Automated assessment of EEG background for neurodevelopmental prediction in neonatal encephalopathy.

ABSTRACT:

Objective

Assess the capacity of brain state of the newborn (BSN) to predict neurodevelopment outcomes in neonatal encephalopathy.

Methods

Trends of BSN, a deep learning-based measure translating EEG background to a continuous trend, were studied from a three-channel montage long-term EEG monitoring from a prospective cohort of 92 infants with neonatal encephalopathy and neurodevelopmental outcomes assessed by Bayley Scales of Infant Development, 3rd edition (Bayley-III) at 18 months. Outcome prediction used categories "Severe impairment" (Bayley-III composite score ≤70 or death) or "Any impairment" (score ≤85 or death).

Results

"Severe impairment" was predicted best for motor outcomes (24 h area under the curve (AUC) = 0.97), followed by cognitive (36 h AUC = 0.90),

SUBMITTER: Lagace M 

PROVIDER: S-EPMC11651191 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature