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- Sandra Horsch (Germany)
- Geraldine B. Boylan (Ireland)
NEONATAL SLEEP AND EARLY BRAIN DEVELOPMENT
- Jeroen Dudink (Netherlands)
ACTIVE SLEEP AS AN EARLY PREDICTOR OF STRUCTURAL BRAIN DEVELOPMENT IN EXTREMELY AND VERY PRETERM INFANTS
- Xiaowan Wang (Netherlands)
Abstract
Background and Aims
Extremely-to-very preterm (EVP) birth (<30 weeks of gestation age, GA) disrupts typical trajectories of brain development. Assumedly, sleep is essential for the formation and development of brain tissues. Therefore, this study aimed to determine the potential of preterm sleep-wake characteristics to serve as early predictors for structural brain maturation at term-equivalent age (TEA) in EVP infants.
Methods
A total of 66 EVP infants who received vital sign monitoring over five to seven consecutive days between 29–32 weeks’ postmenstrual age (PMA) and underwent T2-weighted magnetic resonance imaging at TEA were enrolled in this study. The sleep data were obtained using an automated sleep staging algorithm based on vital signs. Four sleep-wake parameters were estimated: total sleep time (TST), active sleep (AS) and quiet sleep (QS) percentage of TST, and percentage of awake time. To quantify structural brain development, we performed volumetric tissue segmentation on the T2-weighted images, extracting five tissue classes of clinical interest: white matter (WM), cortical gray matter, ventricles, cerebellum and brain stem. The associations between the sleep-wake parameters and brain volumes were examined using multiple linear regression analyses, with adjustment for potential confounding factors: GA at birth, PMA at scanning, and sex.
Results
The regression analyses results showed that a higher AS percentage of TST related significantly with increased total relative WM volume (B = 0.008, CI95% [0.003, 0.012], P < 0.001; Partial R = 0.41) (See Figure).
Conclusions
Our findings demonstrate for the first time the predictive value of AS percentage during preterm period for WM development at TEA in EVP infants.
SLEEP STATE TREND: AN AUTOMATED MEASURE FOR AEEG MONITORS IN THE NEWBORN
- Saeed Montazeri Moghadam (Finland)
Abstract
Background and Aims
Monitoring of fluctuations in sleep states (a.k.a. sleep-wake cycles) is an essential component in the newborn aEEG monitoring, however it has been challenging to recognize them reliably either clinically or from the aEEG trend. Here, we aimed to develop a Sleep State Trend (SST), a transparent and intuitive visualization of sleep states, using a deep learning -based classification of the raw (a)EEG recordings.
Methods
We designed and trained a convolutional neural network (CNN) to detect quiet sleep states from single EEG signals. The algorithm was trained with an aEEG recordings from 30 near-term neonates (total 943.7 hours) and validated using clinical gold standard (N=30), a dataset with polysomnographic recordings.
Results
Accuracy of quiet sleep detection was ~90%, when all bipolar signals were used. Single channel accuracy was 85-86%, and the external validation dataset showed a good generalization (overall accuracy of 81%) despite different EEG derivations. The classifier outputs could be visualized with SST, a continuous trend that shows the likeliest sleep state as well as the confidence in classifications for a bedside quality assessment (Figure 1).
Conclusions
A reliable detection of quiet sleep is possible from single (a)EEG channels, and the result can be readily visualized as an intuitive and transparent SST output in the bedside aEEG monitors. The results hold promise for substantially improving the sleep-oriented care in the neonatal intensive care unit.
SLEEP STATE ORGANISATION OF MODERATE TO LATE PRETERM INFANTS IN THE NEONATAL UNIT
- Mary Anne Ryan (Ireland)
Abstract
Background and Aims
Sleep is a prerequisite for normal neural network development, synaptogenesis and synaptic plasticity, particularly during the accelerated period of brain development that occurs in the last trimester. Sleep architecture reflects brain maturation. This prospective observational study describes the nocturnal sleep architecture of healthy moderate to late preterm (MLP) infants in the neonatal unit at 36 weeks postmenstrual age (PMA)
Methods
Healthy MLP infants (n=98) had overnight continuous video electroencephalography (vEEG) for a minimum 12 hours at 36 weeks PMA.. All sleep states were identified and annotated based on behavioural observation and visual analysis of EEG also incorporating vital recorded including ECG and respirations. The total sleep time (TST), duration of active sleep (AS), quiet sleep (QS) and indeterminate sleep (IS) periods were calculated.
Results
The TST in the 12-hour period was median (IQR) 7.09 (IQR 6.61-7.76) hrs less than expected with 4.58 (3.69-5.09) hours in AS, 2.02 (1.76-2.36) hours in QS and 0.65 (0.48-0.89) hours in IS. The total duration of AS was significantly lower in infants born at lower GA (p= 0.007) whilst the duration of individual QS periods was significantly higher (p=0.001). Moderate to late preterm infants who were exclusively fed orally at 36 weeks had a shorter total sleep time and less AS compared to infants that were fed via nasogastric tube.
Conclusions
Overnight continuous video EEG at 36 weeks PMA showed sleep state architecture is dependent on birth GA. Infants with a lower birth GA have less AS and more QS which may have implications for later neurodevelopment.