ArticleMedical engineering & physics2017
Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach.
Article in Medical engineering & physics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Twitching in sleeping premature infants provides a sensitive behavioral assay of early motor control.iScience · 2026Article
- Ambient Music Decreases EEG Burst-Suppression Ratio During General Anesthesia in Rats.Sensors (Basel, Switzerland) · 2026Article
- Pain, Opioids, and Functional Connectivity in Preterm Infants.Children (Basel, Switzerland) · 2026Article
- Twitching in sleeping premature infants provides a sensitive behavioral assay of early motor control.bioRxiv : the preprint server for biology · 2026Article
- Quantitative electroencephalogram and machine learning to predict expired sevoflurane concentration in infants.Journal of clinical monitoring and computing · 2025Article
- 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
- Early EEG-burst sharpness and 2-year disability in extremely preterm infants.Pediatric research · 2024Article
- Machine learning for the early prediction of infants with electrographic seizures in neonatal hypoxic-ischemic encephalopathy.Epilepsia · 2023Article
- Early brain activity: Translations between bedside and laboratory.Progress in neurobiology · 2022Review
- Identifying tracé alternant activity in neonatal EEG using an inter-burst detection approach.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020Article
- Temporal evolution of quantitative EEG within 3 days of birth in early preterm infants.Scientific reports · 2019Article
- Quantitative Preterm EEG Analysis: The Need for Caution in Using Modern Data Science Techniques.Frontiers in pediatrics · 2019Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
aimTo develop a method that segments preterm EEG into bursts and inter-bursts by extracting and combining multiple EEG features.
methodsTwo EEG experts annotated bursts in individual EEG channels for 36 preterm infants with gestational age < 30 weeks. The feature set included spectral, amplitude, and frequency-weighted energy features. Using a consensus annotation, feature selection removed redundant features and a support vector machine combined features. Area under the receiver operator characteristic (AUC) and Cohen's kappa (κ) evaluated performance within a cross-validation procedure.
resultsThe proposed channel-independent method improves AUC by 4-5% over existing methods (p < 0.001, n=36), with median (95% confidence interval) AUC of 0.989 (0.973-0.997) and sensitivity-specificity of 95.8-94.4%. Agreement rates between the detector and experts' annotations, κ=0.72 (0.36-0.83) and κ=0.65 (0.32-0.81), are comparable to inter-rater agreement, κ=0.60 (0.21-0.74).
conclusionsAutomating the visual identification of bursts in preterm EEG is achievable with a high level of accuracy. Multiple features, combined using a data-driven approach, improves on existing single-feature methods.
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