Objectives: To develop a quantitative electroencephalography (EEG)-based automatic grading system
for neonatal hypoxic-ischemic encephalopathy (HIE).
Methods: Neonatal EEG were recorded in full term infants in the first 6 hours of life after
perinatal hypoxia. The severity of HIE was determined by the visual conventional EEG
grades (French classification), assessed by two neurophysiologists blinded to clinical
data. Six EEG quantitative features were selected based on their correlation scores
with the three visual grades. Thereafter, the six selected features were analyzed
using discriminant factorial analysis (DFA) to predict the severity grade and the
long-term outcome.
Results: A total of 90 EEG were analyzed between 2013 and 2017. The EEG quantitative features
measuring the discontinuity and the amplitude of the signal were able to discriminate
the three visual grades. The DFA results showed an accuracy of 86.7% for predicting
EEG grades and 79.8% for predicting outcome at 1 year.
Conclusion: The proposed automated system using DFA was effective for grading initial EEG and
predicting long-term outcome early after perinatal asphyxia. This system is based
on simple quantitative features already proposed in marketed programs and could be
easily used in clinical routine by unexperienced users. It may facilitate the evaluation
of HIE’s severity within 6 hours after birth and then be useful to determine whether
therapeutic hypothermia has to be initiated.
