ArticleAnnals of biomedical engineering2013
An automated system for grading EEG abnormality in term neonates with hypoxic-ischaemic encephalopathy.
Article in Annals of biomedical engineering, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 73 citations in OpenAlex.
- Automated EEG Background Analysis and 2-Year Outcomes in Neonatal Hypoxic-Ischemic Encephalopathy.JAMA network open · 2025Article
- Neurophysiological changes associated with dysglycemia in term neonates with neonatal encephalopathy.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2025Article
- Long-Term Neonatal EEG Modeling with DSP and ML for Grading Hypoxic-Ischemic Encephalopathy Injury.Sensors (Basel, Switzerland) · 2025Article
- Quantitative EEG features during the first day correlate to clinical outcome in perinatal asphyxia.Pediatric research · 2025Article
- Article
- The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.NPJ digital medicine · 2023Review
- Neonatal EEG graded for severity of background abnormalities in hypoxic-ischaemic encephalopathy.Scientific data · 2023Article
- Technical feasibility of multimodal imaging in neonatal hypoxic-ischemic encephalopathy from an ovine model to a human case series.Frontiers in pediatrics · 2023Article
- A method for AI assisted human interpretation of neonatal EEG.Scientific reports · 2022Article
- [Biomarkers and neuromonitoring for prognosis of development after perinatal brain damage].Monatsschrift Kinderheilkunde : Organ der Deutschen Gesellschaft fur Kinderheilkunde · 2022Review
- Can EEG accurately predict 2-year neurodevelopmental outcome for preterm infants?Archives of disease in childhood. Fetal and neonatal edition · 2021Observational
- Current Status and Future Directions of Neuromonitoring With Emerging Technologies in Neonatal Care.Frontiers in pediatrics · 2021Review
- Precision Medicine in Neonates: A Tailored Approach to Neonatal Brain Injury.Frontiers in pediatrics · 2021Review
- Building an Open Source Classifier for the Neonatal EEG Background: A Systematic Feature-Based Approach From Expert Scoring to Clinical Visualization.Frontiers in human neuroscience · 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 EEG background analysis to identify neonates with hypoxic-ischemic encephalopathy treated with hypothermia at risk for adverse outcome: A pilot study.Pediatrics and neonatology · 2019Article
- Functional maturation in preterm infants measured by serial recording of cortical activity.Scientific reports · 2017Article
- Lost in Transition: A Systematic Review of Neonatal Electroencephalography in the Delivery Room-Are We Forgetting an Important Biomarker for Newborn Brain Health?Frontiers in pediatrics · 2017Review
- Objective differentiation of neonatal EEG background grades using detrended fluctuation analysis.Frontiers in human neuroscience · 2015Article
- Computer-assisted interpretation of the EEG background pattern: a clinical evaluation.PloS one · 2014Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automated analysis of the neonatal EEG has the potential to assist clinical decision making for neonates with hypoxic-ischaemic encephalopathy. This paper proposes a method of automatically grading the degree of abnormality in an hour long epoch of neonatal EEG. The automated grading system (AGS) was based on a multi-class linear classifier grading of short-term epochs of EEG which were converted into a long-term grading of EEG using a majority vote operation. The features used in the AGS were summary measurements of two sub-signals extracted from a quadratic time-frequency distribution: the amplitude modulation and instantaneous frequency. These sub-signals were based on a model of EEG as a multiplication of a coloured random process with a slowly varying pseudo-periodic waveform and may be related to macroscopic neurophysiological function. The 4 grade AGS had a classification accuracy of 83% compared to human annotation of the EEG (level of agreement, κ = 0.76). Features estimated on the developed sub-signals proved more effective at grading the EEG than measures based solely on the EEG and the incorporation of additional sub-grades based on EEG states into the AGS also improved performance.
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Registered trials
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.