{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T18:09:07Z","timestamp":1792174147633,"version":"4.4.0"},"reference-count":37,"publisher":"IOP Publishing","issue":"1","license":[{"start":{"date-parts":[[2020,1,14]],"date-time":"2020-01-14T00:00:00Z","timestamp":1578960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2020,1,14]],"date-time":"2020-01-14T00:00:00Z","timestamp":1578960000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Neural Eng."],"published-print":{"date-parts":[[2020,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    . To classify sleep states using electroencephalogram (EEG) that reliably works over a wide range of preterm ages, as well as term age.\n                    <jats:italic>Approach<\/jats:italic>\n                    . A convolutional neural network is developed to perform 2- and 4-class sleep classification in neonates. The network takes as input an 8-channel 30\u2009s EEG segment and outputs the sleep state probabilities. Apart from simple downsampling of the input and smoothing of the output, the suggested network is an end-to-end algorithm that avoids the need for hand-crafted feature selection or complex pre\/post processing steps. To train and test this method, 113 EEG recordings from 42 infants are used.\n                    <jats:italic>Main results<\/jats:italic>\n                    . For quiet sleep detection (the 2-class problem), mean kappa between the network estimate and the ground truth annotated by EEG human experts is 0.76. The sensitivity and specificity are 90% and 88%, respectively. For 4-class classification, mean kappa is 0.64. The averaged sensitivity and specificity (1 versus all) respectively equal 72% and 91%. The results outperform current state-of-the-art methods for which kappa ranges from 0.66 to 0.70 in preterm and from 0.51 to 0.61 in term infants, based on training and testing using the same database.\n                    <jats:italic>Significance<\/jats:italic>\n                    . The proposed method has the highest reported accuracy for EEG sleep state classification for both preterm and term age neonates.\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/ab5469","type":"journal-article","created":{"date-parts":[[2019,11,5]],"date-time":"2019-11-05T17:45:58Z","timestamp":1572975958000},"page":"016028","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":65,"title":["A convolutional neural network outperforming state-of-the-art sleep staging algorithms for both preterm and term infants"],"prefix":"10.1088","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3261-0404","authenticated-orcid":false,"given":"Amir H","family":"Ansari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6946-4944","authenticated-orcid":false,"given":"Ofelie","family":"De Wel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kirubin","family":"Pillay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anneleen","family":"Dereymaeker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katrien","family":"Jansen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sabine","family":"Van Huffel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gunnar","family":"Naulaers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maarten","family":"De Vos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2020,1,14]]},"reference":[{"key":"jneab5469bib001","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/BF02760575","type":"journal-article","article-title":"NICU in a community level hospital","volume":"72","author":"Garg","year":"2005","journal-title":"Indian J. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2019-05-29","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2019-11-05","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2020-01-14","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}