ArticleFrontiers in human neuroscience2021
Building an Open Source Classifier for the Neonatal EEG Background: A Systematic Feature-Based Approach From Expert Scoring to Clinical Visualization.
Article in Frontiers in human neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Early outcome-prediction with an automated EEG background trend in hypothermia-treated newborns with encephalopathy.Pediatric research · 2026Article
- Toward automated neonatal EEG analysis: multi-center validation of a reliable deep learning pipeline.Frontiers in neuroscience · 2026Article
- Neurophysiological changes associated with dysglycemia in term neonates with neonatal encephalopathy.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2025Article
- Quantitative EEG features during the first day correlate to clinical outcome in perinatal asphyxia.Pediatric research · 2025Article
- Automated assessment of EEG background for neurodevelopmental prediction in neonatal encephalopathy.Annals of clinical and translational neurology · 2024Article
- Networks of cortical activity show graded responses to perinatal asphyxia.Pediatric research · 2024Article
- Deep Learning for Generalized EEG Seizure Detection after Hypoxia-Ischemia-Preclinical Validation.Bioengineering (Basel, Switzerland) · 2024Article
- Neonatal EEG graded for severity of background abnormalities in hypoxic-ischaemic encephalopathy.Scientific data · 2023Article
- Early brain activity: Translations between bedside and laboratory.Progress in neurobiology · 2022Review
- Neurophysiology of the Developing Cerebral Cortex: What We Have Learned and What We Need to Know.Frontiers in cellular neuroscience · 2021Review
Corrections and comments
- Erratum issued
Authors and funding
13 authors at 6 institutions in 4 countries.
Funding
Abstract
Neonatal brain monitoring in the neonatal intensive care units (NICU) requires a continuous review of the spontaneous cortical activity, i.e., the electroencephalograph (EEG) background activity. This needs development of bedside methods for an automated assessment of the EEG background activity. In this paper, we present development of the key components of a neonatal EEG background classifier, starting from the visual background scoring to classifier design, and finally to possible bedside visualization of the classifier results. A dataset with 13,200 5-minute EEG epochs (8-16 channels) from 27 infants with birth asphyxia was used for classifier training after scoring by two independent experts. We tested three classifier designs based on 98 computational features, and their performance was assessed with respect to scoring system, pre- and post-processing of labels and outputs, choice of channels, and visualization in monitor displays. The optimal solution achieved an overall classification accuracy of 97% with a range across subjects of 81-100%. We identified a set of 23 features that make the classifier highly robust to the choice of channels and missing data due to artefact rejection. Our results showed that an automated bedside classifier of EEG background is achievable, and we publish the full classifier algorithm to allow further clinical replication and validation studies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.