A scoping review of behavioral sleep stage classification methods for preterm infants

Bik, Anne; Sam, C.; Groot, Eline R. de; Visser, Simone S.M.; Wang, X.; Tataranno, Maria Luisa; Benders, Manon J. N. L.; Hoogen, Agnes van den; Dudink, Jeroen

doi:10.1016/j.sleep.2022.01.006

Cited by 12 publications

(8 citation statements)

References 44 publications

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“…23 In the context of neonatal sleep, it is difficult to correctly distinguish wake and sleep, since active sleep may resemble waking activity to inexperienced observers. 17,18 Higher self-reported knowledge was related to more frequent adaption of elective care to sleep. This might imply that healthcare professionals may adapt elective care to their own perception of sleep and wake.…”

Section: Discussionmentioning

confidence: 99%

“…This corresponds with a similar small study, including only 30 nurses, finding that 20% of nurses were able to select the correct characteristics of active sleep 22. Findings indicate that self-reported knowledge may not always align with knowledge tested using science-based questions, with participants tending to “overclaim” their personal expertise favorably, in other words, “overprecision.”23 In the context of neonatal sleep, it is difficult to correctly distinguish wake and sleep, since active sleep may resemble waking activity to inexperienced observers 17,18…”

Section: Discussionmentioning

confidence: 99%

“…Recently, our research group published a review18 and a behavioral scoring system17 on sleep stages. It should be noted that the current consensus about the appearance of active sleep may resemble discomfort, according to frequently used comfort scales 33.…”

Section: Discussionmentioning

confidence: 99%

“…Neonatal sleep consists of 2 main stages: active sleep and quiet sleep 15,16. Quiet sleep is easily detected as a sleep stage, since it is characterized by a still posture, closed eyes, and a regular heart and breathing rate 17,18. Active sleep, however, is characterized by frequent body and facial movements and twitches, rapid eye movements—sometimes with fluttering eyelids—and an irregular heart and breathing rate 17,18.…”

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confidence: 99%

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Evaluation of Sleep Practices and Knowledge in Neonatal Healthcare

de Groot,

Ryan,

Sam

et al. 2023

Advances in Neonatal Care

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Background: Developmental care is designed to optimize early brain maturation by integrating procedures that support a healing environment. Protecting preterm sleep is important in developmental care. However, it is unclear to what extent healthcare professionals are aware of the importance of sleep and how sleep is currently implemented in the day-to-day care in the neonatal intensive care unit (NICU). Purpose: Identifying the current state of knowledge among healthcare professionals regarding neonatal sleep and how this is transferred to practice. Methods: A survey was distributed among Dutch healthcare professionals. Three categories of data were sought, including (1) demographics of respondents; (2) questions relating to sleep practices; and (3) objective knowledge questions relating to sleep physiology and importance of sleep. Data were analyzed using Spearman's rho test and Cramer's V test. Furthermore, frequency tables and qualitative analyses were employed. Results: The survey was completed by 427 participants from 34 hospitals in 25 Dutch cities. While healthcare professionals reported sleep to be especially important for neonates admitted in the NICU, low scores were achieved in the area of knowledge of sleep physiology. Most healthcare professionals (91.8%) adapted the timing of elective care procedures to sleep. However, sleep assessments were not based on scientific knowledge. Therefore, the difference between active sleep and wakefulness may often be wrongly assessed. Finally, sleep is rarely discussed between colleagues (27.4% regularly/always) and during rounds (7.5%-14.3% often/always). Implications: Knowledge about sleep physiology should be increased through education among neonatal healthcare professionals. Furthermore, sleep should be considered more often during rounds and handovers.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Evaluation of Sleep Practices and Knowledge in Neonatal Healthcare

de Groot,

Ryan,

Sam

et al. 2023

Advances in Neonatal Care

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“…In early studies, manual sleep stage classification by human sleep experts was tedious and time-consuming, with highly subjective classification results [ 20 , 21 ]. The application of traditional sleep staging methods includes the use of machine learning, such as RF and SVM.…”

Section: Related Workmentioning

confidence: 99%

Multi-Layer Graph Attention Network for Sleep Stage Classification Based on EEG

Wang

Guo

Shen

et al. 2022

Sensors

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Graph neural networks have been successfully applied to sleep stage classification, but there are still challenges: (1) How to effectively utilize epoch information of EEG-adjacent channels owing to their different interaction effects. (2) How to extract the most representative features according to confused transitional information in confused stages. (3) How to improve classification accuracy of sleep stages compared with existing models. To address these shortcomings, we propose a multi-layer graph attention network (MGANet). Node-level attention prompts the graph attention convolution and GRU to focus on and differentiate the interaction between channels in the time-frequency domain and the spatial domain, respectively. The multi-head spatial-temporal mechanism balances the channel weights and dynamically adjusts channel features, and a multi-layer graph attention network accurately expresses the spatial sleep information. Moreover, stage-level attention is applied to easily confused sleep stages, which effectively improves the limitations of a graph convolutional network in large-scale graph sleep stages. The experimental results demonstrated classification accuracy; MF1 and Kappa reached 0.825, 0.814, and 0.775 and 0.873, 0.801, and 0.827 for the ISRUC and SHHS datasets, respectively, which showed that MGANet outperformed the state-of-the-art baselines.

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Methodological quality of 100 recent systematic reviews of health-related outcome measurement instruments: an overview of reviews

Elsman,

Mokkink,

Abma

et al. 2024

Qual Life Res

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Purpose Systematic reviews evaluating and comparing the measurement properties of outcome measurement instruments (OMIs) play an important role in OMI selection. Earlier overviews of review quality (2007, 2014) evidenced substantial concerns with regards to alignment to scientific standards. This overview aimed to investigate whether the quality of recent systematic reviews of OMIs lives up to the current scientific standards. Methods One hundred systematic reviews of OMIs published from June 1, 2021 onwards were randomly selected through a systematic literature search performed on March 17, 2022 in MEDLINE and EMBASE. The quality of systematic reviews was appraised by two independent reviewers. An updated data extraction form was informed by the earlier studies, and results were compared to these earlier studies’ findings. Results A quarter of the reviews had an unclear research question or aim, and in 22% of the reviews the search strategy did not match the aim. Half of the reviews had an incomprehensive search strategy, because relevant search terms were not included. In 63% of the reviews (compared to 41% in 2014 and 30% in 2007) a risk of bias assessment was conducted. In 73% of the reviews (some) measurement properties were evaluated (58% in 2014 and 55% in 2007). In 60% of the reviews the data were (partly) synthesized (42% in 2014 and 7% in 2007); evaluation of measurement properties and data syntheses was not conducted separately for subscales in the majority. Certainty assessments of the quality of the total body of evidence were conducted in only 33% of reviews (not assessed in 2014 and 2007). The majority (58%) did not make any recommendations on which OMI (not) to use. Conclusion Despite clear improvements in risk of bias assessments, measurement property evaluation and data synthesis, specifying the research question, conducting the search strategy and performing a certainty assessment remain poor. To ensure that systematic reviews of OMIs meet current scientific standards, more consistent conduct and reporting of systematic reviews of OMIs is needed.

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A scoping review of behavioral sleep stage classification methods for preterm infants

Cited by 12 publications

References 44 publications

Evaluation of Sleep Practices and Knowledge in Neonatal Healthcare

Evaluation of Sleep Practices and Knowledge in Neonatal Healthcare

Multi-Layer Graph Attention Network for Sleep Stage Classification Based on EEG

Methodological quality of 100 recent systematic reviews of health-related outcome measurement instruments: an overview of reviews

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