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

TL;DR

This paper presents a deep learning-based method for real-time bedside monitoring of neonatal sleep state fluctuations using a single EEG channel, with a novel visualization tool called Sleep State Trend (SST).

Contribution

The study introduces a validated deep learning algorithm and the SST visualization tool for bedside neonatal sleep monitoring, demonstrating high accuracy and generalizability from EEG data.

Findings

90% accuracy in quiet sleep detection in training data

81% accuracy on external dataset with different EEG derivations

SST provides intuitive visualization of sleep state fluctuations

Abstract

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.