EEG Maturational Age Estimation: A Comparison of Visual and Automated Interpretation of the EEG in Preterm Infants

Aim: To assess the inter-rater agreement and accuracy of human experts’ estimate of EEG maturational age (EMA) and a computer algorithm’s estimate of EMA over the first days after birth in a cohort of normally developing preterm infants. In addition, we explore the influence of post-natal age (PNA) on EMA estimates. Methods: Analysis was performed on EEG records from newborns determined appropriate for gestational age (GA) with favorable neurodevelopment at 2 years of age and without significant neurological compromise at time of EEG monitoring. Three 1h epochs of EEG were selected from 29 newborns with GA ranging from 23 to <32 weeks, within 72 h of birth. EEG epochs were visually assessed by two pediatric neurologists and a computer algorithm. In addition, the full, long-duration EEG recording of each newborn was assessed by one pediatric neurologist. EMA estimates were compared to GA using Pearson’s correlation coefficient (r) and bias and standard deviation of error (SDE). Intra-newborn agreements for the EMA estimates were assessed using standard deviation. Linear mixed-effects models were used to quantify the effect of PNA on EMA estimates. Results: The algorithm provides a more accurate estimate of GA using 1 h EEG epochs for correlation and bias: algorithm r = 0.83 vs. experts r = 0.60 and 0.66, p < 0.05 for n = 29; algorithm bias = −0.8 days vs. experts’ bias = 3.6 and 7.0 days, p < 0.01 for n = 29. SDE of 8.7 days for the algorithm was not significantly lower compared to the experts’ SDE = 12.4 and 13.2 days, p > 0.05. The algorithm has higher intra-newborn agreement compared to the experts: algorithm SDE = 4.9 days vs. experts SDE = 7.4 and 7.4 days, p = 0.027. For the two experts, increasing PNA is associated with an increase in EMA estimates of 6.6 days/days and 3.7 days/days. The assessment of full, long-duration EEG recordings improved the experts’ estimate of EMA (r = 0.82; SDE = 9.2 days). Conclusions: Automated analysis outperforms visual interpretation of the EEG at estimating EMA for short-duration EEG recordings. PNA is an important factor in EMA estimates.