ArticleFrontiers in sleep2023
Unobtrusive cot side sleep stage classification in preterm infants using ultra-wideband radar.
Article in Frontiers in sleep, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- EEG electrode setup optimization using feature extraction techniques for neonatal sleep state classification.Frontiers in computational neuroscience · 2025Article
- Sleep as a driver of pre- and postnatal brain development.Pediatric research · 2024Review
- Emerging innovations in neonatal monitoring: a comprehensive review of progress and potential for non-contact technologies.Frontiers in pediatrics · 2024Review
- Ultra-Wideband Radar for Simultaneous and Unobtrusive Monitoring of Respiratory and Heart Rates in Early Childhood: A Deep Transfer Learning Approach.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Sleep is an important driver of development in infants born preterm. However, continuous unobtrusive sleep monitoring of infants in the neonatal intensive care unit (NICU) is challenging. Objective: To assess the feasibility of ultra-wideband (UWB) radar for sleep stage classification in preterm infants admitted to the NICU. Methods: Active and quiet sleep were visually assessed using video recordings in 10 preterm infants (recorded between 29 and 34 weeks of postmenstrual age) admitted to the NICU. UWB radar recorded all infant's motions during the video recordings. From the baseband data measured with the UWB radar, a total of 48 features were calculated. All features were related to body and breathing movements. Six machine learning classifiers were compared regarding their ability to reliably classify active and quiet sleep using these raw signals. Results: The adaptive boosting (AdaBoost) classifier achieved the highest balanced accuracy (81%) over a 10-fold cross-validation, with an area under the curve of receiver operating characteristics (AUC-ROC) of 0.82. Conclusions: The UWB radar data, using the AdaBoost classifier, is a promising method for non-obtrusive sleep stage assessment in very preterm infants admitted to the NICU.
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