Koolen Ninah, Oberdorfer Lisa, Rona Zsofia, Giordano Vito, Werther Tobias, Klebermass-Schrehof Katrin, Stevenson Nathan, Vanhatalo Sampsa
BABA Center, Department of Children's Clinical Neurophysiology, Children's Hospital, HUS Medical Imaging Center, Helsinki University Central Hospital and University of Helsinki, Finland.
Medical University Vienna, Department of Pediatrics, Division of Neonatology, Pediatric Intensive Care and Neuropediatrics, Vienna, Austria.
Clin Neurophysiol. 2017 Jun;128(6):1100-1108. doi: 10.1016/j.clinph.2017.02.025. Epub 2017 Mar 15.
To develop a method for automated neonatal sleep state classification based on EEG that can be applied over a wide range of age.
We collected 231 EEG recordings from 67 infants between 24 and 45weeks of postmenstrual age. Ten minute epochs of 8 channel polysomnography (N=323) from active and quiet sleep were used as a training dataset. We extracted a set of 57 EEG features from the time, frequency, and spatial domains. A greedy algorithm was used to define a reduced feature set to be used in a support vector machine classifier.
Performance tests showed that our algorithm was able to classify quiet and active sleep epochs with 85% accuracy, 83% sensitivity, and 87% specificity. The performance was not substantially lowered by reducing the epoch length or EEG channel number. The classifier output was used to construct a novel trend, the sleep state probability index, that improves the visualisation of brain state fluctuations.
A robust EEG-based sleep state classifier was developed. It performs consistently well across a large span of postmenstrual ages.
This method enables the visualisation of sleep state in preterm infants which can assist clinical management in the neonatal intensive care unit.
开发一种基于脑电图（EEG）的新生儿睡眠状态自动分类方法，该方法可应用于广泛的年龄段。
我们收集了67名孕龄在24至45周的婴儿的231份EEG记录。将来自主动睡眠和安静睡眠的8通道多导睡眠图的10分钟时段（N = 323）用作训练数据集。我们从时间、频率和空间域中提取了一组57个EEG特征。使用贪婪算法定义一个简化的特征集，用于支持向量机分类器。
性能测试表明，我们的算法能够以85%的准确率、83%的灵敏度和87%的特异性对安静和主动睡眠时段进行分类。减少时段长度或EEG通道数量并不会使性能大幅下降。分类器输出用于构建一种新的趋势，即睡眠状态概率指数，它改善了脑状态波动的可视化。
开发了一种强大的基于EEG的睡眠状态分类器。它在较大的孕龄范围内表现一致良好。
这种方法能够实现早产儿睡眠状态的可视化，有助于新生儿重症监护病房的临床管理。
