{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T06:54:47Z","timestamp":1770965687841,"version":"3.50.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T00:00:00Z","timestamp":1755129600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T00:00:00Z","timestamp":1755129600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.82172069"],"award-info":[{"award-number":["No.82172069"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["NO.62402455"],"award-info":[{"award-number":["NO.62402455"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012476","name":"Fundamental Research Funds for Central Universities of the Central South University","doi-asserted-by":"publisher","award":["No. 226-2025-00006"],"award-info":[{"award-number":["No. 226-2025-00006"]}],"id":[{"id":"10.13039\/501100012476","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012476","name":"Fundamental Research Funds for Central Universities of the Central South University","doi-asserted-by":"publisher","award":["226-2024-00163"],"award-info":[{"award-number":["226-2024-00163"]}],"id":[{"id":"10.13039\/501100012476","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Zhejiang Province Key Research and Development Plan","award":["No. 2024SSYS0010"],"award-info":[{"award-number":["No. 2024SSYS0010"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Health Inf Sci Syst"],"DOI":"10.1007\/s13755-025-00366-2","type":"journal-article","created":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T05:53:24Z","timestamp":1755150804000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An EEG-based patient-independent epileptic seizure detection method based on domain generative adversarial network"],"prefix":"10.1007","volume":"13","author":[{"given":"Yulang","family":"Feng","sequence":"first","affiliation":[]},{"given":"Tianshu","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Chengkai","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Jianda","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jianhua","family":"Feng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1064-637X","authenticated-orcid":false,"given":"Jingsong","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,14]]},"reference":[{"key":"366_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TSP.2023.3333546","volume":"72","author":"PF Tong","year":"2024","unstructured":"Tong PF, Zhan HX, Chen SX. Ensembled seizure detection based on small training samples. IEEE Trans Signal Process. 2024;72:1\u201314.","journal-title":"IEEE Trans Signal Process"},{"key":"366_CR2","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/S1474-4422(18)30038-3","volume":"17","author":"CE Elger","year":"2018","unstructured":"Elger CE, Hoppe C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. Lancet Neurol. 2018;17:279\u201388.","journal-title":"Lancet Neurol"},{"key":"366_CR3","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065724500515","author":"M Wu","year":"2024","unstructured":"Wu M, Peng H, Liu Z, Wang J. Seizure detection of EEG signals based on multi-channel long- and short-term memory-like spiking neural model. Int J Neural Syst. 2024. https:\/\/doi.org\/10.1142\/S0129065724500515.","journal-title":"Int J Neural Syst"},{"key":"366_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120585","author":"L Abou-Abbas","year":"2023","unstructured":"Abou-Abbas L, Henni K, Jemal I, Mitiche A, Mezghani N. Patient-independent epileptic seizure detection by stable feature selection. Expert Syst Appl. 2023. https:\/\/doi.org\/10.1016\/j.eswa.2023.120585.","journal-title":"Expert Syst Appl"},{"key":"366_CR5","first-page":"103654","volume":"80","author":"I Ahmad","year":"2024","unstructured":"Ahmad I, et al. An efficient feature selection and explainable classification method for EEG-based epileptic seizure detection. J Inf Secur Appl. 2024;80:103654.","journal-title":"J Inf Secur Appl"},{"key":"366_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-019-1234-4","author":"RS Selvakumari","year":"2019","unstructured":"Selvakumari RS, Mahalakshmi M, Prashalee P. Patient-specific seizure detection method using hybrid classifier with optimized electrodes. J Med Syst. 2019. https:\/\/doi.org\/10.1007\/s10916-019-1234-4.","journal-title":"J Med Syst"},{"key":"366_CR7","doi-asserted-by":"publisher","DOI":"10.1186\/s40708-020-00105-1","author":"MK Siddiqui","year":"2020","unstructured":"Siddiqui MK, Menendez RM, Huang X, Hussain N. A review of epileptic seizure detection using machine learning classifiers. Brain Inform. 