Raurale Sumit A, Boylan Geraldine B, Lightbody Gordon, O'Toole John M
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:6103-6106. doi: 10.1109/EMBC44109.2020.9175337.
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.
脑电图（EEG）是一种用于评估出生时大脑因缺氧缺血所导致损伤程度的重要临床工具。本研究提出了一种新颖的端到端架构，该架构使用深度卷积神经网络，能够从原始脑电图数据中学习分层表示。该系统可对4级缺氧缺血性脑病进行分类，并在来自54名新生儿的63小时多通道脑电图数据集上进行评估。所提出的方法通过一步投票实现了79.6%的测试准确率，通过两步投票实现了81.5%的测试准确率。这些结果表明，一种无特征方法可用于对新生儿脑电图中的不同损伤等级进行分类，其准确率与现有的基于特征的系统相当。对新生儿背景脑电图进行自动分级有助于早期识别那些需要进行低温治疗等干预性治疗的婴儿。
