{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T06:11:09Z","timestamp":1777961469241,"version":"3.51.4"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No.62271291"],"award-info":[{"award-number":["No.62271291"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Key Program of the Natural Science Foundation of Shandong Province","award":["No. ZR2020LZH009"],"award-info":[{"award-number":["No. ZR2020LZH009"]}]},{"name":"the Shenzhen Science and Technology Program","award":["GJHZ20220913142607013"],"award-info":[{"award-number":["GJHZ20220913142607013"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s00521-026-12025-y","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T05:26:17Z","timestamp":1777958777000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient seizure detection using swin transformer integrated with convolutional block attention"],"prefix":"10.1007","volume":"38","author":[{"given":"Shuang","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinlian","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weidong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,5]]},"reference":[{"issue":"2","key":"12025_CR1","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1159\/000503831","volume":"54","author":"E Beghi","year":"2020","unstructured":"Beghi E (2020) The epidemiology of epilepsy. Neuroepidemiology 54(2):185\u2013191. https:\/\/doi.org\/10.1159\/000503831","journal-title":"Neuroepidemiology"},{"key":"12025_CR2","doi-asserted-by":"publisher","unstructured":"Q Sun, Y Liu, S Li (2021) Patient-Specific automatic seizure detection method from EEG signals based on random forest. Proceedings of 14th international congress on image and signal processing, biomedical engineering and informatics. https:\/\/doi.org\/10.1109\/CISP-BMEI53629.2021.9624400","DOI":"10.1109\/CISP-BMEI53629.2021.9624400"},{"key":"12025_CR3","doi-asserted-by":"publisher","unstructured":"Mozafari M, Sardouie SH (2019) Automatic epileptic seizure detection in a mixed generalized and focal seizure dataset. Proceedings of 26th national and 4th international iranian conference on biomedical engineering. https:\/\/doi.org\/10.1109\/ICBME49163.2019.9030381","DOI":"10.1109\/ICBME49163.2019.9030381"},{"key":"12025_CR4","doi-asserted-by":"publisher","unstructured":"Shankar A, Chakraborty D, Dandapat S, BarmaS SMJ (2024) Attention-based deep learning for epileptic seizure type detection. Proceedings of international conference on advancements in smart secure and intelligent computing. https:\/\/doi.org\/10.1109\/ASSIC60049.2024.10507948","DOI":"10.1109\/ASSIC60049.2024.10507948"},{"key":"12025_CR5","doi-asserted-by":"publisher","unstructured":"Sharma A, Khan S, Chhabra R, Kumar R (2024) A way to detect seizure through ML for enhancing the epilepsy way of management. Proceedings of IEEE international conference on computing power and communication technologies. https:\/\/doi.org\/10.1109\/IC2PCT60090.2024.10486613","DOI":"10.1109\/IC2PCT60090.2024.10486613"},{"key":"12025_CR6","doi-asserted-by":"publisher","unstructured":"Devi VS, Pallavi R (2024) Analysis of epileptic seizurde Detection using deep learning algorithms. Proceedings of third international conference on distributed computing and electrical circuits and electronics. https:\/\/doi.org\/10.1109\/ICDCECE60827.2024.10548098","DOI":"10.1109\/ICDCECE60827.2024.10548098"},{"issue":"4","key":"12025_CR7","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1111\/j.1528-1157.1964.tb03337.x","volume":"5","author":"H Gastaut","year":"1964","unstructured":"Gastaut H, Magnus O, Caveness WF, Merlis JK, Landolt H et al (1964) A proposed international classification of epileptic seizures. Epilepsia 5(4):297\u2013306. https:\/\/doi.org\/10.1111\/j.1528-1157.1964.tb03337.x","journal-title":"Epilepsia"},{"issue":"5","key":"12025_CR8","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1016\/0013-4694(82)90038-4","volume":"54","author":"J Gotman","year":"1982","unstructured":"Gotman J (1982) Automatic recognition of epileptic seizures in the EEG. Electroencephalogr Clin Neurophysiol 54(5):530\u2013540. https:\/\/doi.org\/10.1016\/0013-4694(82)90038-4","journal-title":"Electroencephalogr Clin Neurophysiol"},{"issue":"4","key":"12025_CR9","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/0013-4694(90)90032-f","volume":"76","author":"J