The Sleep Well Baby project: an automated real-time sleep-wake state prediction algorithm in preterm infants.

ABSTRACT:

Study objectives

Sleep is an important driver of early brain development. However, sleep is often disturbed in preterm infants admitted to the neonatal intensive care unit (NICU). We aimed to develop an automated algorithm based on routinely measured vital parameters to classify sleep-wake states of preterm infants in real-time at the bedside.

Methods

In this study, sleep-wake state observations were obtained in 1-minute epochs using a behavioral scale developed in-house while vital signs were recorded simultaneously. Three types of vital parameter data, namely, heart rate, respiratory rate, and oxygen saturation, were collected at a low-frequency sampling rate of 0.4 Hz. A supervised machine learning workflow was used to train a classifier to predict sleep-wake states. Inde

SUBMITTER: Sentner T 

PROVIDER: S-EPMC9548667 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature