{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T21:16:31Z","timestamp":1773868591489,"version":"3.50.1"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Zhejiang Province Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LY18F040001"],"award-info":[{"award-number":["LY18F040001"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3024580","type":"journal-article","created":{"date-parts":[[2020,9,18]],"date-time":"2020-09-18T20:17:42Z","timestamp":1600460262000},"page":"172352-172361","source":"Crossref","is-referenced-by-count":20,"title":["Seizure Prediction Using Multi-View Features and Improved Convolutional Gated Recurrent Network"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2328-5163","authenticated-orcid":false,"given":"Lihan","family":"Tang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0842-402X","authenticated-orcid":false,"given":"Ning","family":"Xie","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2500-2892","authenticated-orcid":false,"given":"Menglian","family":"Zhao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1068-5934","authenticated-orcid":false,"given":"Xiaobo","family":"Wu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017","journal-title":"arXiv 1704 04861"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.69.026113"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.physleta.2005.06.092"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2009.05.003"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"215e","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank,PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"goldberger","year":"2000","journal-title":"Circulat"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2976156"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0193(1999)8:4<194::AID-HBM4>3.0.CO;2-C"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1088\/1751-8113\/41\/1\/015501"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3758\/s13428-013-0317-2"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1049\/el.2020.1471"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2003.810689"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2014.02.007"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2014.2358640"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2015.2458982"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2016.2618937"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/1240323"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2019.2943707"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2955285"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2016160274"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2017.00239"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/SPMB.2017.8257020"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1097\/00019052-200204000-00008"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2883562"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/S0920-1211(02)00257-7"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2014.05.022"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2944691"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TBCAS.2015.2477264"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2009.2038990"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awl241"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1002\/9780470511923"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2017.2785401"},{"key":"ref1","year":"2018","journal-title":"Epilepsy Foundation"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2016.00080"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2017.11.002"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007733"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2553131"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2017.03.002"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2586475"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109896"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2013.6610995"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2012.2237399"},{"key":"ref44","first-page":"1","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"chung","year":"2014","journal-title":"Proc NIPS"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2959234"},{"key":"ref43","first-page":"1","article-title":"Session-based recommendations with recurrent neural networks","author":"hidasi","year":"2016","journal-title":"Proc ICLR"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.04.018"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09200354.pdf?arnumber=9200354","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:55:57Z","timestamp":1639770957000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9200354\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":47,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3024580","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}