Related Experiment Video
Updated: Dec 23, 2025
Preterm EEG: A Multimodal Neurophysiological Protocol
Published on: February 18, 2012
Applying a data-driven approach to quantify EEG maturational deviations in preterms with normal and abnormal
Kirubin Pillay1,2, Anneleen Dereymaeker3, Katrien Jansen3,4
1Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Oxford, United Kingdom. kirubin.pillay@paediatrics.ox.ac.uk.
Insights
Environmental stress impacts premature babies' brain development. Researchers used Electroencephalography (EEG) to track brain-age trajectories, identifying deviations linked to neurodevelopmental outcomes for early prediction.
Area of Science:
- Neonatal neuroscience
- Developmental pediatrics
- Clinical neurophysiology
Background:
- Premature birth exposes infants to environmental stressors impacting brain maturation.
- Abnormal neurodevelopmental outcomes in premature infants necessitate early detection tools.
- Understanding the link between early brain maturation and long-term outcomes is critical.
Purpose of the Study:
- To define maturational trajectories of Electroencephalography (EEG)-derived brain-age against postmenstrual age in premature infants.
- To compare trajectory deviations between infants with normal and abnormal neurodevelopmental outcomes.
- To identify potential biomarkers for early outcome estimation.
Main Methods:
- Longitudinal EEG recordings from 65 premature infants (224 total) in the Neonatal Intensive Care Unit.
- Calculation of brain-age and postmenstrual age to define maturational trajectories.
- Analysis of trajectory deviations using root mean squared error (RMSE) and maximum trajectory deviation (δmax) to differentiate outcome groups.
Main Results:
- Significant differences in RMSE and δmax were observed between normal and abnormal neurodevelopmental outcome groups (p < 0.05).
- Infants with abnormal outcomes exhibited higher RMSE (median 1.35 weeks) and δmax (median 1.90 weeks) compared to those with normal outcomes (RMSE 0.75, δmax 0.90 weeks).
- Deviations in brain-age trajectories correlated with clinically defined dysmature and disorganized EEG patterns.
Conclusions:
- Early maturational trajectories derived from EEG-based brain-age are significantly linked to neurodevelopmental outcomes in premature infants.
- Trajectory deviations serve as sensitive indicators for predicting abnormal neurodevelopmental outcomes.
- This approach can potentially lead to a clinical tool for early outcome estimation in high-risk neonates.
Abstract:
Premature babies are subjected to environmental stresses that can affect brain maturation and cause abnormal neurodevelopmental outcome later in life. Better understanding this link is crucial to developing a clinical tool for early outcome estimation. We defined maturational trajectories between the Electroencephalography (EEG)-derived 'brain-age' and postmenstrual age (the age since the last menstrual cycle of the mother) from longitudinal recordings during the baby's stay in the Neonatal Intensive Care Unit. Data consisted of 224 recordings (65 patients) separated for normal and abnormal outcome at 9-24 months follow-up. Trajectory deviations were compared between outcome groups using the root mean squared error (RMSE) and maximum trajectory deviation (δmax). 113 features were extracted (per sleep state) to train a data-driven model that estimates brain-age, with the most prominent features identified as potential maturational and outcome-sensitive biomarkers. RMSE and δmax showed significant differences between outcome groups (cluster-based permutation test, p < 0.05). RMSE had a median (IQR) of 0.75 (0.60-1.35) weeks for normal outcome and 1.35 (1.15-1.55) for abnormal outcome, while δmax had a median of 0.90 (0.70-1.70) and 1.90 (1.20-2.90) weeks, respectively. Abnormal outcome trajectories were associated with clinically defined dysmature and disorganised EEG patterns, cementing the link between early maturational trajectories and neurodevelopmental outcome.
