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

Early EEG background analysis combined with readily available clinical data helped predict infants at highest risk of seizures, hours before they occur. Automated quantitative-EEG analysis was as good as expert analysis for predicting seizures, supporting the use of automated assessment tools for early evaluation of HIE.