroobaea, R., Masud, M., and Bairagi, A. K., NeuroWave-Net: Enhancing epileptic seizure detection from EEG brain signals via advanced convolutional and long short-term memory networks. AIMS Bioeng. 11(1):85\u2013109, 2024. https:\/\/doi.org\/10.3934\/bioeng.2024006. Accessed 13 Feb 2025.","DOI":"10.3934\/bioeng.2024006"},{"key":"2274_CR207","doi-asserted-by":"publisher","unstructured":"Hassan, K. M., Zhao, X., Sugano, H., and Tanaka, T., Riemannian manifold-based epileptic seizure detection using transfer learning and artifact rejection techniques. APSIPA Trans. Signal Inf. Process. 13(1), 2024. https:\/\/doi.org\/10.1561\/116.20240032. Publisher: Now Publishers, Inc. Accessed 13 Feb 2025.","DOI":"10.1561\/116.20240032"},{"key":"2274_CR208","doi-asserted-by":"publisher","unstructured":"Hasan, S. A., Yasin, O., Taib, E., Nassif, O., Joudeh, M., and Audaall, S., Advanced machine learning techniques for precise EEG analysis and epileptic seizure detection. In: 2024 22nd International Conference on Research and Education in Mechatronics (REM), pp. 354\u2013358, 2024. https:\/\/doi.org\/10.1109\/REM63063.2024.10735614. ISSN: 2993-4591. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10735614. Accessed 13 Feb 2025.","DOI":"10.1109\/REM63063.2024.10735614"},{"key":"2274_CR209","doi-asserted-by":"publisher","unstructured":"Gulfaraz, M., Johnson, S., and Saadeh, W., Analysis and design of a compute-in-memory system for epileptic seizure detection system. In: 2024 IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 792\u2013796, 2024. https:\/\/doi.org\/10.1109\/MWSCAS60917.2024.10658867. ISSN: 1558-3899. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10658867. Accessed 13 Feb 2025.","DOI":"10.1109\/MWSCAS60917.2024.10658867"},{"key":"2274_CR210","doi-asserted-by":"publisher","unstructured":"Grubov, V. V., Nazarikov, S. I., Kurkin, S. A., Utyashev, N. P., Andrikov, D. A., Karpov, O. E., and Hramov, A. E., Two-stage approach with combination of outlier detection method and deep learning enhances automatic epileptic seizure detection. IEEE Access 12:122168\u2013122182, 2024. https:\/\/doi.org\/10.1109\/ACCESS.2024.3453039. Accessed 13 Feb 2025.","DOI":"10.1109\/ACCESS.2024.3453039"},{"key":"2274_CR211","doi-asserted-by":"publisher","unstructured":"Grubov, V., Nazarikov, S., Utyashev, N., and Karpov, O. E., Error-aware CNN improves automatic epileptic seizure detection. Eur. Phys. J. Spec. Top., 2024. https:\/\/doi.org\/10.1140\/epjs\/s11734-024-01292-2. Accessed 13 Feb 2025.","DOI":"10.1140\/epjs\/s11734-024-01292-2"},{"key":"2274_CR212","unstructured":"Gopalakrishnan, A., Surendran, S. P., Wahab, A. B., ..., Enhanced detection of epileptic seizure using supervised and unsupervised algorithms. In: Workshop on Advances in Computational Intelligence at ICAIDS 2023. ceur-ws.org, 2024. https:\/\/ceur-ws.org\/Vol-3706\/Paper3.pdf."},{"key":"2274_CR213","unstructured":"Ghuli, A., Kannur, A., Mali, A., and Mangasuli, A., Quantification of EEG characteristics for epileptic seizure detection and monitoring of anaesthesia using spectral analysis. mecs-press.org, 2024. Type: PDF."},{"key":"2274_CR214","doi-asserted-by":"publisher","unstructured":"Fredes, S. U., Firoozabadi, A. D., Adasme, P., Zabala-Blanco, D., Palacios\u00a0J\u00e1tiva, P., and Azurdia-Meza, C., Enhanced epileptic seizure detection through wavelet-based analysis of EEG signal processing. Appl. Sci. 14(13):5783, 2024. https:\/\/doi.org\/10.3390\/app14135783. Number: 13 Publisher: Multidisciplinary Digital Publishing Institute. Accessed 13 Feb 2025.","DOI":"10.3390\/app14135783"},{"key":"2274_CR215","doi-asserted-by":"publisher","unstructured":"Fatma, N., Singh, P., and Siddiqui, M. K., Enhanced epileptic seizure detection: Convolution neural net and features selection in EEG signals. In: 2024 International Conference on Automation and Computation (AUTOCOM), pp. 502\u2013506, 2024. https:\/\/doi.org\/10.1109\/AUTOCOM60220.2024.10486151. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10486151. Accessed 13 Feb 2025.","DOI":"10.1109\/AUTOCOM60220.2024.10486151"},{"key":"2274_CR216","doi-asserted-by":"crossref","unstructured":"Esha, O. K., Begum, N., and Rahman, S., EpiNet: A hybrid machine learning model for epileptic seizure prediction using EEG signals from a 500 patient dataset. Int. J. Adv. Comput. Sci. Appl., 2024. Publisher: search.ebscohost.com.","DOI":"10.14569\/IJACSA.2024.01501116"},{"key":"2274_CR217","doi-asserted-by":"publisher","unstructured":"Egorova, L. D., Kazakovtsev, L. A., Stupina, A. A., Rozhnov, I. P., and Savitskaya, T. N., Hybrid stacking model for automatic epileptic seizure detection using electroencephalogram signals. In: 2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), pp. 1\u20136, 2024. https:\/\/doi.org\/10.1109\/ICECCME62383.2024.10796593. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10796593. Accessed 13 Feb 2025.","DOI":"10.1109\/ICECCME62383.2024.10796593"},{"key":"2274_CR218","doi-asserted-by":"publisher","unstructured":"Dutta, K. K., Manohar, P., and Krishnappa, I., Seizure stage detection of epileptic seizure using convolutional neural networks. Int. J. Electr. Comput. Eng. (IJECE) 14(2):2226\u20132233, 2024. https:\/\/doi.org\/10.11591\/ijece.v14i2.pp2226-2233. Number: 2. Accessed 13 Feb 2025.","DOI":"10.11591\/ijece.v14i2.pp2226-2233"},{"key":"2274_CR219","doi-asserted-by":"publisher","unstructured":"Dong, X., Wen, Y., Ji, D., Yuan, S., Liu, Z., Shang, W., and Zhou, W., Epileptic seizure detection with an end-to-end temporal convolutional network and bidirectional long short-term memory model. Int. J. Neural Syst. 34(03):2450012, 2024. https:\/\/doi.org\/10.1142\/S0129065724500126. Publisher: World Scientific Publishing Co. Accessed 13 Feb 2025.","DOI":"10.1142\/S0129065724500126"},{"key":"2274_CR220","doi-asserted-by":"crossref","unstructured":"Deng, X., A novel dual-branch network for comprehensive spatiotemporal information integration for Eeg-based epileptic seizure detection. Publisher: papers.ssrn.com, 2024. https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4906994.","DOI":"10.2139\/ssrn.4906994"},{"key":"2274_CR221","doi-asserted-by":"publisher","unstructured":"Cui, H., Zhong, X., Li, H., Li, C., Dong, X., Ji, D., He, L., and Zhou, W., A lightweight convolutional neural network-reformer model for efficient epileptic seizure detection. Int. J. Neural Syst. 34(12):2450065, 2024. https:\/\/doi.org\/10.1142\/S0129065724500655.","DOI":"10.1142\/S0129065724500655"},{"key":"2274_CR222","doi-asserted-by":"publisher","unstructured":"Cheng, L., Xiong, J., Duan, J., Zhang, Y., Chen, C., Zhong, J., Zhou, Z., and Quan, Y., SaE-GBLS: An effective self-adaptive evolutionary optimized graph-broad model for EEG-based automatic epileptic seizure detection. Front. Comput. Neurosci. 18, 2024. https:\/\/doi.org\/10.3389\/fncom.2024.1379368. Publisher: Frontiers. Accessed 13 Feb 2025.","DOI":"10.3389\/fncom.2024.1379368"},{"key":"2274_CR223","doi-asserted-by":"publisher","unstructured":"Chavan, P. A., and Desai, S., An efficient epileptic seizure detection by classifying focal and non-focal EEG signals using optimized deep dual adaptive CNN-HMM classifier. Multimedia Tools Appl. 83(19):57347\u201357388, 2024. https:\/\/doi.org\/10.1007\/s11042-024-18560-x. Accessed 13 Feb 2025.","DOI":"10.1007\/s11042-024-18560-x"},{"key":"2274_CR224","doi-asserted-by":"publisher","unstructured":"Buldu, A., Kaplan, K., and Kuncan, M., A hybrid study for epileptic seizure detection based on deep learning using EEG data. J. Univ. Comput. Sci. 30(7), 2024. https:\/\/doi.org\/10.3897\/jucs.109933. Accessed 13 Feb 2025.","DOI":"10.3897\/jucs.109933"},{"key":"2274_CR225","doi-asserted-by":"publisher","unstructured":"Bouallagui, H., Chniter, H., Ghaffari, F., and Romain, O., Enhancing deep learning-based epileptic seizure detection with generative AI techniques. In: 2024 International Conference on Microelectronics (ICM), pp. 1\u20136, 2024. https:\/\/doi.org\/10.1109\/ICM63406.2024.10815779. ISSN: 2159-1679. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10815779. Accessed 13 Feb 2025.","DOI":"10.1109\/ICM63406.2024.10815779"},{"key":"2274_CR226","doi-asserted-by":"publisher","unstructured":"Borhade, R. R., Barekar, S. S., Ohatkar, S. N., Mathurkar, P. K., Borhade, R. H., and Bangare, P. M., ResneXt-Lenet: A hybrid deep learning for epileptic seizure prediction. Intelligent Decision Technologies, 2024. https:\/\/doi.org\/10.3233\/IDT-240923. Publisher: SAGE PublicationsSage UK: London, England. Accessed 12 Feb 2025.","DOI":"10.3233\/IDT-240923"},{"key":"2274_CR227","doi-asserted-by":"publisher","unstructured":"Bhadra, R., Singh, P. K., and Mahmud, M., HyEpiSeiD: A hybrid convolutional neural network and gated recurrent unit model for epileptic seizure detection from electroencephalogram signals. Brain Inform. 11(1):21, 2024. https:\/\/doi.org\/10.1186\/s40708-024-00234-x. Accessed 12 Feb 2025.","DOI":"10.1186\/s40708-024-00234-x"},{"key":"2274_CR228","unstructured":"Bettayeb, N., and Hadjadj, Y., EEG signals classification for epileptic seizure detection. PhD thesis, 2024. https:\/\/dspace.univ-ouargla.dz\/jspui\/handle\/123456789\/37333."},{"key":"2274_CR229","doi-asserted-by":"publisher","unstructured":"Basha, N. K., Surendiran, B., Benzikar, A., and Joyal, S., Hybrid approach for the detection of epileptic seizure using electroencephalography input. Int. J. Inf. Technol. 16(1):569\u2013575, 2024. https:\/\/doi.org\/10.1007\/s41870-023-01657-1. Accessed 12 Feb 2025.","DOI":"10.1007\/s41870-023-01657-1"},{"key":"2274_CR230","doi-asserted-by":"publisher","unstructured":"Assim, O. M., and Mahmood, A. F., A new epileptic seizure prediction framework based on electroencephalography signals. Mol. Sci. Appl. 4:57\u201364, 2024. https:\/\/doi.org\/10.37394\/232023.2024.4.7. Publisher: WSEAS. Accessed 12 Feb 2025.","DOI":"10.37394\/232023.2024.4.7"},{"key":"2274_CR231","doi-asserted-by":"publisher","unstructured":"Aslan, S., and Bing\u00f6l, H., Epileptic seizure detection from EEG signals with recurrent neural networks based classification model. J. Phys. Chem. Funct. Mater. 7(2):14\u201321, 2024. https:\/\/doi.org\/10.54565\/jphcfum.1500546. Number: 2 Publisher: Niyazi BULUT. Accessed 12 Feb 2025.","DOI":"10.54565\/jphcfum.1500546"},{"key":"2274_CR232","doi-asserted-by":"publisher","unstructured":"Anita, M., and Meena\u00a0Kowshalya, A., Automatic epileptic seizure detection using MSA-DCNN and LSTM techniques with EEG signals. Expert Syst. Appl. 238:121727, 2024. https:\/\/doi.org\/10.1016\/j.eswa.2023.121727. Accessed 12 Feb 2025.","DOI":"10.1016\/j.eswa.2023.121727"},{"key":"2274_CR233","doi-asserted-by":"publisher","unstructured":"Aniruddha Prabhu\u00a0BS, P., Chandradeep, B., and Taranath\u00a0N, L., Epileptic seizure detection and analysis using machine learning. In: 2024 Second International Conference on Advances in Information Technology (ICAIT), vol. 1, pp. 1\u20135, 2024. https:\/\/doi.org\/10.1109\/ICAIT61638.2024.10690319. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10690319. Accessed 13 Feb 2025.","DOI":"10.1109\/ICAIT61638.2024.10690319"},{"key":"2274_CR234","doi-asserted-by":"publisher","unstructured":"Anandan, P., and Anbuselvan, N., Epileptic seizure prediction on EEG data using a firefly algorithm trained with deep neural networks. In: 2024 International Conference on Integrated Circuits and Communication Systems (ICICACS), pp. 1\u20135, 2024. https:\/\/doi.org\/10.1109\/ICICACS60521.2024.10498609. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10498609. Accessed 12 Feb 2025.","DOI":"10.1109\/ICICACS60521.2024.10498609"},{"key":"2274_CR235","doi-asserted-by":"publisher","unstructured":"Alsaadan, A., Alzamel, M., and Hussain, M., LMPSeizNet: A lightweight multiscale pyramid convolutional neural network for epileptic seizure detection on EEG brain signals. Mathematics 12(23):3648, 2024. https:\/\/doi.org\/10.3390\/math12233648. Number: 23 Publisher: Multidisciplinary Digital Publishing Institute. Accessed 12 Feb 2025.","DOI":"10.3390\/math12233648"},{"key":"2274_CR236","doi-asserted-by":"publisher","unstructured":"Alqirshi, R., and Belhaouari, S. B., EEG-based patient independent epileptic seizure detection using GCN-BRF. In: Deep learning theory and applications, pp. 351\u2013366. Springer. https:\/\/doi.org\/10.1007\/978-3-031-66705-3_23.","DOI":"10.1007\/978-3-031-66705-3_23"},{"key":"2274_CR237","unstructured":"Alizadeh, G., Rezaii, T. Y., and Meshgini, S., Epileptic seizure prediction using one channel EEG signal and 2 D-convolutional neural networks. Neurol. Disord. 