Peer-Reviewed Journal Details
Mandatory Fields
O'Toole, JM;Boylan, GB;Vanhatalo, S;Stevenson, NJ
2016
August
Clinical Neurophysiology
Estimating functional brain maturity in very and extremely preterm neonates using automated analysis of the electroencephalogram
Validated
WOS: 34 ()
Optional Fields
SPONTANEOUS ACTIVITY TRANSIENTS PREMATURE-INFANTS MATURATIONAL CHANGES CEREBRAL-PALSY EEG ACTIVITY SLEEP INDEX BIRTH CONNECTIVITY DYSMATURITY
127
2910
2918
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 32 weeks. 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 82 days (SD = 9.1 days; SE = 4.8%) which was significantly lower than a nominal reading (the mean GA in the dataset; MSE of 267 days, SD of 16.3 days, 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. (C) 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
CLARE
1388-2457
10.1016/j.clinph.2016.02.024
Grant Details