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

## TL;DR

This study evaluates an inter-burst detection method originally designed for preterm infants to classify injury severity in term infant EEGs, demonstrating that simple inter-burst measures can effectively grade hypoxic-ischemic encephalopathy.

## Contribution

It adapts and validates an existing inter-burst detection technique for term infants, showing its effectiveness in injury classification using machine learning.

## Key findings

- Best feature, percentage of inter-bursts, achieved 59.3% accuracy.
- Combining features with an MLP improved accuracy to 77.8%.
- Simple inter-burst interval measures can classify injury grades effectively.

## Abstract

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.