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Updated: Jan 11, 2026
Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates
Published on: September 6, 2017
Automated estimation of EEG maturity in preterm neonates and its association with long-term outcome
Johannes Mader1, Manfred Hartmann2, Katrin Klebermass-Schrehof3
1Division of Neonatology, Pediatric Intensive Care and Neuropediatrics, Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Paediatrics (CCP), Medical University of Vienna, Vienna, Austria; Center for Health & Bioresources, AIT (Austrian Institute of Technology), Vienna, Austria.
Objective:
To develop convolutional neural network (CNN) models to estimate EEG maturational age (EMA) from EEG recorded via amplitude-integrated EEG monitors in preterm infants and evaluate its association with long-term neurodevelopmental outcomes.
Methods:
Three CNN models were trained on EEG data recorded from aEEG monitors from 92 preterm infants (23-41 weeks postmenstrual age) without major neurological complications and with normal cognitive outcomes at 2 years. The best-performing model based on MAE was applied to a broader cohort of 148 infants to assess the relationship between predicted age difference (PAD = EMA - PMA) and neurodevelopmental outcome using the Bayley Scales of Infant and Toddler Development (BSID-III).
Results:
The best models achieved 87 % and 84 % accuracy within ± 1 week of actual PMA and a mean absolute error of 0.63 and 0.55 weeks. Infants with severely abnormal cognitive outcomes had significantly lower PAD scores compared to those with normal outcomes (P < 0.001). PAD and MAE showed moderate predictive value (AUC 0.69 and 0.77, respectively).
Conclusions:
CNN-based EMA estimation from EEG recorded via aEEG monitors is accurate and correlates with long-term cognitive outcomes in preterm infants.
Significance:
This study demonstrates the clinical potential of automated EEG maturity tracking using EEG as a real-time biomarker for neurodevelopmental risk.
