A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial

BACKGROUND: Despite the availability of continuous conventional electroencephalography (cEEG), accurate diagnosis of neonatal seizures is challenging in clinical practice. Algorithms for decision support in the recognition of neonatal seizures could improve detection. We aimed to assess the diagnostic accuracy of an automated seizure detection algorithm called Algorithm for Neonatal Seizure Recognition (ANSeR). METHODS: This multicentre, randomised, two-arm, parallel, controlled trial was done in eight neonatal centres across Ireland, the Netherlands, Sweden, and the UK. Neonates with a correc... Mehr ...

Verfasser: Pavel, Andreea M
Rennie, Janet M
de Vries, Linda S
Blennow, Mats
Foran, Adrienne
Shah, Divyen K
Pressler, Ronit M
Kapellou, Olga
Dempsey, Eugene M
Mathieson, Sean R
Pavlidis, Elena
van Huffelen, Alexander C
Livingstone, Vicki
Toet, Mona C
Weeke, Lauren C
Finder, Mikael
Mitra, Subhabrata
Murray, Deirdre M
Marnane, William P
Boylan, Geraldine B
Dokumenttyp: Artikel
Erscheinungsdatum: 2020
Schlagwörter: Algorithms / Electroencephalography/methods / Humans / Infant / Intensive Care / Neonatal / Ireland / Machine Learning/statistics & numerical data / Monitoring / Physiologic/methods / Netherlands / Seizures/diagnosis / Sweden / United Kingdom / Developmental and Educational Psychology / Pediatrics / Perinatology / and Child Health / Journal Article / Research Support / Non-U.S. Gov't / Randomized Controlled Trial / Multicenter Study
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-27612612
Datenquelle: BASE; Originalkatalog
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Link(s) : https://dspace.library.uu.nl/handle/1874/440408