# Suitability of an inter-burst detection method for grading hypoxic-ischemic encephalopathy in newborn EEG

arXiv: https://arxiv.org/abs/1907.02877 · HTML: https://mlsanity.com/paper/1907.02877/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2019.

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

- [Neonatal EEG graded for severity of background abnormalities in hypoxic-ischaemic encephalopathy](https://mlsanity.com/paper/2206.04420/) (2022)
- [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)

Electroencephalography (EEG) is an important clinical tool for grading injury caused by lack of oxygen or blood to the brain during birth. Characteristics of low-voltage waveforms, known as inter-bursts, are related to different grades of injury. This study assesses the suitability of an existing inter-burst detection method, developed from preterm infants born <30 weeks of gestational age, to detect inter-bursts in term infants. Different features from the temporal organisation of the inter-bursts are combined using a multi-layer perceptron (MLP) machine learning algorithm to classify four grades of injury in the EEG. We find that the best performing feature, percentage of inter-bursts, has an accuracy of 59.3%. Combining this with the maximum duration of inter-bursts in the MLP produces a testing accuracy of 77.8%, with similar performance to existing multi-feature methods. These results validate the use of the preterm detection method in term EEG and show how simple measures of the inter-burst interval can be used to classify different grades of injury.