Gschwandtner Laura, Hartmann Manfred, Oberdorfer Lisa, Furbass Franz, Klebermas-Schrehof Katrin, Werther Tobias, Stevenson Nathan, Gritsch Gerhard, Perko Hannes, Berger Angelika, Kluge Tilmann, Giordano Vito
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:104-107. doi: 10.1109/EMBC44109.2020.9175380.
EEG monitoring of early brain function and development in neonatal intensive care units may help to identify infants with high risk of serious neurological impairment and to assess brain maturation for evaluation of neurodevelopmental progress. Automated analysis of EEG data makes continuous evaluation of brain activity fast and accessible. A convolutional neural network (CNN) for regression of EEG maturational age of premature neonates from marginally preprocessed serial EEG recordings is proposed. The CNN was trained and validated using 141 EEG recordings from 43 preterm neonates born below 28 weeks of gestation with normal neurodevelop-mental outcome at 12 months of corrected age. The estimated functional brain maturation between the first and last EEG recording increased in each patient. On average over 96% of repeated measures within an infant had an increasing EEG maturational age according to the post menstrual age at EEG recording time. Our algorithm has potential to be deployed to support neonatologists for accurate estimation of functional brain maturity in premature neonates.
在新生儿重症监护病房对早期脑功能和发育进行脑电图监测，可能有助于识别有严重神经功能障碍高风险的婴儿，并评估脑成熟度以评估神经发育进程。脑电图数据的自动分析使对脑活动的持续评估变得快速且可实现。本文提出了一种卷积神经网络（CNN），用于从未经充分预处理的早产儿系列脑电图记录中回归脑电图成熟年龄。使用来自43例孕周小于28周、矫正年龄12个月时神经发育结局正常的早产儿的141份脑电图记录对该CNN进行训练和验证。每位患者在首次和最后一次脑电图记录之间估计的功能性脑成熟度均有所增加。根据脑电图记录时的孕龄，婴儿内部平均超过96%的重复测量显示脑电图成熟年龄增加。我们的算法有潜力被应用，以支持新生儿科医生准确估计早产儿的功能性脑成熟度。
