Creating an optimal observational sleep stage classification system for very and extremely preterm infants

Groot, Eline R. de; Bik, Anne; Sam, C.; Wang, X.; Shellhaas, Renée A.; Austin, Topun; Tataranno, Maria-Luisa; Benders, Manon J. N. L.; Hoogen, Agnes van den; Dudink, Jeroen

doi:10.1016/j.sleep.2022.01.020

Cited by 13 publications

(22 citation statements)

References 38 publications

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“…Another limitation is that the behavioral sleep observations were performed by one observer at a time in this study to prevent crowding in the NICU. Despite the overall high reliability and validity of the employed behavioral sleep–wake observation scale [ 29 ], this could inherently limit the potential performance of our model because some observers only achieved moderate reliability (κ = 0.46–0.82). Nonetheless, by including various experiences and knowledge of different observers, the SWB model may transcend the limitations of individual observers and be considered more stable and reliable [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

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

Sentner

Wang

Groot

et al. 2022

Sleep

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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. Independent training (n = 37) and validation datasets were used (n = 9). Finally, a setup was designed for real-time implementation at the bedside. Results The macro-averaged area-under-the-receiver-operator-characteristic (AUROC) of the automated sleep staging algorithm ranged between 0.69 and 0.82 for the training data, and 0.61 and 0.78 for the validation data. The algorithm provided the most accurate prediction for wake states (AUROC = 0.80). These findings were well validated on an independent sample (AUROC = 0.77). Conclusions With this study, to the best of our knowledge, a reliable, non-obtrusive, and real-time sleep staging algorithm was developed for the first time for preterm infants. Deploying this algorithm in the NICU environment may assist and adapt bedside clinical work based on infants’ sleep-wake states, potentially promoting the early brain development and well-being of preterm infants.

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Section: Discussionmentioning

confidence: 99%

“…A behavioral sleep–wake state classification system developed in-house was used for sleep observation, producing four different sleep–wake states: AS, QS, IS, and W [ 29 ]. Four observers performed sleep–wake state annotations for the training dataset and two other observers for the validation dataset.…”

Section: Methodsmentioning

confidence: 99%

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

Sentner

Wang

Groot

et al. 2022

Sleep

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“…SWB started as a grassroots project winning the best innovation price at Dutch Hacking Health 2019 1 It is an in-house developed ML model intended for monitoring real-time sleep-wake patterns in preterm neonates between 28 and 34 weeks gestational age 2 For the untrained caregiver it is almost impossible to accurately assess the sleep-wake state of preterm infants ( 21 ). The added value of real-time sleep-wake state monitoring comes from adapting elective clinical management of these preterm infants toward less disturbance during sleep periods.…”

Section: Sleep-well-babymentioning

confidence: 99%

“…During this development process, quality standards including IEC62304, ISO14971, and internal AI/ML standards were applied. Technical and clinical validation was performed by comparing predictions against a ground truth, namely sleep-wake state observations by a highly-trained and internally-calibrated team of students according to a standardized observation method ( 21 ). In Figure 1 an overview is given of SWB development and implementation at the NICU of the WKZ.…”

Section: Sleep-well-babymentioning

confidence: 99%

A Perspective on a Quality Management System for AI/ML-Based Clinical Decision Support in Hospital Care

Bartels

Dudink

Haitjema

et al. 2022

Front. Digit. Health

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Although many artificial intelligence (AI) and machine learning (ML) based algorithms are being developed by researchers, only a small fraction has been implemented in clinical-decision support (CDS) systems for clinical care. Healthcare organizations experience significant barriers implementing AI/ML models for diagnostic, prognostic, and monitoring purposes. In this perspective, we delve into the numerous and diverse quality control measures and responsibilities that emerge when moving from AI/ML-model development in a research environment to deployment in clinical care. The Sleep-Well Baby project, a ML-based monitoring system, currently being tested at the neonatal intensive care unit of the University Medical Center Utrecht, serves as a use-case illustrating our personal learning journey in this field. We argue that, in addition to quality assurance measures taken by the manufacturer, user responsibilities should be embedded in a quality management system (QMS) that is focused on life-cycle management of AI/ML-CDS models in a medical routine care environment. Furthermore, we highlight the strong similarities between AI/ML-CDS models and in vitro diagnostic devices and propose to use ISO15189, the quality guideline for medical laboratories, as inspiration when building a QMS for AI/ML-CDS usage in the clinic. We finally envision a future in which healthcare institutions run or have access to a medical AI-lab that provides the necessary expertise and quality assurance for AI/ML-CDS implementation and applies a QMS that mimics the ISO15189 used in medical laboratories.

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“…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.…”

mentioning

confidence: 99%

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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Creating an optimal observational sleep stage classification system for very and extremely preterm infants

Cited by 13 publications

References 38 publications

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

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

A Perspective on a Quality Management System for AI/ML-Based Clinical Decision Support in Hospital Care

Evaluation of Sleep Practices and Knowledge in Neonatal Healthcare

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