  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.
