论文标题
新生儿脑电图对低氧 - 缺血性脑病的背景异常严重程度等级
Neonatal EEG graded for severity of background abnormalities in hypoxic-ischaemic encephalopathy
论文作者
论文摘要
本报告描述了一组新生儿脑电图（EEG）记录，根据背景模式中异常的严重程度分级。该数据集由来自新生儿重症监护病房记录的53个新生儿的169小时多通道脑电图组成。所有新生儿均诊断出缺氧 - 缺血性脑病（HIE），这是成年婴儿脑损伤的最常见原因。对于每种新生儿，选择了多个1小时的高质量脑电图，然后对背景异常进行评分。分级系统评估eeg属性，例如振幅和频率，连续性，睡眠 - 循环循环，对称性和同步性以及异常波形。然后将背景严重程度分为4年级：正常或轻度异常的脑电图，中度异常的脑电图，严重异常的脑电图和不活跃的脑电图。数据可用作用于HIE，用于脑电图训练目的的新生儿的多渠道脑电图的参考集，或用于开发和评估自动化分级算法。
This report describes a set of neonatal electroencephalogram (EEG) recordings graded according to the severity of abnormalities in the background pattern. The dataset consists of 169 hours of multichannel EEG from 53 neonates recorded in a neonatal intensive care unit. All neonates received a diagnosis of hypoxic-ischaemic encephalopathy (HIE), the most common cause of brain injury in full term infants. For each neonate, multiple 1-hour epochs of good quality EEG were selected and then graded for background abnormalities. The grading system assesses EEG attributes such as amplitude and frequency, continuity, sleep--wake cycling, symmetry and synchrony, and abnormal waveforms. Background severity was then categorised into 4 grades: normal or mildly abnormal EEG, moderately abnormal EEG, severely abnormal EEG, and inactive EEG. The data can be used as a reference set of multi-channel EEG for neonates with HIE, for EEG training purposes, or for developing and evaluating automated grading algorithms.