12, 2024."},{"key":"2274_CR238","doi-asserted-by":"publisher","unstructured":"Ahmad, I., Zhu, M., Liu, Z., Shabaz, M., Ullah, I., Tong, M. C. F., Sambas, A., Men, L., Chen, Y., and Chen, S., Multi-feature fusion-based convolutional neural networks for EEG epileptic seizure prediction in consumer internet of things. IEEE Trans. Consum. Electron. 70(3):5631\u20135643, 2024. https:\/\/doi.org\/10.1109\/TCE.2024.3363166. Accessed 12 Feb 2025.","DOI":"10.1109\/TCE.2024.3363166"},{"key":"2274_CR239","doi-asserted-by":"publisher","unstructured":"Ahmad, I., Yao, C., Li, L., Chen, Y., Liu, Z., Ullah, I., Shabaz, M., Wang, X., Huang, K., Li, G., Zhao, G., Samuel, O. W., and Chen, S., An efficient feature selection and explainable classification method for EEG-based epileptic seizure detection. J. Inf. Secur. Appl. 80:103654, 2024. https:\/\/doi.org\/10.1016\/j.jisa.2023.103654. Accessed 12 Feb 2025.","DOI":"10.1016\/j.jisa.2023.103654"},{"key":"2274_CR240","doi-asserted-by":"publisher","unstructured":"Ahmad, I., Liu, Z., Li, L., Ullah, I., Aboyeji, S. T., Wang, X., Samuel, O. W., Li, G., Tao, Y., Chen, Y., and Chen, S., Robust epileptic seizure detection based on biomedical signals using an advanced multi-view deep feature learning approach. IEEE J. Biomed. Health Inform. 28(10):5742\u20135754, 2024. https:\/\/doi.org\/10.1109\/JBHI.2024.3396130. Accessed 12 Feb 2025.","DOI":"10.1109\/JBHI.2024.3396130"},{"key":"2274_CR241","doi-asserted-by":"publisher","unstructured":"Adusumilli, V., and Bee, M. K. M., Epileptic seizure detection and classification of EEG signal using k-nearest neighbors (KNN) compared with ANFIS-adaptive network-based fuzzy inference system. AIP Conf. Proc. 2816(1):030001, 2024. https:\/\/doi.org\/10.1063\/5.0186409. Accessed 12 Feb 2025.","DOI":"10.1063\/5.0186409"},{"key":"2274_CR242","doi-asserted-by":"publisher","unstructured":"Abdulwahhab, A. H., Abdulaal, A. H., Thary\u00a0Al-Ghrairi, A. H., Mohammed, A. A., and Valizadeh, M., Detection of epileptic seizure using EEG signals analysis based on deep learning techniques. Chaos, Solitons Fractals 181:114700, 2024. https:\/\/doi.org\/10.1016\/j.chaos.2024.114700. Accessed 12 Feb 2025.","DOI":"10.1016\/j.chaos.2024.114700"},{"key":"2274_CR243","doi-asserted-by":"publisher","unstructured":"Abdallah, T., Jrad, N., Hajjar, S. E., Abdallah, F., Humeau-Heurtier, A., and Van\u00a0Bogaert, P., A novel unsupervised approach for accurate epileptic seizure detection. In: 2024 32nd European Signal Processing Conference (EUSIPCO), pp. 1426\u20131430. https:\/\/doi.org\/10.23919\/EUSIPCO63174.2024.10715300. ISSN: 2076-1465. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10715300. Accessed 27 Mar 2025.","DOI":"10.23919\/EUSIPCO63174.2024.10715300"},{"key":"2274_CR244","doi-asserted-by":"publisher","unstructured":"Abdallah, T., Jrad, N., Abdallah, F., Humeau-Heurtier, A., El\u00a0Howayek, E., and Van\u00a0Bogaert, P., Cross-site generalization using attention layer for epileptic seizure detection. In: 2024 32nd European Signal Processing Conference (EUSIPCO), pp. 1571\u20131575. https:\/\/doi.org\/10.23919\/EUSIPCO63174.2024.10714945. ISSN: 2076-1465. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10714945. Accessed 27 Mar 2025.","DOI":"10.23919\/EUSIPCO63174.2024.10714945"},{"key":"2274_CR245","doi-asserted-by":"publisher","unstructured":"Yalabaka, S., Sneha, B., Prasad, C.R., Vineetha, B., Srinith, S., and Revanth, N., A comprehensive analysis of ML and DL approaches for epileptic seizure prediction. In: 2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET), pp. 1\u20136, 2024. https:\/\/doi.org\/10.1109\/ACROSET62108.2024.10743928. