论文标题
使用卷积神经网络对新生儿脑电图中低氧 - 缺血性脑病的严重程度进行评分
Grading the severity of hypoxic-ischemic encephalopathy in newborn EEG using a convolutional neural network
论文作者
论文摘要
脑电图（EEG）是由于出生时缺乏血液和氧对大脑缺乏血液和氧气引起的分级损伤的有价值的临床工具。这项研究使用深层卷积神经网络介绍了一种新颖的端到端体系结构，该结构在原始脑电图数据中学习了层次的表示。该系统对4年级的低氧 - 缺血性脑病进行了分类，并在54个新生儿的63小时的多渠道EEG数据集上进行了评估。拟议的方法以一步投票获得了79.6％的测试准确性，而两步投票则达到了81.5％。这些结果表明，如何使用无功能方法来对新生儿脑电图的不同等级的损伤进行分类，其精度与现有的基于功能的系统相当。新生儿背景的自动分级脑电图可以帮助对需要介入疗法（例如体温过低）的婴儿的早期鉴定。
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.
