@inproceedings{ce22bb23c2eb421784862f9aca512194,
title = "Grading the severity of hypoxic-ischemic encephalopathy in newborn EEG using a convolutional neural network",
abstract = "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.",
author = "Raurale, \{Sumit A.\} and Boylan, \{Geraldine B.\} and Gordon Lightbody and O'Toole, \{John M.\}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 ; Conference date: 20-07-2020 Through 24-07-2020",
year = "2020",
month = jul,
doi = "10.1109/EMBC44109.2020.9175337",
language = "English",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6103--6106",
booktitle = "42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society",
address = "United States",
}