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10743928. Accessed 03 Mar 2025.","DOI":"10.1109\/ACROSET62108.2024.10743928"},{"key":"2274_CR246","doi-asserted-by":"publisher","unstructured":"Xu, J., Yan, K., Deng, Z., Yang, Y., Liu, J.-X., Wang, J., and Yuan, S., EEG-based epileptic seizure detection using deep learning techniques: A survey. Neurocomputing 610, 2024. https:\/\/doi.org\/10.1016\/j.neucom.2024.128644. Accessed 26 Feb 2025.","DOI":"10.1016\/j.neucom.2024.128644"},{"key":"2274_CR247","doi-asserted-by":"publisher","unstructured":"Wang, Z., Song, X., Chen, L., Nan, J., Sun, Y., Pang, M., Zhang, K., Liu, X., and Ming, D., Research progress of epileptic seizure prediction methods based on EEG. Cogn. Neurodyn. 18(5):2731\u20132750, 2024. https:\/\/doi.org\/10.1007\/s11571-024-10109-w. Accessed 03 Mar 2025.","DOI":"10.1007\/s11571-024-10109-w"},{"key":"2274_CR248","doi-asserted-by":"publisher","unstructured":"Viswanath, J., Annamalai, S., and Ramesh, S., Epileptic seizure prediction through ML and DL models: A survey. In: 2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 1641\u20131648, 2024. https:\/\/doi.org\/10.1109\/ICECA63461.2024.10800774. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10800774. Accessed 03 Mar 2025.","DOI":"10.1109\/ICECA63461.2024.10800774"},{"key":"2274_CR249","doi-asserted-by":"publisher","unstructured":"Velvizhi, V. A., Anbarasan, M., Gayathiri, S., Bakkiyalakshmi, S., Harini, A., and Supirya, D., Comparative analysis of epileptic seizure detection techniques. In: 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), pp. 1\u20134, 2024. https:\/\/doi.org\/10.1109\/ICPECTS62210.2024.10780133. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10780133. Accessed 03 Mar 2025.","DOI":"10.1109\/ICPECTS62210.2024.10780133"},{"key":"2274_CR250","doi-asserted-by":"publisher","unstructured":"Thakare, V., and Ranawat, R., Machine learning techniques in epileptic seizure detection: A comprehensive review. In: 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1\u20135, 2024. https:\/\/doi.org\/10.1109\/ICCCNT61001.2024.10724979. ISSN: 2473-7674. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10724979. Accessed 03 Mar 2025.","DOI":"10.1109\/ICCCNT61001.2024.10724979"},{"key":"2274_CR251","doi-asserted-by":"publisher","unstructured":"Shafiezadeh, S., Duma, G. M., Pozza, M., and Testolin, A., A systematic review of cross-patient approaches for EEG epileptic seizure prediction. J. Neural Eng. 21(6):061004, 2024. https:\/\/doi.org\/10.1088\/1741-2552\/ad9682. Publisher: IOP Publishing. Accessed 26 Feb 2025.","DOI":"10.1088\/1741-2552\/ad9682"},{"key":"2274_CR252","doi-asserted-by":"publisher","unstructured":"Marinis, M., Vrochidou, E., and Papakostas, G. A., EEG channel selection for epileptic seizure prediction. In: 2024 Panhellenic Conference on Electronics & Telecommunications (PACET), pp. 1\u20134, 2024. https:\/\/doi.org\/10.1109\/PACET60398.2024.10497022. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10497022. Accessed 03 Mar 2025.","DOI":"10.1109\/PACET60398.2024.10497022"},{"key":"2274_CR253","unstructured":"Khandekar, D., and Patil, S. P., Prediction of epileptic seizure from EEG signal by DWT and ANN technique-A review. J. Electron. Eng. Commun. Eng., 2024. Publisher: enrichedpublications.com Type: PDF."},{"key":"2274_CR254","doi-asserted-by":"publisher","unstructured":"Fatma, N., Singh, P., and Siddiqui, M. K., Extensive review of epileptic seizure detection techniques: Performance, achievements, and future directions. In: 2024 IEEE International Conference on Communication, Computing and Signal Processing (IICCCS), pp. 1\u20134, 2024. https:\/\/doi.org\/10.1109\/IICCCS61609.2024.10763652. