{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T09:11:50Z","timestamp":1778749910093,"version":"3.51.4"},"reference-count":24,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T00:00:00Z","timestamp":1694476800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T00:00:00Z","timestamp":1694476800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Epilepsy is a life threatening neurological disorder. The person with epilepsy suffers from recurrent seizures. Sudden emission of electrical signal in the nerves of the human brain is called seizure event. The most widely used method for diagnosing epilepsy is analysing electroencephalogram signals in short called as EEG signals collected from the scalp of the patient. The EEG data are normally used for seizure detection. If the recurrent seizure signals are detected in the input EEG dataset, then it can be considered as the presence of epilepsy disorder. Manual inspection of seizure signals in the EEG data is a laborious process. An automated system is very crucial for the neurologists to identify seizures. In this paper, an automated seizure detection method is presented using deep learning method, pre\u2010trained convolutional neural network architecture. Freely available EEG dataset from Temple University Hospital database is used for the study. The pre\u2010trained CNN networks, VGGNet and ResNet are used for classifying the seizure activities from non\u2010seizure activities. CNNs are extremely good in learning the features of the input data. A very large dataset from TUH is provided as input to the multiple layers of CNN model. The same data is fed to VGGNet and ResNet models. The results of CNN, VGGNet and ResNet models are assessed using performance metrics accuracy, AUC, precision and recall. All the three models gave extremely good performance compared to state\u2010of\u2010the\u2010art works in the literature. In comparison VGGNet performed with little higher results giving 97% accuracy, 96% AUC, 97% precision and 79% recall.<\/jats:p>","DOI":"10.1111\/exsy.13447","type":"journal-article","created":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T04:58:47Z","timestamp":1694494727000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Detection of epileptic seizure events using pre\u2010trained convolutional neural network,\n                    <scp>VGGNet<\/scp>\n                    and\n                    <scp>ResNet<\/scp>"],"prefix":"10.1111","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6743-7723","authenticated-orcid":false,"given":"D. K.","family":"Thara","sequence":"first","affiliation":[{"name":"Department of Information Science &amp; Engineering Channabasaveshwara Institute of Technology  Tumkur Karnataka India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B. G.","family":"Premasudha","sequence":"additional","affiliation":[{"name":"Department Master of Computer Applications Siddaganga Institute of Technology  Tumkur Karnataka India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Senka","family":"Krivic","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering University of Sarajevo  Sarajevo Bosnia and Herzegovina"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,9,12]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"crossref","unstructured":"Agarwal S. Basu I. Kumar M. Salami P. &Cash S. S.Classification of seizure termination patterns using deep learning on intracranial EEG.2022 44th annual international conference of the IEEE engineering in medicine & biology society (EMBC) Glasgow Scotland United Kingdom 2022 pp. 2933\u20132936.https:\/\/doi.org\/10.1109\/EMBC48229.2022.9871579","DOI":"10.1109\/EMBC48229.2022.9871579"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065718500119"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-11800-6_9"},{"key":"e_1_2_8_5_1","doi-asserted-by":"crossref","unstructured":"Dong Z. &Zhou S.(2022).EEG\u2010based seizure detection using generative model and deep learning.2022 E\u2010health and bioengineering conference (EHB) Iasi Romania pp. 1\u20134.https:\/\/doi.org\/10.1109\/EHB55594.2022.9991438","DOI":"10.1109\/EHB55594.2022.9991438"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2019.101684"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2020.00375"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-78784-3"},{"key":"e_1_2_8_9_1","doi-asserted-by":"crossref","unstructured":"Guan Y. Koerner J. Valiante T. A. Genov R. &O'Leary G.Generative adversarial network\u2010based synthetic seizure dataset augmentation.2021 10th international IEEE\/EMBS conference on neural engineering (NER) 2021 pp. 797\u2013800.https:\/\/doi.org\/10.1109\/NER49283.2021.9441413","DOI":"10.1109\/NER49283.2021.9441413"},{"key":"e_1_2_8_10_1","unstructured":"Ie\u0161mantas T. &Alzbutas R.(2019).Convolutional neural network for detection and classification of seizures in clinical data.https:\/\/arxiv.org\/ftp\/arxiv\/papers\/1903\/1903.08864.pdf"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TLA.2023.10068845"},{"key":"e_1_2_8_12_1","doi-asserted-by":"crossref","unstructured":"Liu Y. Sivathamboo S. Goodin P. Bonnington P. Kwan P. Kuhlmann L. O'Brien T. Perucca P. &Ge Z.(2020).Epileptic seizure detection using convolutional neural network: A multi\u2010biosignal study pp. 1\u20138.https:\/\/doi.org\/10.1145\/3373017.3373055","DOI":"10.1145\/3373017.3373055"},{"key":"e_1_2_8_13_1","doi-asserted-by":"crossref","unstructured":"O'Shea A. Lightbody G. Boylan G. &Temko A.(2017).Neonatal seizure detection using convolutional neural networks. In2017 IEEE 27th international workshop on machine learning for signal processing (MLSP) pp. 1\u20136.https:\/\/doi.org\/10.1109\/MLSP.2017.8168193","DOI":"10.1109\/MLSP.2017.8168193"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/4825767"},{"key":"e_1_2_8_15_1","doi-asserted-by":"crossref","unstructured":"Premasudha B. G. Thara D. K. &Tara K. N.(2022).ML based methods XGBoost and random Forest for crop and fertilizer prediction. In2022 14th international conference on computational intelligence and communication networks (CICN) Al\u2010Khobar Saudi Arabia pp. 492\u2013497.https:\/\/doi.org\/10.1109\/CICN56167.2022.10008234","DOI":"10.1109\/CICN56167.2022.10008234"},{"key":"e_1_2_8_16_1","doi-asserted-by":"crossref","unstructured":"Rathod P. Bhalodiya J. &Naik S.Epilepsy detection using Bi\u2010LSTM with explainable artificial intelligence.2022 IEEE 19th India council international conference (INDICON) Kochi India 2022 pp. 1\u20136.https:\/\/doi.org\/10.1109\/INDICON56171.2022.10039816","DOI":"10.1109\/INDICON56171.2022.10039816"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104566"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106053"},{"issue":"2","key":"e_1_2_8_19_1","first-page":"25","article-title":"A review on computer aided diagnosis of epilepsy using machine learning and deep learning","volume":"6","author":"Thara D. 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