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arXiv · 2208.11933

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

Saeed Montazeri Moghadam·Päivi Nevalainen·Nathan J. Stevenson·Sampsa Vanhatalo
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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.

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Saeed Montazeri Moghadam, Päivi Nevalainen, Nathan J. Stevenson, Sampsa Vanhatalo. 2022-08-25. Development of Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels. https://arxiv.org/abs/2208.11933

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