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 ...
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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-29203622 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://dspace.library.uu.nl/handle/1874/440408 |