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Clinical parameters included intrapartum complications, fetal distress, gestational age, delivery mode, gender, birth weight, Apgar scores, assisted ventilation, cord pH, and blood gases. The earliest EEG hour provided a qualitative analysis (discontinuity, amplitude, asymmetry\/asynchrony, sleep\u2013wake cycle [SWC]) and a quantitative analysis (power, discontinuity, spectral distribution, inter\u2010hemispheric connectivity) from full montage and two\u2010channel amplitude\u2010integrated EEG (aEEG). Subgroup analysis, only including infants without anti\u2010seizure medication (ASM) prior to EEG was also performed. Machine\u2010learning (ML) models (random forest and gradient boosting algorithms) were developed to predict infants who would later develop seizures and assessed using Matthews correlation coefficient (MCC) and area under the receiver\u2010operating characteristic curve (AUC).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The study included 162 infants with HIE (53 had seizures). Low Apgar, need for ventilation, high lactate, low base excess, absent SWC, low EEG power, and increased EEG discontinuity were associated with seizures. The following predictive models were developed: clinical (MCC 0.368, AUC 0.681), qualitative EEG (MCC 0.467, AUC 0.729), quantitative EEG (MCC 0.473, AUC 0.730), clinical and qualitative EEG (MCC 0.470, AUC 0.721), and clinical and quantitative EEG (MCC 0.513, AUC 0.746). The clinical and qualitative\u2010EEG model significantly outperformed the clinical model alone (MCC 0.470 vs 0.368, <jats:italic>p<\/jats:italic>\u2010value .037). The clinical and quantitative\u2010EEG model significantly outperformed the clinical model (MCC 0.513 vs 0.368, <jats:italic>p<\/jats:italic>\u2010value .012). The clinical and quantitative\u2010EEG model for infants without ASM (<jats:italic>n<\/jats:italic>\u00a0=\u00a0131) had MCC 0.588, AUC 0.832. Performance for quantitative aEEG (<jats:italic>n<\/jats:italic>\u00a0=\u00a0159) was MCC 0.381, AUC 0.696 and clinical and quantitative aEEG was MCC 0.384, AUC 0.720.<\/jats:p><\/jats:sec><jats:sec><jats:title>Significance<\/jats:title><jats:p>Early EEG background analysis combined with readily available clinical data helped predict infants who were at highest risk of seizures, hours before they occur. Automated quantitative\u2010EEG analysis was as good as expert analysis for predicting seizures, supporting the use of automated assessment tools for early evaluation of HIE.<\/jats:p><\/jats:sec>","DOI":"10.1111\/epi.17468","type":"journal-article","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T07:39:07Z","timestamp":1668757147000},"page":"456-468","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Machine learning for the early prediction of infants with electrographic seizures in neonatal hypoxic\u2010ischemic encephalopathy"],"prefix":"10.1111","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7233-3408","authenticated-orcid":false,"given":"Andreea M.","family":"Pavel","sequence":"first","affiliation":[{"name":"INFANT Research Centre University College Cork  Cork Ireland"},{"name":"Department of Paediatrics and Child Health University College Cork  Cork 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