Montazeri Saeed, Nevalainen Päivi, Stevenson Nathan J, Vanhatalo Sampsa
BABA Center, Department of Clinical Neurophysiology, HUS diagnostic center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Physiology, University of Helsinki, Helsinki, Finland.
BABA Center, Department of Clinical Neurophysiology, HUS diagnostic center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Clin Neurophysiol. 2022 Nov;143:75-83. doi: 10.1016/j.clinph.2022.08.022. Epub 2022 Sep 9.
To develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units.
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 independent dataset from 30 polysomnography recordings. In addition, we constructed Sleep State Trend (SST), a bedside-ready means for visualizing classifier outputs.
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 a polysomnography dataset, showing 81% overall accuracy despite different signal derivations. SST allowed an intuitive, clear visualization of the classifier output.
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.
The Sleep State Trend (SST) may provide caregivers and clinical studies a real-time view of sleep state fluctuations and its cyclicity.
开发并验证一种用于新生儿重症监护病房睡眠状态波动床边监测的自动化方法。
使用来自30名近足月新生儿长期（a）脑电图监测的53份脑电图记录设计并训练了一种基于深度学习的算法。使用来自30份多导睡眠图记录的独立数据集对结果进行验证。此外，我们构建了睡眠状态趋势（SST），这是一种便于床边使用的可视化分类器输出的方法。
训练数据中安静睡眠检测的准确率为90%，在4电极记录的所有双极导联中准确率相当（85 - 86%）。该算法对多导睡眠图数据集具有良好的泛化能力，尽管信号导联不同，但总体准确率仍达81%。SST能够直观、清晰地可视化分类器输出。
可以从单个脑电图通道高保真地检测睡眠状态波动，并且结果可以在床边监护仪中可视化为透明且直观的趋势。
睡眠状态趋势（SST）可为护理人员和临床研究提供睡眠状态波动及其周期性的实时视图。
