Unobtrusive cot side sleep stage classification in preterm infants using ultra-wideband radar

Arasteh, Emad; Groot, Eline R. de; Ende, Demi van den; Alderliesten, Thomas; Long, Xi; Goederen, Robbin de; Benders, Manon J. N. L.; Dudink, Jeroen

doi:10.3389/frsle.2023.1150962

Cited by 6 publications

(1 citation statement)

References 46 publications

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“…Previous studies have established a correlation between the peak frequency component of radar signals and heart and HRs [ 14 ]. However, in most research dealing with the young age range of patients, the cardiorespiratory features of patients are primarily utilized for sleep stage classification rather than directly determining HR and RR through radar [ 15 ]. This can be attributed to the intra-individual and inter-individual variability in HR and RR of young children.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Wideband Radar for Simultaneous and Unobtrusive Monitoring of Respiratory and Heart Rates in Early Childhood: A Deep Transfer Learning Approach

Arasteh,

Veldhoen,

Long

et al. 2023

Sensors

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Unobtrusive monitoring of children’s heart rate (HR) and respiratory rate (RR) can be valuable for promoting the early detection of potential health issues, improving communication with healthcare providers and reducing unnecessary hospital visits. A promising solution for wireless vital sign monitoring is radar technology. This paper presents a novel approach for the simultaneous estimation of children’s RR and HR utilizing ultra-wideband (UWB) radar using a deep transfer learning algorithm in a cohort of 55 children. The HR and RR are calculated by processing radar signals via spectrogram from time epochs of 10 s (25 sample length of hamming window with 90% overlap) and then transforming the resultant representation into 2-dimensional images. These images were fed into a pre-trained Visual Geometry Group-16 (VGG-16) model (trained on ImageNet dataset), with weights of five added layers fine-tuned using the proposed data. The prediction on the test data achieved a mean absolute error (MAE) of 7.3 beats per minute (BPM < 6.5% of average HR) and 2.63 breaths per minute (BPM < 7% of average RR). We also achieved a significant Pearson’s correlation of 77% and 81% between true and extracted for HR and RR, respectively. HR and RR samples are extracted every 10 s.

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Section: Introductionmentioning

confidence: 99%

Ultra-Wideband Radar for Simultaneous and Unobtrusive Monitoring of Respiratory and Heart Rates in Early Childhood: A Deep Transfer Learning Approach

Arasteh,

Veldhoen,

Long

et al. 2023

Sensors

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Association between sleep stages and brain microstructure in preterm infants: Insights from DTI analysis

de Groot,

Wang,

Wojtal

et al. 2024

Sleep Medicine

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Sleep as a driver of pre- and postnatal brain development

de Groot,

Dudink,

Austin

2024

Pediatr Res

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In 1966, Howard Roffwarg proposed the ontogenic sleep hypothesis, relating neural plasticity and development to rapid eye movement (REM) sleep, a hypothesis that current fetal and neonatal sleep research is still exploring. Recently, technological advances have enabled researchers to automatically quantify neonatal sleep architecture, which has caused a resurgence of research in this field as attempts are made to further elucidate the important role of sleep in pre- and postnatal brain development. This article will review our current understanding of the role of sleep as a driver of brain development and identify possible areas for future research. Impact The evidence to date suggests that Roffwarg’s ontogenesis hypothesis of sleep and brain development is correct. A better understanding of the relationship between sleep and the development of functional connectivity is needed. Reliable, non-invasive tools to assess sleep in the NICU and at home need to be tested in a real-world environment and the best way to promote healthy sleep needs to be understood before clinical trials promoting and optimizing sleep quality in neonates could be undertaken.

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Unobtrusive cot side sleep stage classification in preterm infants using ultra-wideband radar

Cited by 6 publications

References 46 publications

Ultra-Wideband Radar for Simultaneous and Unobtrusive Monitoring of Respiratory and Heart Rates in Early Childhood: A Deep Transfer Learning Approach

Ultra-Wideband Radar for Simultaneous and Unobtrusive Monitoring of Respiratory and Heart Rates in Early Childhood: A Deep Transfer Learning Approach

Association between sleep stages and brain microstructure in preterm infants: Insights from DTI analysis

Sleep as a driver of pre- and postnatal brain development

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