论文标题
睡眠状态趋势（SST）的发展，基于单个EEG通道的新生儿睡眠状态波动的床边量度
Development of Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels
论文作者
论文摘要
目的：开发和验证一种自动化方法，用于对新生儿重症监护病房中睡眠状态波动的床边监测。 方法：基于深度学习的算法是使用30个近期新生儿中长期（a）EEG监测的53个EEG记录设计和训练的。使用来自30个多摄影记录的外部数据集对结果进行了验证。除了训练和验证单个EEG通道安静的睡眠探测器外，我们还构建了睡眠状态趋势（SST），这是一种可视化分类器输出的床旁准备手段。 结果：训练数据中安静的睡眠检测的准确性为90％，在4电极记录中获得的所有双极派生中，精度是可比的（85-86％）。该算法很好地概括了外部数据集，尽管信号推导不同，但仍显示81％的总体精度。 SST允许对分类器输出的直观，清晰可视化。 结论：可以从单个脑电图通道以高保真度检测到睡眠状态的波动，结果可以将结果视为床边监视器中透明和直观的趋势。 意义：睡眠状态趋势（SST）可以为护理人员提供对睡眠状态波动及其周期性的实时视图。
Objective: To develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units. Methods: A deep learning -based algorithm was designed and trained using 53 EEG recordings from a long-term (a)EEG monitoring in 30 near-term neonates. The results were validated using an external dataset from 30 polysomnography recordings. In addition to training and validating a single EEG channel quiet sleep detector, we constructed Sleep State Trend (SST), a bedside-ready means for visualizing classifier outputs. Results: The accuracy of quiet sleep detection in the training data was 90%, and the accuracy was comparable (85-86%) in all bipolar derivations available from the 4-electrode recordings. The algorithm generalized well to an external dataset, showing 81% overall accuracy despite different signal derivations. SST allowed an intuitive, clear visualization of the classifier output. Conclusions: Fluctuations in sleep states can be detected at high fidelity from a single EEG channel, and the results can be visualized as a transparent and intuitive trend in the bedside monitors. Significance: The Sleep State Trend (SST) may provide caregivers a real-time view of sleep state fluctuations and its cyclicity.
