{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T22:05:34Z","timestamp":1789423534798,"version":"build-2803163510"},"reference-count":68,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,24]],"date-time":"2024-02-24T00:00:00Z","timestamp":1708732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Health Research Council of New Zealand","award":["HRC-17\/601"],"award-info":[{"award-number":["HRC-17\/601"]}]},{"name":"Health Research Council of New Zealand","award":["HRC-18\/225"],"award-info":[{"award-number":["HRC-18\/225"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Brain maturity and many clinical treatments such as therapeutic hypothermia (TH) can significantly influence the morphology of neonatal EEG seizures after hypoxia\u2013ischemia (HI), and so there is a need for generalized automatic seizure identification. This study validates efficacy of advanced deep-learning pattern classifiers based on a convolutional neural network (CNN) for seizure detection after HI in fetal sheep and determines the effects of maturation and brain cooling on their accuracy. The cohorts included HI\u2013normothermia term (n = 7), HI\u2013hypothermia term (n = 14), sham\u2013normothermia term (n = 5), and HI\u2013normothermia preterm (n = 14) groups, with a total of &gt;17,300 h of recordings. Algorithms were trained and tested using leave-one-out cross-validation and k-fold cross-validation approaches. The accuracy of the term-trained seizure detectors was consistently excellent for HI\u2013normothermia preterm data (accuracy = 99.5%, area under curve (AUC) = 99.2%). Conversely, when the HI\u2013normothermia preterm data were used in training, the performance on HI\u2013normothermia term and HI\u2013hypothermia term data fell (accuracy = 98.6%, AUC = 96.5% and accuracy = 96.9%, AUC = 89.6%, respectively). Findings suggest that HI\u2013normothermia preterm seizures do not contain all the spectral features seen at term. Nevertheless, an average 5-fold cross-validated accuracy of 99.7% (AUC = 99.4%) was achieved from all seizure detectors. This significant advancement highlights the reliability of the proposed deep-learning algorithms in identifying clinically translatable post-HI stereotypic seizures in 256Hz recordings, regardless of maturity and with minimal impact from hypothermia.<\/jats:p>","DOI":"10.3390\/bioengineering11030217","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T06:50:23Z","timestamp":1708930223000},"page":"217","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep Learning for Generalized EEG Seizure Detection after Hypoxia\u2013Ischemia\u2014Preclinical Validation"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1136-3280","authenticated-orcid":false,"given":"Hamid","family":"Abbasi","sequence":"first","affiliation":[{"name":"Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand"},{"name":"Auckland Bioengineering Institute (ABI), University of Auckland, Auckland 1010, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joanne O.","family":"Davidson","sequence":"additional","affiliation":[{"name":"Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simerdeep K.","family":"Dhillon","sequence":"additional","affiliation":[{"name":"Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kelly Q.","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guido","family":"Wassink","sequence":"additional","affiliation":[{"name":"Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0656-7035","authenticated-orcid":false,"given":"Alistair J.","family":"Gunn","sequence":"additional","affiliation":[{"name":"Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Bennet","sequence":"additional","affiliation":[{"name":"Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/B978-0-444-64029-1.00010-2","article-title":"Neonatal encephalopathy and hypoxic\u2013ischemic encephalopathy","volume":"Volume 162","author":"Gunn","year":"2019","journal-title":"Handbook of Clinical Neurology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e20193182","DOI":"10.1542\/peds.2019-3182","article-title":"Levetiracetam versus phenobarbital for neonatal seizures: A randomized controlled trial","volume":"145","author":"Sharpe","year":"2020","journal-title":"Pediatrics"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101279","DOI":"10.1016\/j.siny.2021.101279","article-title":"Management of seizures in neonates with neonatal encephalopathy treated with hypothermia","volume":"26","author":"Mailo","year":"2021","journal-title":"Semin. 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