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

ABSTRACT:

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 corrected gestational age between 36 and 44 weeks with, or at significant risk of, seizures requiring EEG monitoring, received cEEG plus ANSeR linked to the EEG monitor displaying a seizure pro

SUBMITTER: Pavel AM 

PROVIDER: S-EPMC7492960 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature