# Neonatal EEG graded for severity of background abnormalities in hypoxic-ischaemic encephalopathy

arXiv: https://arxiv.org/abs/2206.04420 · HTML: https://mlsanity.com/paper/2206.04420/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 9 Jun 2022.

## Builds on (most cited first, 4 of 4)

- [NEURAL: quantitative features for newborn EEG using Matlab](https://mlsanity.com/paper/1704.05694/) (2017)
- [Suitability of an inter-burst detection method for grading hypoxic-ischemic encephalopathy in newborn EEG](https://mlsanity.com/paper/1907.02877/) (2019)
- [Grading the severity of hypoxic-ischemic encephalopathy in newborn EEG using a convolutional neural network](https://mlsanity.com/paper/2005.05561/) (2020)
- [Trac'e alternant detector for grading hypoxic-ischemic encephalopathy in neonatal EEG](https://mlsanity.com/paper/2106.00061/) (2021)

## Built on by (most cited first, 2 of 2)

- [Machine-learning competition to grade EEG background patterns in newborns with hypoxic-ischaemic encephalopathy](https://mlsanity.com/paper/2509.09695/) (2025)
- [Expert-level detection of low-voltage intervals in EEG as a robust biomarker of neonatal encephalopathy](https://mlsanity.com/paper/2610.12680/) (2026)

## Abstract (arXiv)

This report describes a set of neonatal electroencephalogram (EEG) recordings graded according to the severity of abnormalities in the background pattern. The dataset consists of 169 hours of multichannel EEG from 53 neonates recorded in a neonatal intensive care unit. All neonates received a diagnosis of hypoxic-ischaemic encephalopathy (HIE), the most common cause of brain injury in full term infants. For each neonate, multiple 1-hour epochs of good quality EEG were selected and then graded for background abnormalities. The grading system assesses EEG attributes such as amplitude and frequency, continuity, sleep--wake cycling, symmetry and synchrony, and abnormal waveforms. Background severity was then categorised into 4 grades: normal or mildly abnormal EEG, moderately abnormal EEG, severely abnormal EEG, and inactive EEG. The data can be used as a reference set of multi-channel EEG for neonates with HIE, for EEG training purposes, or for developing and evaluating automated grading algorithms.