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

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, we constructed Sleep State Trend (SST), a bedside-ready means for visualising 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 generalised well to an external dataset, showing 81% overall accuracy despite different signal derivations. SST allowed an intuitive, clear visualisation 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 visualised 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.