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

ABSTRACT:

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 <12 h of birth from infants with HIE across eight European Neonatal Units were used to develop seizure-prediction models. 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-wake cycle [SWC]) and a quantitative analysis (power, discontinuity, spectral distribution, inter-hemispheric connectivity) from full montage and two-channel amp

SUBMITTER: Pavel AM 

PROVIDER: S-EPMC10107538 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature