Machine learning for the early prediction of infants with electrographic seizures in neonatal hypoxic‐ischemic encephalopathy

Objective To assess if early clinical and electroencephalography (EEG) features predict later seizure development in infants with hypoxic‐ischemic encephalopathy (HIE). Methods Clinical and EEG parameters

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Veröffentlicht in:	Epilepsia (Copenhagen) 2023-02, Vol.64 (2), p.456-468
Hauptverfasser:	Pavel, Andreea M., O'Toole, John M., Proietti, Jacopo, Livingstone, Vicki, Mitra, Subhabrata, Marnane, William P., Finder, Mikael, Dempsey, Eugene M., Murray, Deirdre M., Boylan, Geraldine B., Pavlidis, Elena, Kharoshankaya, Liudmila, Mathieson, Sean R., Lightbody, Gordon, O’Leary, Jackie, Murray, Mairead, Conway, Jean, Dwyer, Denis, Temko, Andrey, Kiely, Taragh, Ryan, Anthony C., Rennie, Janet M., Vries, Linda S., Weeke, Lauren C., Toet, Mona C., Harteman, Johanneke C., Blennow, Mats, Edqvist, Ingela, Foran, Adrienne, Pinnamaneni, Raga Mallika, Colby‐Milley, Jessica, Shah, Divyen K., Openshaw‐Lawrence, Nicola, Pressler, Ronit M., Kapellou, Olga, Huffelen, Alexander C.
Format:	Artikel
Sprache:	eng
Schlagworte:	Apgar score Automation Birth weight Convulsions & seizures EEG Electroencephalography Encephalopathy Fetuses Gestational Age Hemispheric laterality Humans Hypoxia Hypoxia-Ischemia, Brain - complications Hypoxia-Ischemia, Brain - diagnosis Infant Infant, Newborn Infants Ischemia Lactic Acid Learning algorithms Machine learning Mechanical ventilation Medicin och hälsovetenskap Neonatal care neonatal encephalopathy neonatal seizures Neonates prediction algorithm Prediction models ROC Curve Seizures Sleep and wakefulness Ventilators
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Beschreibung
Zusammenfassung:	Objective To assess if early clinical and electroencephalography (EEG) features predict later seizure development in infants with hypoxic‐ischemic encephalopathy (HIE). Methods Clinical and EEG parameters
ISSN:	0013-9580 1528-1167 1528-1167
DOI:	10.1111/epi.17468