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An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation.

ABSTRACT: Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ([Formula: see text] age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27-42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement [Formula: see text]), using Sensitivity, Specificity, Detection Factor ([Formula: see text] of visual QS periods correctly detected by CLASS) and Misclassification Factor ([Formula: see text] of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31-38 weeks (median [Formula: see text], median MF 0-0.25, median Sensitivity 0.93-1.0, and median Specificity 0.80-0.91 across this age range), with minimal misclassifications at 35-36 weeks (median [Formula: see text]). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation.

SUBMITTER: Dereymaeker A

PROVIDER: S-EPMC6342251 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation.

Dereymaeker Anneleen A Pillay Kirubin K Vervisch Jan J Van Huffel Sabine S Naulaers Gunnar G Jansen Katrien K De Vos Maarten M

International journal of neural systems 20170224 6

Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ([Formula: see text] age) range, focusing first on Quiet Sleep (QS) as an initial marke ...[more]

PMID: 28460602

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Project description:Background: Sleep is an important determinant of brain development in preterm infants. Its temporal organization varies with gestational age (GA) and post-menstrual age (PMA) but little is known about how sleep develops in very preterm infants. The objective was to study the correlation between the temporal organization of quiet sleep (QS) and maturation in premature infants without severe complications during their neonatal hospitalization. Methods: Percentage of time spent in QS and average duration of time intervals (ADI) spent in QS were analyzed from a cohort of newborns with no severe complications included in the Digi-NewB prospective, multicentric, observational study in 2017-19. Three groups were analyzed according to GA: Group 1 (27-30 weeks), Group 2 (33-37 weeks), Group 3 (>39 weeks). Two 8-h video recordings were acquired in groups 1 and 2: after birth (T1) and before discharge from hospital (T2). The annotation of the QS phases was performed by analyzing video recordings together with heart rate and respiratory traces thanks to a dedicated software tool of visualization and annotation of multimodal long-time recordings, with a double expert reading. Results are expressed as median (interquartile range, IQR). Correlations were analyzed using a linear mixed model. Results: Five newborns were studied in each group (160 h of recording). Median time spent in QS increased from 13.0% [IQR: 13-20] to 28.8% [IQR: 27-30] and from 17.0% [IQR: 15-21] to 29.6% [IQR: 29.5-31.5] in Group 1 and 2, respectively. Median ADI increased from 54 [IQR: 53-54] to 288 s [IQR: 279-428] and from 90 [IQR: 84-96] to 258 s [IQR: 168-312] in Group 1 and 2. Both groups reach values similar to that of group 3, respectively 28.2% [IQR: 24.5-31.3] and 270 s [IQR: 210-402]. The correlation between PMA and time spent in QS or ADI were, respectively 0.73 (p < 10-4) and 0.46 (p = 0.06). Multilinear analysis using temporal organization of QS gave an accurate estimate of PMA (r 2 = 0.87, p < 0.001). Conclusion: The temporal organization of QS is correlated with PMA in newborns without severe complication. An automated standardized continuous behavioral quantification of QS could be interesting to monitor during the hospitalization stay in neonatal units.

Project description:Diffuse white matter abnormality (DWMA), or diffuse excessive high signal intensity is observed in 50-80% of very preterm infants at term-equivalent age. It is subjectively defined as higher than normal signal intensity in periventricular and subcortical white matter in comparison to normal unmyelinated white matter on T2-weighted MRI images. Despite the well-documented presence of DWMA, it remains debatable whether DWMA represents pathological tissue injury or a transient developmental phenomenon. Manual tracing of DWMA exhibits poor reliability and reproducibility and unduly increases image processing time. Thus, objective and ideally automatic assessment is critical to accurately elucidate the biologic nature of DWMA. We propose a deep learning approach to automatically identify DWMA regions on T2-weighted MRI images. Specifically, we formulated DWMA detection as an image voxel classification task; that is, the voxels on T2-weighted images are treated as samples and exclusively assigned as DWMA or normal white matter voxel classes. To utilize the spatial information of individual voxels, small image patches centered on the given voxels are retrieved. A deep convolutional neural networks (CNN) model was developed to differentiate DWMA and normal voxels. We tested our deep CNN in multiple validation experiments. First, we examined DWMA detection accuracy of our CNN model using computer simulations. This was followed by in vivo assessments in a cohort of very preterm infants (N = 95) using cross-validation and holdout validation. Finally, we tested our approach on an independent preterm cohort (N = 28) to externally validate our model. Our deep CNN model achieved Dice similarity index values ranging from 0.85 to 0.99 for DWMA detection in the aforementioned validation experiments. Our proposed deep CNN model exhibited significantly better performance than other popular machine learning models. We present an objective and automated approach for accurately identifying DWMA that may facilitate the clinical diagnosis of DWMA in very preterm infants.

Project description:BackgroundPrevious studies demonstrated a short-term relationship between infant sleep-wake states and oral feeding performance, with state being an indication of infants' neurobehavioral readiness for feeding. However, the relationship between sleep-wake states and feeding skills has not been evaluated longitudinally during hospitalization.ObjectivesThe purpose of this study was to examine preterm infants' sleep-wake state developmental trajectories and their associations with feeding progression during hospitalization.MethodsThis descriptive and exploratory study was a secondary analysis using data from a longitudinal two-group, randomized controlled trial evaluating the effects of early and late cycled light on health and developmental outcomes among extremely preterm infants who were born ≤28 weeks of gestational age. Sleep-wake states were assessed for two 2-hour interfeeding periods per day (day and night hours), 30 weeks postmenstrual age, and every 3 weeks until discharge. Occurrences of active sleep, quiet sleep, and waking were recorded every 10 seconds. Feeding progression was assessed based on an infant's postmenstrual age at five milestones: first enteral feeding, full enteral feeding, first oral feeding, half oral feeding, and full oral feeding. Trajectory analyses were used to describe developmental changes in sleep-wake states, feeding progression patterns, and associations between feeding progression and sleep-wake trajectories.ResultsActive sleep decreased while waking, and quiet sleep increased during hospitalization. Two distinct feeding groups were identified: typical and delayed feeding progression. In infants with delayed feeding progression, rates of active and quiet sleep development during the day were delayed compared to those with typical feeding progression. We also found that infants with delayed feeding progression were more likely to be awake more often during the night compared to infants with typical feeding progression.DiscussionsFindings suggest that delays in sleep-wake state development may be associated with delays in feeding progression during hospitalization. Infants with delayed feeding skill development may require more environmental protection to further support their sleep development.

Dataset Information

An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation.

Publications

An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation.

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