Gotman","year":"1990","unstructured":"Gotman J (1990) Automatic seizure detection: improvements and evaluation. Electroencephalogr Clin Neurophysiol 76(4):317\u2013324. https:\/\/doi.org\/10.1016\/0013-4694(90)90032-f","journal-title":"Electroencephalogr Clin Neurophysiol"},{"key":"12025_CR10","doi-asserted-by":"publisher","first-page":"20605","DOI":"10.1007\/s00521-023-08832-2","volume":"35","author":"S Shanmugam","year":"2023","unstructured":"Shanmugam S, Dharmar S (2023) A CNN-LSTM hybrid network for automatic seizure detection in EEG signals. Neural Comput Appl 35:20605\u201320617. https:\/\/doi.org\/10.1007\/s00521-023-08832-2","journal-title":"Neural Comput Appl"},{"issue":"3","key":"12025_CR11","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/s11571-020-09641-2","volume":"15","author":"M Melek","year":"2025","unstructured":"Melek M, Manshouri N, Kayikcioglu T (2025) An automatic EEG-based sleep staging system with introducing NAoSP and NAoGP as new metrics for sleep staging systems. Cogn Neurodyn 15(3):405\u2013423. https:\/\/doi.org\/10.1007\/s11571-020-09641-2","journal-title":"Cogn Neurodyn"},{"issue":"1","key":"12025_CR12","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1093\/comjnl\/bxz043","volume":"63","author":"N Manshouri","year":"2020","unstructured":"Manshouri N, Kayikcioglu T (2020) A comprehensive analysis of 2D and 3D video watching of EEG signals by increasing PLSR and SVM classification results. Comput J 63(1):425\u2013434. https:\/\/doi.org\/10.1093\/comjnl\/bxz043","journal-title":"Comput J"},{"key":"12025_CR13","doi-asserted-by":"publisher","unstructured":"Alvin W, Lisa D, Daniel H, Jihan Y, Lee S, et al (2021) NBDT: neural-backed decision trees. Proceedings of computer vision and pattern recognition. https:\/\/doi.org\/10.48550\/arXiv.2004.00221","DOI":"10.48550\/arXiv.2004.00221"},{"issue":"2","key":"12025_CR14","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1109\/JBHI.2012.2237409","volume":"17","author":"S Alam","year":"2013","unstructured":"Alam S, Bhuiyan M (2013) Detection of seizure and epilepsy using higher order statistics in the EMD domain. IEEE J Biomed Health Inform 17(2):312\u2013318. https:\/\/doi.org\/10.1109\/JBHI.2012.2237409","journal-title":"IEEE J Biomed Health Inform"},{"key":"12025_CR15","doi-asserted-by":"publisher","unstructured":"Elgammal MA, Mostafa H, Salama KN, Mohieldin AN (2019) A comparison of artificial neural network (ANN) and support vector machine (SVM) classifiers for neural seizure detection. Proceedings of the 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems. https:\/\/doi.org\/10.1109\/MWSCAS.2019.8884989","DOI":"10.1109\/MWSCAS.2019.8884989"},{"key":"12025_CR16","doi-asserted-by":"publisher","unstructured":"Reddy VN, Priyanka R (2023) Analysis of novel seizure detection system using linear regression algorithm to improvise the performance efficiency in comparison with naive bayes. Proceedings of the 2022 5th International Conference on Contemporary Computing and Informatics. https:\/\/doi.org\/10.1109\/IC3I56241.2022.10072424","DOI":"10.1109\/IC3I56241.2022.10072424"},{"issue":"2","key":"12025_CR17","doi-asserted-by":"publisher","first-page":"138","DOI":"10.35377\/saucis...1436915","volume":"7","author":"N Melek","year":"2024","unstructured":"Melek N (2024) The importance of rhythm activity in epilepsy EEG signal classification (an educational article). Sakarya Univ J Comput Inf Sci 7(2):138\u2013155. https:\/\/doi.org\/10.35377\/saucis...1436915","journal-title":"Sakarya Univ J Comput Inf Sci"},{"key":"12025_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102360","volume":"64","author":"J Craley","year":"2021","unstructured":"Craley J, Johnson E, Jouny C, Venkataraman A (2021) Automated inter-patient seizure detection using multichannel convolutional and recurrent neural networks. Biomed Signal Process Control 64:102360. https:\/\/doi.org\/10.1016\/j.bspc.2020.102360","journal-title":"Biomed Signal Process Control"},{"key":"12025_CR19","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436\u2013444. https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"12025_CR20","doi-asserted-by":"publisher","unstructured":"Craley J, Johnson E, Venkataraman A (2019) Integrating convolutional neural networks and probabilistic graphical modeling for epileptic seizure detection in multichannel EEG. Proceedings of the 26th International Conference on Information Processing in Medical Imaging 291\u2013303. https:\/\/doi.org\/10.1007\/978-3-030-20351-1_22","DOI":"10.1007\/978-3-030-20351-1_22"},{"key":"12025_CR21","doi-asserted-by":"publisher","unstructured":"Chen X, Ji J, Ji T, Li P (2018) Cost-sensitive deep active learning for epileptic seizure detection. Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics 2018. 226\u2013235. https:\/\/doi.org\/10.1145\/3233547.3233566","DOI":"10.1145\/3233547.3233566"},{"key":"12025_CR22","doi-asserted-by":"publisher","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. Neural Comput. https:\/\/doi.org\/10.48550\/arXiv.1412.3555","DOI":"10.48550\/arXiv.1412.3555"},{"key":"12025_CR23","doi-asserted-by":"publisher","unstructured":"Roy S, KiralKornek I, Harrer S (2019) ChronoNet: A deep recurrentneural network for abnormal EEG identification. Proceedings of 17th Conference on Artificial Intelligence in Medicine in Europe 2019. 47\u201356. https:\/\/doi.org\/10.48550\/arXiv.1802.00308","DOI":"10.48550\/arXiv.1802.00308"},{"issue":"3","key":"12025_CR24","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065723500120","volume":"33","author":"WY Peh","year":"2023","unstructured":"Peh WY, Thangavel P, Yao Y, Thomas J, Tan Y et al (2023) Six-Center assessment of CNN-Transformer with belief matching loss for patient-independent seizure detection in EEG. Int J Neural Syst 33(3):2350012. https:\/\/doi.org\/10.1142\/S0129065723500120","journal-title":"Int J Neural Syst"},{"key":"12025_CR25","doi-asserted-by":"publisher","unstructured":"Zachary CL, John B, Charles E (2015) A critical review of recurrent neural networks for sequence learning. Neural Comput. https:\/\/doi.org\/10.48550\/arXiv.1506.00019","DOI":"10.48550\/arXiv.1506.00019"},{"key":"12025_CR26","doi-asserted-by":"publisher","unstructured":"Yoon K (2014) Convolutional neural networks for sentence classification. Proceedings of Computation and Language. https:\/\/doi.org\/10.48550\/arXiv.1408.5882","DOI":"10.48550\/arXiv.1408.5882"},{"key":"12025_CR27","doi-asserted-by":"publisher","unstructured":"Zhong X, Liu G, Dong X, Li C, Li H, et al (2024) Automatic seizure detection based on stockwell transform and transformer. Sensors 24(1). https:\/\/doi.org\/10.3390\/s24010077","DOI":"10.3390\/s24010077"},{"key":"12025_CR28","doi-asserted-by":"publisher","first-page":"7132","DOI":"10.1109\/CVPR.2018.00745","volume":"2018","author":"J Hu","year":"2018","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. Proc IEEE Conf Comput Vis Pattern Recognit 2018:7132\u20137141. https:\/\/doi.org\/10.1109\/CVPR.2018.00745","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"12025_CR29","doi-asserted-by":"publisher","unstructured":"Zhong Z, Lin Z, Bidart R, Hu X, Daya IB, et al (2020) Squeeze-and-Attention networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2020. 13065\u201313074. https:\/\/doi.org\/10.48550\/arXiv.1909.03402","DOI":"10.48550\/arXiv.1909.03402"},{"key":"12025_CR30","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume":"2018","author":"S Woo","year":"2018","unstructured":"Woo S, Park J, Lee J, Kweon IS (2018) CBAM: convolutional block attention module. Proceedings of European Conference on Computer Vision 2018:3\u201319. https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1","journal-title":"Proceedings of European Conference on Computer Vision"},{"key":"12025_CR31","doi-asserted-by":"publisher","unstructured":"Ashish V, Noam S, Niki P, Jakob U, Llion J, et al (2017) Attention is all you need. Proceedings of Computation and Language. https:\/\/doi.org\/10.48550\/arXiv.1706.03762","DOI":"10.48550\/arXiv.1706.03762"},{"key":"12025_CR32","doi-asserted-by":"publisher","unstructured":"Alexey D, Lucas B, Alexander K, Dirk W, Zhai X, et al (2020) An image is worth 16$$\\times$$16 words: transformers for image recognition at scale. Proceedings of Computer Vision and Pattern Recognition. https:\/\/doi.org\/10.48550\/arXiv.2010.11929","DOI":"10.48550\/arXiv.2010.11929"},{"key":"12025_CR33","doi-asserted-by":"publisher","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, et al (2021) Swin Transformer: hierarchical vision transformer using shifted windows. Proceedings of Computer