2020. https:\/\/doi.org\/10.1186\/s40708-020-00105-1.","journal-title":"Brain Inform"},{"key":"366_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103671","author":"S Yang","year":"2020","unstructured":"Yang S, et al. Selection of features for patient-independent detection of seizure events using scalp EEG signals. Comput Biol Med. 2020. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103671.","journal-title":"Comput Biol Med"},{"key":"366_CR9","doi-asserted-by":"publisher","unstructured":"Nandini D, Yadav J, Rani A, Singh V, Kravchenko OV. Efficient patient independent seizure detection system using WAF based hybrid feature extraction method and XGBoost classifier. In: 2022 IEEE Delhi section conference, DELCON 2022. 2022. p. 1\u20135. https:\/\/doi.org\/10.1109\/DELCON54057.2022.9753599.","DOI":"10.1109\/DELCON54057.2022.9753599"},{"key":"366_CR10","first-page":"1","volume":"XX","author":"Y Sun","year":"2022","unstructured":"Sun Y, et al. Continuous seizure detection based on transformer and long-term iEEG. IEEE J Biomed Health Inform. 2022;XX:1\u201310.","journal-title":"IEEE J Biomed Health Inform"},{"key":"366_CR11","doi-asserted-by":"publisher","first-page":"105165","DOI":"10.1016\/j.bspc.2023.105165","volume":"86","author":"W Huang","year":"2023","unstructured":"Huang W, Xu H, Yu Y. MRP-net: seizure detection method based on modified recurrence plot and additive attention convolution neural network. Biomed Signal Process Control. 2023;86:105165.","journal-title":"Biomed Signal Process Control"},{"key":"366_CR12","doi-asserted-by":"publisher","first-page":"016037","DOI":"10.1088\/1741-2552\/acb1d9","volume":"20","author":"X Si","year":"2023","unstructured":"Si X, et al. Patient-independent seizure detection based on long-term iEEG and a novel lightweight CNN. J Neural Eng. 2023;20:016037.","journal-title":"J Neural Eng"},{"key":"366_CR13","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065724500424","author":"H Lv","year":"2024","unstructured":"Lv H, et al. Seizure detection based on lightweight inverted residual attention network. Int J Neural Syst. 2024. https:\/\/doi.org\/10.1142\/S0129065724500424.","journal-title":"Int J Neural Syst"},{"key":"366_CR14","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1109\/TNSRE.2020.2973434","volume":"28","author":"S Network","year":"2020","unstructured":"Network S, et al. Epileptic seizure detection in EEG signals using a unified temporal-spectral squeeze-and-excitation network. IEEE Trans Neural Syst Rehabil Eng. 2020;28:782\u201394.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"366_CR15","doi-asserted-by":"publisher","first-page":"108665","DOI":"10.1016\/j.engappai.2024.108665","volume":"133","author":"H Li","year":"2024","unstructured":"Li H, et al. End-to-end model for automatic seizure detection using supervised contrastive learning. Eng Appl Artif Intell. 2024;133:108665.","journal-title":"Eng Appl Artif Intell"},{"key":"366_CR16","doi-asserted-by":"publisher","first-page":"115551","DOI":"10.1016\/j.eswa.2021.115551","volume":"184","author":"S Eldeeb","year":"2021","unstructured":"Eldeeb S, et al. Person-dependent seizure detection using statistical CUSUM detector: preliminary results. Expert Syst Appl. 2021;184:115551.","journal-title":"Expert Syst Appl"},{"key":"366_CR17","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1111\/epi.16815","volume":"62","author":"RM Pressler","year":"2021","unstructured":"Pressler RM, et al. The ILAE classification of seizures and the epilepsies: modification for seizures in the neonate. Position paper by the ILAE Task Force on Neonatal Seizures. Epilepsia. 2021;62:615\u201328.","journal-title":"Epilepsia"},{"key":"366_CR18","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1109\/JBHI.2021.3100297","volume":"26","author":"T Dissanayake","year":"2022","unstructured":"Dissanayake T, Fernando T, Denman S. Independent epileptic seizure prediction using scalp EEG signals. IEEE J Biomed Health Inform. 2022;26:527\u201338.","journal-title":"IEEE J Biomed Health Inform"},{"key":"366_CR19","doi-asserted-by":"publisher","unstructured":"Zhu Y, Wang MD. Automated Seizure detection using transformer models on multi-channel EEGs. In: BHI 2023\u2014IEEE-EMBS international conference on biomedical and health informatics, proceedings. 