{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T01:02:07Z","timestamp":1775264527688,"version":"3.50.1"},"reference-count":26,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,6,6]],"date-time":"2021-06-06T00:00:00Z","timestamp":1622937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,6,6]],"date-time":"2021-06-06T00:00:00Z","timestamp":1622937600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,6,6]],"date-time":"2021-06-06T00:00:00Z","timestamp":1622937600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,6,6]]},"DOI":"10.1109\/aicas51828.2021.9458539","type":"proceedings-article","created":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T20:01:10Z","timestamp":1624478470000},"page":"1-4","source":"Crossref","is-referenced-by-count":19,"title":["A Two-Layer LSTM Deep Learning Model for Epileptic Seizure Prediction"],"prefix":"10.1109","author":[{"given":"Shiva Maleki","family":"Varnosfaderani","sequence":"first","affiliation":[]},{"given":"Rihat","family":"Rahman","sequence":"additional","affiliation":[]},{"given":"Nabil J.","family":"Sarhan","sequence":"additional","affiliation":[]},{"given":"Levin","family":"Kuhlmann","sequence":"additional","affiliation":[]},{"given":"Eishi","family":"Asano","sequence":"additional","affiliation":[]},{"given":"Aimee","family":"Luat","sequence":"additional","affiliation":[]},{"given":"Mohammad","family":"Alhawari","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","article-title":"A generalised seizure prediction with convolutional neural networks for intracranial and scalp electroencephalogram data analysis","volume":"abs 1707 1976","author":"truong","year":"2017","journal-title":"CoRR"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2018.05.019"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/j.patrec.2019.10.034","article-title":"Epileptic seizure detection and prediction using stacked bidirectional long short term memory","volume":"128","author":"thara","year":"2019","journal-title":"Pattern Recognition Letters"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2933046"},{"key":"ref14","article-title":"Human intracranial EEG quantitative analysis and automatic feature learning for epileptic seizure prediction","volume":"abs 1904 3603","author":"hussein","year":"2019","journal-title":"CoRR"},{"key":"ref15","article-title":"A generative model to synthesize EEG data for epileptic seizure prediction","author":"rasheed","year":"2020","journal-title":"2012 arXiv preprint arXiv"},{"key":"ref16","article-title":"Epileptic seizure prediction: A semi-dilated convolutional neural network architecture","author":"hussein","year":"2020","journal-title":"arXiv preprint arXiv 2007 12869"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2020.06.008"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007733"},{"key":"ref19","article-title":"Searching for activation functions","author":"ramachandran","year":"2017","journal-title":"arXiv preprint arXiv 1710 05941"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1155\/2011\/450635"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"11s","DOI":"10.1111\/j.1528-1167.2005.00411.x","article-title":"Sudden unexpected death in epilepsy: A review of incidence and risk factors","volume":"46","author":"tomson","year":"2005","journal-title":"Epilepsia"},{"key":"ref6","year":"0"},{"key":"ref5","year":"0"},{"key":"ref8","year":"0"},{"key":"ref7","year":"0"},{"key":"ref2","article-title":"Neurological Disorders: Public Health Challenges","year":"2006","journal-title":"World Health Organization"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TBCAS.2019.2929053"},{"key":"ref1","year":"0"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2012.2227478"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1155\/2011\/406391"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2010.2051683"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awy210"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS51556.2021.9401766"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0228025"},{"key":"ref25","article-title":"Activation functions: Comparison of trends in practice and research for deep learning","author":"nwankpa","year":"2018"}],"event":{"name":"2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)","location":"Washington DC, DC, USA","start":{"date-parts":[[2021,6,6]]},"end":{"date-parts":[[2021,6,9]]}},"container-title":["2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9458399\/9458400\/09458539.pdf?arnumber=9458539","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:42:38Z","timestamp":1652197358000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9458539\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,6]]},"references-count":26,"URL":"https:\/\/doi.org\/10.1109\/aicas51828.2021.9458539","relation":{},"subject":[],"published":{"date-parts":[[2021,6,6]]}}}