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10763652. Accessed 13 Feb 2025.","DOI":"10.1109\/IICCCS61609.2024.10763652"},{"key":"2274_CR255","doi-asserted-by":"publisher","unstructured":"Devi, S. V., and Pallavi, R., Analysis of epileptic seizure detection using deep learning algorithms. In: 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), pp. 01\u201305, 2024. https:\/\/doi.org\/10.1109\/ICDCECE60827.2024.10548098. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10548098. Accessed 13 Feb 2025.","DOI":"10.1109\/ICDCECE60827.2024.10548098"},{"key":"2274_CR256","doi-asserted-by":"publisher","unstructured":"Dash, D. P., Kolekar, M., Chakraborty, C., and Khosravi, M. R., 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):16\u201311629, 2024. https:\/\/doi.org\/10.1145\/3552512. Accessed 13 Feb 2025.","DOI":"10.1145\/3552512"},{"key":"2274_CR257","doi-asserted-by":"publisher","unstructured":"Dan, J., Validation of artificial intelligence for epileptic seizure detection: Moving from research into the clinic. Dev. Med. Child Neurol., 2024. https:\/\/doi.org\/10.1111\/dmcn.16002. Publisher: infoscience.epfl.ch Type: PDF.","DOI":"10.1111\/dmcn.16002"},{"key":"2274_CR258","doi-asserted-by":"publisher","unstructured":"Costa, G., Teixeira, C., and Pinto, M. F., Comparison between epileptic seizure prediction and forecasting based on machine learning. Sci. Rep. 14(1):5653, 2024. https:\/\/doi.org\/10.1038\/s41598-024-56019-z. Publisher: Nature Publishing Group. Accessed 13 Feb 2025.","DOI":"10.1038\/s41598-024-56019-z"},{"key":"2274_CR259","doi-asserted-by":"publisher","unstructured":"Choudhary, S. K., and Bera, T. K., A systematic review of deep learning algorithms utilising electroencephalography signals for epileptic seizure detection. International J. Biomed. Eng. Technol. 46(3):228\u2013262, 2024. https:\/\/doi.org\/10.1504\/IJBET.2024.142531. Publisher: Inderscience Publishers. Accessed 13 Feb 2025.","DOI":"10.1504\/IJBET.2024.142531"},{"key":"2274_CR260","doi-asserted-by":"publisher","unstructured":"Cherian, R., and Kanaga\u00a0E, G. M., Unleashing the potential of spiking neural networks for epileptic seizure detection: A comprehensive review. Neurocomputing 598:127934, 2024. https:\/\/doi.org\/10.1016\/j.neucom.2024.127934. Accessed 13 Feb 2025.","DOI":"10.1016\/j.neucom.2024.127934"},{"key":"2274_CR261","unstructured":"Arora, S., and Chugh, N., EEG Signal classification for epileptic seizure detection: A review of machine learning approaches. J. Multi Discip. Eng. Technol., 2024. Publisher: jmdet.com Type: PDF."},{"key":"2274_CR262","doi-asserted-by":"publisher","unstructured":"Yan, T., Zhang, M., Chen, H., Wan, S., Shang, K., Zhang, H., Cao, X., Lin, X., and Dai, Q., EEG opto-processor: Epileptic seizure detection using diffractive photonic computing units. Engineering 35:56\u201366, 2024. https:\/\/doi.org\/10.1016\/j.eng.2024.01.008. Accessed 03 Mar 2025.","DOI":"10.1016\/j.eng.2024.01.008"},{"key":"2274_CR263","doi-asserted-by":"publisher","unstructured":"Wang, H., Zhang, L., Xiao, E., Wang, X., Wang, Z., and Xu, R., RRAM-based bio-inspired circuits for mobile epileptic correlation extraction and seizure prediction. [eess], 2024. https:\/\/doi.org\/10.48550\/arXiv.2407.19841. arXiv:2407.19841. Accessed 03 Mar 2025.","DOI":"10.48550\/arXiv.2407.19841"},{"key":"2274_CR264","doi-asserted-by":"publisher","unstructured":"Kawaguchi, S., Banerjee, A., Hirotani, J., and Tsuchiya, T., MEMS resonator-based reservoir computing for epileptic seizure prediction. In: 2024 IEEE International Meeting for Future of Electron Devices, Kansai (IMFEDK), pp. 1\u20132, 2024. https:\/\/doi.org\/10.1109\/IMFEDK64776.2024.10814263. ISSN: 2836-9947. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10814263. Accessed 03 Mar 2025.","DOI":"10.1109\/IMFEDK64776.2024.10814263"},{"key":"2274_CR265","doi-asserted-by":"publisher","unstructured":"Murali\u00a0Krishn, Y., and Vinay\u00a0Kumar, P., Efficient EEG motion artifact elimination framework for ambulatory epileptic seizure detection application. Biomed. Phys. Eng. Express 10(3):035005, 2024. https:\/\/doi.org\/10.1088\/2057-1976\/ad2ff4. Publisher: IOP Publishing. Accessed 03 Mar 2025.","DOI":"10.1088\/2057-1976\/ad2ff4"},{"key":"2274_CR266","unstructured":"Lemoine, L., and Pham, N., Epileptic seizure detection with Tiny Machine Learning-a preliminary study. orca.cardiff.ac.uk. Type: PDF, 2024. https:\/\/orca.cardiff.ac.uk\/id\/eprint\/174851\/1\/S4_P2_Lemoine_Seizure.pdf."