2023. p. 1\u20136. https:\/\/doi.org\/10.1109\/BHI58575.2023.10313440.","DOI":"10.1109\/BHI58575.2023.10313440"},{"key":"366_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/S0129065721500519","volume":"32","author":"G Liu","year":"2022","unstructured":"Liu G, Tian L, Zhou W. Patient-independent seizure detection based on channel-perturbation convolutional neural network and bidirectional long short-term memory. Int J Neural Syst. 2022;32:1\u201317.","journal-title":"Int J Neural Syst"},{"key":"366_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-020-0264-0","volume":"3","author":"K Saab","year":"2020","unstructured":"Saab K, Dunnmon J, R\u00e9 C, Rubin D, Lee-Messer C. Weak supervision as an efficient approach for automated seizure detection in electroencephalography. NPJ Digit Med. 2020;3:1\u201312.","journal-title":"NPJ Digit Med"},{"key":"366_CR22","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1109\/TNSRE.2022.3229066","volume":"31","author":"X Cui","year":"2023","unstructured":"Cui X, Cao J, Lai X, Jiang T, Gao F. Cluster embedding joint-probability-discrepancy transfer for cross-subject seizure detection. IEEE Trans Neural Syst Rehabil Eng. 2023;31:593\u2013605.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"366_CR23","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/978-3-319-58347-1_10","volume":"17","author":"Y Ganin","year":"2017","unstructured":"Ganin Y, et al. Domain-adversarial training of neural networks. Adv Comput Vis Pattern Recognit. 2017;17:189\u2013209.","journal-title":"Adv Comput Vis Pattern Recognit"},{"key":"366_CR24","doi-asserted-by":"publisher","first-page":"2852","DOI":"10.1109\/JBHI.2020.2971610","volume":"24","author":"X Zhang","year":"2020","unstructured":"Zhang X, et al. Adversarial representation learning for robust patient-independent epileptic seizure detection. IEEE J Biomed Health Inform. 2020;24:2852\u20139.","journal-title":"IEEE J Biomed Health Inform"},{"key":"366_CR25","doi-asserted-by":"publisher","first-page":"2147","DOI":"10.1109\/JBHI.2021.3138852","volume":"26","author":"L Duan","year":"2022","unstructured":"Duan L, et al. An automatic method for epileptic seizure detection based on deep metric learning. IEEE J Biomed Health Inform. 2022;26:2147\u201357.","journal-title":"IEEE J Biomed Health Inform"},{"key":"366_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/6184713","volume":"2018","author":"Z Chao","year":"2018","unstructured":"Chao Z, Pu F, Yin Y, Han B, Chen X. Research on real-time local rainfall prediction based on MEMS sensors. J Sens. 2018;2018:1\u20139.","journal-title":"J Sens"},{"key":"366_CR27","doi-asserted-by":"publisher","unstructured":"Guttag J. CHB-MIT scalp EEG database (version 1.0.0). PhysioNet. 2010. https:\/\/doi.org\/10.13026\/C2K01R","DOI":"10.13026\/C2K01R"},{"key":"366_CR28","doi-asserted-by":"publisher","first-page":"121359","DOI":"10.1016\/j.eswa.2023.121359","volume":"236","author":"Y Xu","year":"2024","unstructured":"Xu Y, Yang J, Ming W, Wang S, Sawan M. Shorter latency of real-time epileptic seizure detection via probabilistic prediction. Expert Syst Appl. 2024;236:121359.","journal-title":"Expert Syst Appl"},{"key":"366_CR29","first-page":"1","volume":"2194","author":"T Liu","year":"2020","unstructured":"Liu T, Truong ND, Nikpour A, Zhou L, Kavehei O. Epileptic seizure classification with symmetric and hybrid bilinear models. IEEE J Biomed Health Inform. 2020;2194:1\u20131.","journal-title":"IEEE J Biomed Health Inform"}],"container-title":["Health Information Science and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-025-00366-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13755-025-00366-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-025-00366-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T09:09:46Z","timestamp":1765444186000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13755-025-00366-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,14]]},"references-count":29,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["366"],"URL":"https:\/\/doi.org\/10.1007\/s13755-025-00366-2","relation":{},"ISSN":["2047-2501"],"issn-type":[{"value":"2047-2501","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,14]]},"assertion":[{"value":"3 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 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":"All authors declare that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study was approved by the Ethics Committee of the Second Affiliated Hospital Zhejiang University School of Medicine (YAN2019-148).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"50"}}