Scher M S, Sun M, Hatzilabrou G M, Greenberg N L, Cebulka G, Krieger D, Guthrie R D, Sclabassi R J
Developmental Neurophysiology Laboratory, Magee-Women's Hospital, Pittsburgh, PA 15210.
J Clin Neurophysiol. 1990 Jul;7(3):417-41. doi: 10.1097/00004691-199007000-00007.
Neonatal EEG interpretation can aid in the estimation of central nervous system maturation, as well as provide diagnostic and prognostic information of the high-risk infant. However, one cannot easily visualize the complex interrelationships coupling EEG and polysomnographic components of the EEG-sleep rhythm. This is particularly relevant for the preterm neonate, in whom a rudimentary sleep cycle has not yet been clearly delineated. Computer analysis can augment the information derived from the visual interpretation of scalp-generated EEG activity. Automated techniques for EEG-sleep analysis have only recently been applied to a neonatal population. Such studies have been limited to full-term rather than preterm infants and rely on conventional methods that assume stationarity of neurophysiologic signals. We describe a computer system that simultaneously compares behavioral and electrographic components of EEG-sleep in a manner that preserves the integrity of the signals over time, while investigating the time- and frequency-dependent relationships among signals. Strategies for on-line and off-line editing, data storage, and off-line signal processing are described. Computational algorithms regarding analyses of EEG power, motility, and cardiorespiratory data are being used to study the ontogeny of EEG-sleep in asymptomatic preterm and full-term neonates. Computer strategies are based on both principles of stationarity and nonstationarity of physiologic signals and are applied depending on the temporal resolution required for specific signal processing needs.
新生儿脑电图解读有助于评估中枢神经系统成熟度，还能为高危婴儿提供诊断和预后信息。然而，脑电图睡眠节律中脑电图和多导睡眠图成分之间复杂的相互关系并不容易直观呈现。这对于早产儿尤为重要，因为其基本睡眠周期尚未清晰界定。计算机分析可以增加从头皮脑电图活动的视觉解读中获得的信息。脑电图睡眠分析的自动化技术直到最近才应用于新生儿群体。此类研究仅限于足月儿而非早产儿，且依赖于假定神经生理信号平稳性的传统方法。我们描述了一种计算机系统，该系统能以一种随时间保持信号完整性的方式同时比较脑电图睡眠的行为和电图成分，同时研究信号之间的时间和频率依赖关系。文中描述了在线和离线编辑、数据存储及离线信号处理的策略。关于脑电图功率、运动和心肺数据的计算算法正用于研究无症状早产儿和足月儿脑电图睡眠的个体发生。计算机策略基于生理信号的平稳性和非平稳性原理，并根据特定信号处理需求所需的时间分辨率来应用。
