Estimating functional brain maturity in very and extremely preterm neonates using automated analysis of the electroencephalogram

Objective: To develop an automated estimate of EEG maturational age (EMA) for preterm neonates.

Methods: The EMA estimator was based on the analysis of hourly epochs of EEG from 49 neonates with gestational age (GA) ranging from 23 to 32weeks. Neonates had appropriate EEG for GA based on visual interpretation of the EEG. The EMA estimator used a linear combination (support vector regression) of a subset of 41 features based on amplitude, temporal and spatial characteristics of EEG segments. Estimator performance was measured with the mean square error (MSE), standard deviation of the estimate (SD) and the percentage error (SE) between the known GA and estimated EMA.

Results: The EMA estimator provided an unbiased estimate of EMA with a MSE of 82days (SD=9.1days; SE=4.8%) which was significantly lower than a nominal reading (the mean GA in the dataset; MSE of 267days, SD of 16.3days, SE=8.4%: p<0.001). The EMA estimator with the lowest MSE used amplitude, spatial and temporal EEG characteristics.

Conclusions: The proposed automated EMA estimator provides an accurate estimate of EMA in early preterm neonates.

Significance: Automated analysis of the EEG provides a widely accessible, noninvasive and continuous assessment of functional brain maturity.