# Grading the severity of hypoxic-ischemic encephalopathy in newborn EEG using a convolutional neural network

arXiv: https://arxiv.org/abs/2005.05561 · HTML: https://mlsanity.com/paper/2005.05561/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2020.

## Built on by (most cited first, 1 of 1)

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

## Abstract (arXiv)

Electroencephalography (EEG) is a valuable clinical tool for grading injury caused by lack of blood and oxygen to the brain during birth. This study presents a novel end-to-end architecture, using a deep convolutional neural network, that learns hierarchical representations within raw EEG data. The system classifies 4 grades of hypoxic-ischemic encephalopathy and is evaluated on a multi-channel EEG dataset of 63 hours from 54 newborns. The proposed method achieves a testing accuracy of 79.6% with one-step voting and 81.5% with two-step voting. These results show how a feature-free approach can be used to classify different grades of injury in newborn EEG with comparable accuracy to existing feature-based systems. Automated grading of newborn background EEG could help with the early identification of those infants in need of interventional therapies such as hypothermia.