Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels

Abstract

ObjectiveTo develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units. MethodsA 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. ResultsThe 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. ConclusionsFluctuations 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. SignificanceThe Sleep State Trend (SST) may provide caregivers and clinical studies a real-time view of sleep state fluctuations and its cyclicity.