Vision and Pattern Recognition. https:\/\/doi.org\/10.48550\/arXiv.2103.14030","DOI":"10.48550\/arXiv.2103.14030"},{"issue":"5","key":"12025_CR34","doi-asserted-by":"publisher","first-page":"3329","DOI":"10.1109\/JBHI.2025.3532223","volume":"29","author":"J Pordoy","year":"2025","unstructured":"Pordoy J, Jones G, Matoorian N et al (2025) Enhanced non-EEG multimodal seizure detection: a real-world model for identifying generalised seizures across the ictal state. IEEE J Biomed Health Inform 29(5):3329\u20133342. https:\/\/doi.org\/10.1109\/JBHI.2025.3532223","journal-title":"IEEE J Biomed Health Inform"},{"issue":"7","key":"12025_CR35","doi-asserted-by":"publisher","first-page":"2379","DOI":"10.1109\/TBME.2022.3144634","volume":"69","author":"X Kok","year":"2022","unstructured":"Kok X, Imtiaz S, Rodriguez-Villegas E et al (2022) Assessing the feasibility of acoustic based seizure detection. IEEE T Bio-med Eng 69(7):2379\u20132389. https:\/\/doi.org\/10.1109\/TBME.2022.3144634","journal-title":"IEEE T Bio-med Eng"},{"key":"12025_CR36","doi-asserted-by":"publisher","unstructured":"Ahsan MNI, Kertesz C, Mesaros A, et al (2019) Audio-Based Epileptic Seizure Detection. 2019 27th European Signal Processing Conference (EUSIPCO). https:\/\/doi.org\/10.23919\/EUSIPCO.2019.8902840","DOI":"10.23919\/EUSIPCO.2019.8902840"},{"issue":"1","key":"12025_CR37","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1109\/TCDS.2024.3433551","volume":"17","author":"G Wang","year":"2025","unstructured":"Wang G, Lei X, Li W et al (2025) Channel-selection-based temporal convolutional network for patient-specific epileptic seizure detection. IEEE T Cogn Dev Syst 17(1):179\u2013188. https:\/\/doi.org\/10.1109\/TCDS.2024.3433551","journal-title":"IEEE T Cogn Dev Syst"},{"issue":"5","key":"12025_CR38","doi-asserted-by":"publisher","first-page":"5501704","DOI":"10.1109\/LSENS.2025.3558422","volume":"9","author":"VV Narayana","year":"2025","unstructured":"Narayana VV, Kodali P (2025) Hybrid LPF-LSTM model for enhanced epileptic seizure detection in EEG signal. IEEE Sensors Letters 9(5):5501704. https:\/\/doi.org\/10.1109\/LSENS.2025.3558422","journal-title":"IEEE Sensors Letters"},{"key":"12025_CR39","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2025.3551573","author":"R Fu","year":"2025","unstructured":"Fu R, Zhang B, Xue B et al (2025) A spatiotemporal posterior graph convolutional neural network based on multihead attention with Squeeze-and-Excitation module for patient-specific epileptic seizure prediction. IEEE Trans Instrum Meas. https:\/\/doi.org\/10.1109\/TIM.2025.3551573","journal-title":"IEEE Trans Instrum Meas"},{"key":"12025_CR40","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1109\/TNSRE.2024.3350074","volume":"32","author":"Y Tang","year":"2024","unstructured":"Tang Y, Wu Q, Mao H et al (2024) Epileptic seizure detection based on path signature and Bi-LSTM network with attention mechanism. IEEE Trans Neural Networks Learn Syst 32:304\u2013313. https:\/\/doi.org\/10.1109\/TNSRE.2024.3350074","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"key":"12025_CR41","doi-asserted-by":"publisher","first-page":"60213","DOI":"10.1109\/ACCESS.2025.3556811","volume":"13","author":"G Amrani","year":"2025","unstructured":"Amrani G, Adadi A, Berrada M et al (2025) An attention mechanism-based interpretable model for epileptic seizure detection and localization with Self-Supervised Pre-Training. IEEE Access 13:60213\u201360232. https:\/\/doi.org\/10.1109\/ACCESS.2025.3556811","journal-title":"IEEE Access"},{"key":"12025_CR42","doi-asserted-by":"publisher","unstructured":"Shi Y,Wang M (2024) Swin-CANet: A Novel Integration of Swin Transformer with Channel Attention for Enhanced Motor Imagery Classification. 