An Automated EEG Grading System for the Early Neonatal Brain Injury Severity Prediction Postperinatal Asphyxia

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.