Murphy Keelin, Stevenson Nathan J, Goulding Robert M, Lloyd Rhodri O, Korotchikova Irina, Livingstone Vicki, Boylan Geraldine B
Neonatal Brain Research Group, University College Cork, Cork, Ireland; Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.
Neonatal Brain Research Group, University College Cork, Cork, Ireland; Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.
Clin Neurophysiol. 2015 Sep;126(9):1692-702. doi: 10.1016/j.clinph.2014.11.024. Epub 2014 Dec 9.
To develop and validate two automatic methods for the detection of burst and interburst periods in preterm eight-channel electroencephalographs (EEG). To perform a detailed analysis of interobserver agreement on burst and interburst periods and use this as a benchmark for the performance of the automatic methods. To examine mathematical features of the EEG signal and their potential correlation with gestational age.
Multi-channel EEG from 36 infants, born at less than 30 weeks gestation was utilised, with a 10 min artifact-free epoch selected for each subject. Three independent expert observers annotated all EEG activity bursts in the dataset. Two automatic algorithms for burst/interburst detection were applied to the EEG data and their performances were analysed and compared with interobserver agreement. A total of 12 mathematical features of the EEG signal were calculated and correlated with gestational age.
The mean interobserver agreement was found to be 77% while mean algorithm/observer agreement was 81%. Six of the mathematical features calculated (spectral entropy, Higuchi fractal dimension, spectral edge frequency, variance, extrema median and Hilberts transform amplitude) were found to have significant correlation with gestational age.
Automatic detection of burst/interburst periods has been performed in multi-channel EEG of 36 preterm infants. The algorithm agreement with expert observers is found to be on a par with interobserver agreement. Mathematical features of EEG have been calculated which show significant correlation with gestational age.
Automatic analysis of preterm multi-channel EEG is possible. The methods described here have the potential to be incorporated into a fully automatic system to quantitatively assess brain maturity from preterm EEG.
开发并验证两种自动检测早产儿八通道脑电图（EEG）中爆发期和爆发间期的方法。对观察者间关于爆发期和爆发间期的一致性进行详细分析，并将其作为自动方法性能的基准。研究EEG信号的数学特征及其与胎龄的潜在相关性。
使用36名孕周小于30周的婴儿的多通道EEG，为每个受试者选择10分钟无伪迹的时段。三名独立的专家观察者对数据集中的所有EEG活动爆发进行标注。将两种用于检测爆发/爆发间期的自动算法应用于EEG数据，并分析其性能，与观察者间的一致性进行比较。计算EEG信号的总共12个数学特征，并与胎龄进行相关性分析。
观察者间的平均一致性为77%，而算法与观察者的平均一致性为81%。发现计算出的六个数学特征（频谱熵、 Higuchi分形维数、频谱边缘频率、方差、极值中位数和希尔伯特变换幅度）与胎龄有显著相关性。
已在36名早产儿的多通道EEG中进行了爆发/爆发间期的自动检测。发现算法与专家观察者的一致性与观察者间的一致性相当。已计算出EEG的数学特征，其与胎龄有显著相关性。
对早产儿多通道EEG进行自动分析是可行的。这里描述的方法有可能被纳入一个全自动系统，以从早产儿EEG中定量评估脑成熟度。