2024 IEEE 4th International Conference on Software Engineering and Artificial Intelligence (SEAI) Xiamen, China. https:\/\/doi.org\/10.1109\/SEAI62072.2024.10674039","DOI":"10.1109\/SEAI62072.2024.10674039"},{"key":"12025_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107075","volume":"100","author":"C Dong","year":"2025","unstructured":"Dong C, Sun D, Zhang Z, Luo B (2025) EEG-based patient-specific seizure prediction based on spatial-temporal hypergraph attention transformer. Biomed Signal Process Control 100:107075. https:\/\/doi.org\/10.1016\/j.bspc.2024.107075","journal-title":"Biomed Signal Process Control"},{"key":"12025_CR44","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2025.3545265","author":"Q Dong","year":"2025","unstructured":"Dong Q, Zhang H, Xiao J et al (2025) Multi-scale spatio-temporal attention network for epileptic seizure prediction. IEEE J Biomed Health Inform. https:\/\/doi.org\/10.1109\/JBHI.2025.3545265","journal-title":"IEEE J Biomed Health Inform"},{"key":"12025_CR45","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1109\/TNSRE.2022.3143540","volume":"30","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Yao S, Yang R, Liu X, Qiu W (2022) Epileptic seizure detection based on bidirectional gated recurrent unit network. IEEE T Neur Sys Reh 30:135\u2013145. https:\/\/doi.org\/10.1109\/TNSRE.2022.3143540","journal-title":"IEEE T Neur Sys Reh"},{"key":"12025_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.bbe.2021.11.002","volume":"42","author":"B Gao","year":"2022","unstructured":"Gao B, Zhou J, Yang Y, Chi J, Yuan Q (2022) Generative adversarial network and convolutional neural network-based EEG imbalanced classification model for seizure detection. Biocybern Biomed Eng 42:1\u201315. https:\/\/doi.org\/10.1016\/j.bbe.2021.11.002","journal-title":"Biocybern Biomed Eng"},{"key":"12025_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2023.01.002","author":"T Zhang","year":"2023","unstructured":"Zhang T, Chen W, Chen X (2023) Identifying epileptic EEGs and congestive heart failure ECGs under unified framework of wavelet scattering transform, bidirectional weighted $$(2D)^2$$PCA and KELM. Biocybern Biomed Eng. https:\/\/doi.org\/10.1016\/j.bbe.2023.01.002","journal-title":"Biocybern Biomed Eng"},{"issue":"3","key":"12025_CR48","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065724500126","volume":"34","author":"X Dong","year":"2024","unstructured":"Dong X, Wen Y, Ji D, Yuan S, Liu Z et al (2024) Epileptic seizure detection with an end-to-end temporal convolutional network and bidirectional long short-term memory model. Int J Neural Syst 34(3):2450012. https:\/\/doi.org\/10.1142\/S0129065724500126","journal-title":"Int J Neural Syst"},{"issue":"6","key":"12025_CR49","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065721500519","volume":"32","author":"G Liu","year":"2021","unstructured":"Liu G, Tian L, Zhou W (2021) Patient-independent seizure detection based on channel-perturbation convolutional neural network and bidirectional long short-term memory. Int J Neural Syst 32(6):2150051. https:\/\/doi.org\/10.1142\/S0129065721500519","journal-title":"Int J Neural Syst"},{"key":"12025_CR50","doi-asserted-by":"publisher","first-page":"35221","DOI":"10.1007\/s11042-023-14995-w","volume":"82","author":"CJ Gopal","year":"2023","unstructured":"Gopal CJ, Keshav S, Anupam A (2023) Capsule neural network based approach for subject specific and cross-subjects seizure detection from EEG signals. Multimed Tools Appl 82:35221\u201335252. https:\/\/doi.org\/10.1007\/s11042-023-14995-w","journal-title":"Multimed Tools Appl"},{"key":"12025_CR51","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/978-3-031-66705-3_23","volume":"2024","author":"R Alqirshi","year":"2024","unstructured":"Alqirshi R, Belhaouari SB (2024) EEG-Based patient pndependent epileptic seizure detection using GCN-BRF. Proceedings of International Conference on Deep Learning Theory and Applications 2024:351\u2013366. https:\/\/doi.org\/10.1007\/978-3-031-66705-3_23","journal-title":"Proceedings of International Conference on Deep Learning Theory and Applications"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-12025-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-026-12025-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-12025-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T05:26:20Z","timestamp":1777958780000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-026-12025-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":51,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["12025"],"URL":"https:\/\/doi.org\/10.1007\/s00521-026-12025-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"15 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2026","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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"365"}}