{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T17:47:51Z","timestamp":1785520071584,"version":"3.56.0"},"reference-count":35,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T00:00:00Z","timestamp":1752796800000},"content-version":"vor","delay-in-days":198,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Signal Processing"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>Epilepsy is a neurological disorder characterized by frequent seizures and abnormal brain activity. It is typically diagnosed by examining electroencephalogram (EEG) recordings from epilepsy patients. Early detection and careful monitoring of children with epilepsy are crucial to preventing damaging spikes before the onset of the first seizure. Traditionally, this condition is examined manually by medical experts, a time\u2010consuming process, especially during prolonged recordings. Therefore, an automated method for diagnosing focal (abnormal) EEG signals is essential. This study proposes an efficient model to classify and provide insights into focal and nonfocal stages. The model is based on an Inception ResNet v2 architecture pooled with a Deep Adagrad (Adaptive Gradient Descent Algorithm) Long Short\u2010Term Memory (LSTM) network. EEG signal features are extracted using the Inception and ResNet layers, and significant features are then trained with a deep convolutional neural network (CNN) integrated with an Adagrad\u2010optimized LSTM layer to classify focal and nonfocal EEG signals. The results demonstrate that the model achieves an impressive 99.76% accuracy in automatically detecting epilepsy abnormalities.<\/jats:p>","DOI":"10.1049\/sil2\/7543401","type":"journal-article","created":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T11:05:01Z","timestamp":1752836701000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Automatic Epilepsy Seizure Classification Using EEG Signals Based on the CNN\u2010LSTM Model"],"prefix":"10.1049","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2098-0539","authenticated-orcid":false,"given":"C. Ruth","family":"Vinutha","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1703-9767","authenticated-orcid":false,"given":"M. S. P.","family":"Subathra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0304-495X","authenticated-orcid":false,"given":"S. Thomas","family":"George","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1825-8427","authenticated-orcid":false,"given":"Geno","family":"Peter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3572-2885","authenticated-orcid":false,"given":"Albert Alexander","family":"Stonier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6125-4248","authenticated-orcid":false,"given":"N. J.","family":"Sairamya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9264-7589","authenticated-orcid":false,"given":"J.","family":"Prasanna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5646-2138","authenticated-orcid":false,"given":"Vivekananda","family":"Ganji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2025,7,18]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"crossref","unstructured":"GuptaS. BaggaS. MaheshkarV. andBhatiaM. P. S. Detection of Epileptic Seizures Using EEG Signals 2020 International Conference on Artificial Intelligence and Signal Processing (AISP) 2020 IEEE 1\u20135.","DOI":"10.1109\/AISP48273.2020.9073157"},{"key":"e_1_2_10_2_2","doi-asserted-by":"crossref","unstructured":"KhanI. M. KhanM. M. andFarooqO. Epileptic Seizure Detection Using EEG Signals 2022 5th International Conference on Computing and Informatics (ICCI) 2022 IEEE 111\u2013117.","DOI":"10.1109\/ICCI54321.2022.9756061"},{"key":"e_1_2_10_3_2","doi-asserted-by":"crossref","unstructured":"BrariZ.andBelghithS. A New Method for the Detection of Epilepsy and Epileptic Seizures Based on the Variance of EEG Signals and its Derivatives With a Simple Kernel Trick 2020 4th International Conference on Advanced Systems and Emergent Technologies (IC_ASET) 2020 IEEE 261\u2013265.","DOI":"10.1109\/IC_ASET49463.2020.9318218"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2020.3008792"},{"key":"e_1_2_10_5_2","doi-asserted-by":"crossref","unstructured":"ShekokarK.andDourS. Epileptic Seizure Detection Based on LSTM Model Using Noisy EEG Signals 2021 5th International Conference on Electronics Communication and Aerospace Technology (ICECA) 2021 IEEE 292\u2013296.","DOI":"10.1109\/ICECA52323.2021.9675941"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40708-020-00105-1"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11571-022-09816-z"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph18115780"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13755-020-00129-1"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13638-020-01810-5"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.seizure.2019.02.001"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.5455\/aim.2021.29.104-107"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40846-019-00467-w"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2018.00095"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3077578"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2970012"},{"key":"e_1_2_10_17_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21227710"},{"key":"e_1_2_10_18_2","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci11050668"},{"key":"e_1_2_10_19_2","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/6849360"},{"key":"e_1_2_10_20_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-78784-3"},{"key":"e_1_2_10_21_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-15830-2"},{"key":"e_1_2_10_22_2","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2021.650050"},{"key":"e_1_2_10_23_2","first-page":"2367","article-title":"Automated Detection of Epileptic EEG Signals Using Recurrence Plots Based Feature Extraction With Transfer Learning","volume":"28","author":"Goel S.","year":"2022","journal-title":"Application of soft computing"},{"key":"e_1_2_10_24_2","doi-asserted-by":"crossref","unstructured":"AltayY. A. KremlevA. S. andZimenkoK. A. A New ECG Signal Processing Method Based on Wide-Band Notch Filter 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) 2020 IEEE 1464\u20131469.","DOI":"10.1109\/EIConRus49466.2020.9039403"},{"key":"e_1_2_10_25_2","first-page":"904","article-title":"Recognition of Plant Species Using Deep Convolutional Feature Extraction","volume":"11","author":"Van Hieu N.","journal-title":"International Journal on Emerging Technologies"},{"key":"e_1_2_10_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105285"},{"key":"e_1_2_10_27_2","doi-asserted-by":"crossref","unstructured":"JiwaniN. GuptaK. SharifM. H. U. AdhikariN. andAfreenN. A LSTM-CNN Model for Epileptic Seizures Detection Using EEG Signal 2022 2nd International Conference on Emerging Smart Technologies and Applications (eSmarTA) 2022 IEEE 1\u20135.","DOI":"10.1109\/eSmarTA56775.2022.9935403"},{"key":"e_1_2_10_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2023.101943"},{"key":"e_1_2_10_29_2","doi-asserted-by":"crossref","unstructured":"ChoiG. ParkC. andKimJ. et al.A Novel Multi-scale 3D CNN With Deep Neural Network for Epileptic Seizure Detection Proceedings of the 2019 IEEE International Conference on Consumer Electronics (ICCE) 2019 Las Vegas NV USA IEEE 1\u20132.","DOI":"10.1109\/ICCE.2019.8661969"},{"key":"e_1_2_10_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.10.108"},{"key":"e_1_2_10_31_2","first-page":"47","volume-title":"ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification","author":"Roy S.","year":"2019"},{"key":"e_1_2_10_32_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/9689821"},{"key":"e_1_2_10_33_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-41537-z"},{"key":"e_1_2_10_34_2","article-title":"Automated Detection of Epilepsy EEG Based on Hybrid Model of CNN and LSTM","volume":"2022","author":"Wang X.","year":"2022","journal-title":"SSRN Electronic Journal"},{"key":"e_1_2_10_35_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11760-022-02318-9"}],"container-title":["IET Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/sil2\/7543401","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/full-xml\/10.1049\/sil2\/7543401","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/sil2\/7543401","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T04:13:55Z","timestamp":1773029635000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/sil2\/7543401"}},"subtitle":[],"editor":[{"given":"Charles Casimiro","family":"Cavalcante","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1049\/sil2\/7543401"],"URL":"https:\/\/doi.org\/10.1049\/sil2\/7543401","archive":["Portico"],"relation":{},"ISSN":["1751-9675","1751-9683"],"issn-type":[{"value":"1751-9675","type":"print"},{"value":"1751-9683","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2024-07-31","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-25","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"7543401"}}