},{"key":"2274_CR267","doi-asserted-by":"publisher","unstructured":"Khan, A., Varnosfaderani, S. M., Alhawari, M., and Tutuncuoglu, G., Benchmarking RRAM crossbar arrays for epileptic seizure prediction. In: 2024 IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1314\u20131318, 2024. https:\/\/doi.org\/10.1109\/MWSCAS60917.2024.10658668. ISSN: 1558-3899. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10658668. Accessed 03 Mar 2025.","DOI":"10.1109\/MWSCAS60917.2024.10658668"},{"key":"2274_CR268","doi-asserted-by":"publisher","unstructured":"Jagtap, S.S., Saranya, G., Sathish, M., and Priyadharshini, S., Purnima: GPS based IoT module for vehicle safety in epileptic seizure detection and alcohol monitoring. In: 2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN), pp. 1036\u20131042, 2024. https:\/\/doi.org\/10.1109\/ICPCSN62568.2024.00172. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10607699. Accessed 13 Feb 2025.","DOI":"10.1109\/ICPCSN62568.2024.00172"},{"key":"2274_CR269","doi-asserted-by":"publisher","unstructured":"Hussein, A. M., Alomari, S. A., Almomani, M. H., Zitar, R. A., Saleem, K., Smerat, A., Nusier, S., and Abualigah, L., A smart IoT-cloud framework with adaptive deep learning for real-time epileptic seizure detection. Circ. Syst. Signal Process., 2024. https:\/\/doi.org\/10.1007\/s00034-024-02919-4. Accessed 13 Feb 2025.","DOI":"10.1007\/s00034-024-02919-4"},{"key":"2274_CR270","doi-asserted-by":"publisher","unstructured":"Dhandayuthapani, B. V., Dudeja, D., Duggal, S., Sharma, S., Jain, A., and Pareek, P. K., Cyber security based application-specific integrated circuit for epileptic seizure prediction using convolutional neural network. J. Intell. Syst. Internet Things (Issue 1) 234\u2013250, 2024. https:\/\/doi.org\/10.54216\/JISIoT.130117. Publisher: American Scientific Publishing Group (ASPG). Accessed 13 Feb 2025.","DOI":"10.54216\/JISIoT.130117"},{"key":"2274_CR271","doi-asserted-by":"publisher","unstructured":"Ali, E., Angelova, M., and Karmakar, C., Epileptic seizure detection using CHB-MIT dataset: The overlooked perspectives. R. Soc. Open Sc., 2024. https:\/\/doi.org\/10.1098\/rsos.230601. Publisher: The Royal Society. Accessed 12 Feb 2025.","DOI":"10.1098\/rsos.230601"},{"key":"2274_CR272","doi-asserted-by":"publisher","unstructured":"Ulrich, T., Sparse wavelet representation of interictal epileptic activity for postsurgical seizure outcome prediction. Master\u2019s thesis, University of Zurich, 2024. https:\/\/doi.org\/10.5167\/uzh-270207. https:\/\/www.zora.uzh.ch\/id\/eprint\/270207\/. Accessed 03 Mar 2025.","DOI":"10.5167\/uzh-270207"},{"key":"2274_CR273","doi-asserted-by":"publisher","unstructured":"Tong, P. F., Dong, B., Zeng, X., Chen, L., and Chen, S. X., Detection of interictal epileptiform discharges using transformer based deep neural network for patients with self-limited epilepsy with centrotemporal spikes. Biomed. Signal Process. Control 101:107238, 2025. https:\/\/doi.org\/10.1016\/j.bspc.2024.107238. Accessed 31 Mar 2025.","DOI":"10.1016\/j.bspc.2024.107238"},{"key":"2274_CR274","doi-asserted-by":"publisher","unstructured":"Rao, W., Wang, H., Zhuang, K., Guo, J., Gu, P., Zhang, L., Wang, X., Jiang, J., and Chen, D., Automatic multi-label classification of Interictal Epileptiform Discharges (IED) detection based on scalp EEG and transformer. In: Huang, D. -S., Zhang, X., and Pan, Y. (Eds.), Advanced Intelligent Computing Technology and Applications, pp. 106\u2013117. Springer, Singapore, 2024. https:\/\/doi.org\/10.1007\/978-981-97-5581-3_9","DOI":"10.1007\/978-981-97-5581-3_9"},{"key":"2274_CR275","doi-asserted-by":"publisher","unstructured":"Munia, M. S., Hosseini, M., Nourani, M., and Harvey, J., Interictal epileptiform discharge detection using time-frequency analysis and transfer learning. In: 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1\u20134, 2024.https:\/\/doi.org\/10.1109\/EMBC53108.2024.10782120. ISSN: 2694-0604. