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孕龄相似的早产儿和足月儿睡眠的计算机分类

Computer classification of sleep in preterm and full-term neonates at similar postconceptional term ages.

作者信息

Scher M S, Dokianakis S G, Sun M, Steppe D A, Guthrie R D, Sclabassi R J

机构信息

Department of Pediatrics, Magee-Womens Hospital, Pittsburgh, Pennsylvania 15213, USA.

出版信息

Sleep. 1996 Jan;19(1):18-25. doi: 10.1093/sleep/19.1.18.

Abstract

A classification strategy of neonatal sleep is being developed by comparing visually scored minutes of 21 channels of electroencephalographic (EEG)/polygraphic recordings with the corresponding values for each physiological signal derived from either visual or computer analyses. Continuous 3-hour sleep studies on 54 preterm and full-term neonates at similar postconceptional term ages were acquired under environmentally controlled conditions using a computerized monitoring system. An on-line event marker program recorded behavioral observations. One of three EEG sleep states was assigned to each of 8,995 minutes by traditional visual analysis criteria. EEG spectral values, spectral and nonspectral cardiorespiratory calculations and behaviorally observed movements, arousals and rapid eye movement counts were submitted for discriminant analysis. Based on the total minutes known for each of three states (i.e. active, quiet and awake), linear combinations of all specified digitized parameters were formed into an arithmetic algorithm by use of discriminant analysis, which served as the basis of a state assignment for each minute. Fifty percent of the data were arbitrarily used as the training set to derive the state classification model. The remaining fifty percent of the data were used as the cross-validation "test sample" to determine the accuracy of the classification when compared to the visually analyzed score for each corresponding minute. Thirteen out of 32 physiological measures best predicted state of both preterm and full-term neonatal groups. For both groups, the correct classification for active sleep was 90.3%, quiet sleep was 97.4%, awake was 97% and the overall accuracy was 93.3%. However, the order of significance for specific variables differed between these two neonatal groups. Differences in the order of variables that predict sleep states between preterm and full-term infants may reflect adaptation of brain function of the preterm infant to prematurity and/or prolonged extrauterine experience.

摘要

通过将21通道脑电图(EEG)/多导记录的视觉评分分钟数与通过视觉或计算机分析得出的每个生理信号的相应值进行比较,正在开发一种新生儿睡眠分类策略。在环境受控条件下,使用计算机监测系统对54名孕龄相似的早产和足月新生儿进行了连续3小时的睡眠研究。一个在线事件标记程序记录了行为观察结果。根据传统视觉分析标准,为8995分钟中的每一分钟分配三种EEG睡眠状态之一。提交EEG频谱值、频谱和非频谱心肺计算结果以及行为观察到的运动、觉醒和快速眼动计数,进行判别分析。基于三种状态(即活跃、安静和清醒)各自已知的总分钟数,通过判别分析将所有指定数字化参数的线性组合形成一个算术算法,该算法作为每分钟状态分配的基础。任意使用50%的数据作为训练集来推导状态分类模型。其余50%的数据用作交叉验证“测试样本”,以确定与每个相应分钟的视觉分析评分相比时分类的准确性。32项生理指标中的13项最能预测早产和足月新生儿组的状态。对于两组,活跃睡眠的正确分类率为90.3%,安静睡眠为97.4%,清醒为97%,总体准确率为93.3%。然而,这两个新生儿组中特定变量的显著性顺序不同。早产和足月婴儿之间预测睡眠状态的变量顺序差异可能反映了早产儿脑功能对早产和/或宫外长期经历的适应情况。

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