ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2016
Estimating functional brain maturity in very and extremely preterm neonates using automated analysis of the electroencephalogram.
Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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22 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.
- Quantitative assessment of neurodevelopmental maturation: a comprehensive systematic literature review of artificial intelligence-based brain age prediction in pediatric populations.Frontiers in neuroinformatics · 2024Pooled it
- Applying a data-driven approach to quantify EEG maturational deviations in preterms with normal and abnormal neurodevelopmental outcomes.Scientific reports · 2020Trial
- A dataset of EEG and ECG recordings around the onset of NREM sleep from infancy to adolescence.Scientific data · 2026Article
- Brain complexity in response to auditory stimulation improves evaluation of cerebral maturation in premature newborns.Pediatric research · 2026Article
- EEG Maturational Age Estimation: A Comparison of Visual and Automated Interpretation of the EEG in Preterm Infants.Journal of clinical medicine · 2025Article
- Optimal channel and feature selection for automatic prediction of functional brain age of preterm infant based on EEG.Frontiers in neuroscience · 2025Article
- Resting state electroencephalographic brain activity in neonates can predict age and is indicative of neurodevelopmental outcome.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2024Article
- Sensory event-related potential morphology predicts age in premature infants.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2024Article
- Sleep state organisation of moderate to late preterm infants in the neonatal unit.Pediatric research · 2023Observational
- Examining time-frequency mechanisms of full-fledged deep sleep development in newborns of different gestational age in the first days of their postnatal development.Scientific reports · 2022Article
- Background suppression of electrical activity is a potential biomarker of subsequent brain injury in a rat model of neonatal hypoxia-ischemia.Journal of neurophysiology · 2022Article
- Relationship Between Early Functional and Structural Brain Developments and Brain Injury in Preterm Infants.Cerebellum (London, England) · 2021Article
- Grading the severity of hypoxic-ischemic encephalopathy in newborn EEG using a convolutional neural network.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020Article
- Automated cot-side tracking of functional brain age in preterm infants.Annals of clinical and translational neurology · 2020Article
- Temporal evolution of quantitative EEG within 3 days of birth in early preterm infants.Scientific reports · 2019Article
- Neural Correlates of Anesthesia in Newborn Mice and Humans.Frontiers in neural circuits · 2019Article
- Quantitative Preterm EEG Analysis: The Need for Caution in Using Modern Data Science Techniques.Frontiers in pediatrics · 2019Review
- Functional maturation in preterm infants measured by serial recording of cortical activity.Scientific reports · 2017Article
- An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation.International journal of neural systems · 2017Article
- Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach.Medical engineering & physics · 2017Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
objectiveTo develop an automated estimate of EEG maturational age (EMA) for preterm neonates.
methodsThe 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.
resultsThe 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.
conclusionsThe 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.
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