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10782120. Accessed 31 Mar 2025.","DOI":"10.1109\/EMBC53108.2024.10782120"},{"key":"2274_CR276","doi-asserted-by":"publisher","unstructured":"Inoue, I., Zhao, X., Komeiji, S., Yoshida, N., Sugano, H., Nakajima, M., and Tanaka, T., LightIED: Explainable AI with light CNN for interictal epileptiform discharge detection. In: 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1\u20134, 2024. https:\/\/doi.org\/10.1109\/EMBC53108.2024.10782804. ISSN: 2694-0604. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10782804. Accessed 31 Mar 2025.","DOI":"10.1109\/EMBC53108.2024.10782804"},{"key":"2274_CR277","doi-asserted-by":"publisher","unstructured":"Sousa, A. M. A., Putten, M. J. A. M., Berg, S., and Amir\u00a0Haeri, M., Detection of interictal epileptiform discharges with semi-supervised deep learning. Biomed. Signal Process. Control 88:105610, 2024. https:\/\/doi.org\/10.1016\/j.bspc.2023.105610. Accessed 31 Mar 2025.","DOI":"10.1016\/j.bspc.2023.105610"},{"key":"2274_CR278","doi-asserted-by":"publisher","unstructured":"Costa, F., Schaft, E. V., Huiskamp, G., Aarnoutse, E. J., Klooster, M. A., Krayenb\u00fchl, N., Ramantani, G., Zijlmans, M., Indiveri, G., and Sarnthein, J., Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework. Nat. Commun. 15(1):3255, 2024. https:\/\/doi.org\/10.1038\/s41467-024-47495-y. Publisher: Nature Publishing Group. Accessed 31 Mar 2025.","DOI":"10.1038\/s41467-024-47495-y"},{"key":"2274_CR279","doi-asserted-by":"publisher","unstructured":"Borges Camargo\u00a0Diniz, J., Silva\u00a0Santana, L., Leite, M., Silva\u00a0Santana, J. L., Magalh\u00e3es\u00a0Costa, S. I., Martins\u00a0Castro, L. H., and Mota\u00a0Telles, J. P., Advancing epilepsy diagnosis: A meta-analysis of artificial intelligence approaches for interictal epileptiform discharge detection. Seizure: Eur. J. Epilepsy 122:80\u201386, 2024. https:\/\/doi.org\/10.1016\/j.seizure.2024.09.019. Accessed 31 Mar 2025.","DOI":"10.1016\/j.seizure.2024.09.019"},{"key":"2274_CR280","doi-asserted-by":"publisher","unstructured":"Balzekas, I., Trzasko, J., Yu, G., Richner, T.J., Mivalt, F., Sladky, V., Gregg, N. M., Gompel, J. V., Miller, K., Croarkin, P. E., Kremen, V., and Worrell, G. A., Method for cycle detection in sparse, irregularly sampled, long-term neuro-behavioral timeseries: Basis pursuit denoising with polynomial detrending of long-term, inter-ictal epileptiform activity. PLOS Comput. Biol. 20(4):1011152, 2024. https:\/\/doi.org\/10.1371\/journal.pcbi.1011152. Publisher: Public Library of Science. Accessed 31 Mar 2025.","DOI":"10.1371\/journal.pcbi.1011152"},{"key":"2274_CR281","doi-asserted-by":"publisher","unstructured":"Abdi-Sargezeh, B., Shirani, S., Sanei, S., Took, C. C., Geman, O., Alarcon, G., and Valentin, A., A review of signal processing and machine learning techniques for interictal epileptiform discharge detection. Comput. Biol. Med. 168:107782, 2024. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.107782. Accessed 31 Mar 2025.","DOI":"10.1016\/j.compbiomed.2023.107782"}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02274-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10916-025-02274-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02274-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T06:56:21Z","timestamp":1760943381000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10916-025-02274-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,20]]},"references-count":281,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2274"],"URL":"https:\/\/doi.org\/10.1007\/s10916-025-02274-0","relation":{},"ISSN":["1573-689X"],"issn-type":[{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,20]]},"assertion":[{"value":"11 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical Trial Number"}}],"article-number":"142"}}