Review of sleep-EEG in preterm and term neonates
Introduction
Intensive monitoring of the vulnerable preterm and critically ill term neonate, has been increasingly complemented with bed-side neuromonitoring to achieve optimal insight into neurological well-being [1], [2], [3]. Serial electro-encephalography (EEG) monitoring over time can document actual normal or altered brain function and provides insight into the progress of brain maturation during this period of intensive neonatal care [1], [4], [5], with the ultimate goal to improve therapeutic interventions and long-term neurodevelopmental outcome.
Neonates spend most of their time resting in the sleeping state. Previous research has highlighted the important role of neonatal sleep as a state that involves endogenous driven brain activity, crucial for neuronal survival and guidance of brain networks [6], [7], [8], [9] and relate the impact of sleep on cognitive, psychomotor and behavioural development in both animal [10], [11] as well as human studies [12], [13], [14], [15], [16]. Moreover, sleep ontogenesis is a specific, pre-programmed process of the maturing brain that manifests itself within a certain time window that begins in utero with rapid and major changes during the neonatal periods and infancy, and more subtle changes throughout childhood [11], [17]. It's a complex and highly regulated neurologic function requiring the integration of different brain networks, influenced by the interplay between genetic endowment and environmental inputs, which allows for manifestations of neuroplasticity [6], [8].
Qualitative assessment of neonatal sleep is therefore an essential and valuable measure of functional brain integrity [11], [18], and recognition of sleep-wake states can be useful in the day-to-day monitoring, for assessing optimal periods for feeding and neonatal care, to support and optimise sleep in the NICU [19], as well as give insight in the sleep architecture of infants with sleep related problems (e.g. increased risk of sudden infant death syndrome, sleep apnoea).
This review discusses sleep in the preterm and term neonate and its relationship to brain development, measured with video-EEG polysomnography. We preface this with a short overview of monitoring tools and focus on the maturation of electrographic patterns. To summarize, we assess what is currently known regarding qualitative and quantitative sleep-EEG assessment in the neonatal period.
Section snippets
Monitoring techniques
Conventional video-EEG-polysomnography studies are using a combination of behavioural and EEG characteristics for visual sleep-wake scoring to differentiate recognizable sleep EEG patterns in the preterm and full-term neonate, since neither of these characteristics alone are considered as gold standard [17], [20], [21]. To yield high quality EEG-polysomnography recordings and document specific temporal and regional electrographic changes in brain activity, a reduced 10–20 position with 9
Sleep EEG patterns according to age (Figs. 1–8)
The development of different behavioural states in humans is species-specific and can be divided in quiet sleep (QS or non-REM sleep (NREM)), active sleep (AS or Rapid-Eye Movement (REM)), and wakefulness. These behavioural states are usually defined by a combination of behavioural, cardiorespiratory and EEG state specific criteria which emerge coherently over time, concurrent with the process of rapid brain maturation during foetal and early life [39].
Ultrasound observations have documented a
Sleep state and sleep wake cycling across early brain development
A rough, rudimentary sleep wake cycle, based on alternating periods with and without eye movements related to EEG discontinuity, have been described in preterm infants of 24–30 weeks of GA (Table 1) [40], [41]. In our own cohort with PMA between 27 and 31 weeks, we found a slightly shorter cycle duration between two successive QS periods based on quantitative analysis (for detailed description of this study group see [37]). This is comparable to results of visual EEG-assessment of Curzi
Automated sleep-EEG analysis
Automated detection of sleep periods and quantification of sleep may document alterations in cortical function during extra-uterine brain development and accelerate the more subjective visual assessment.
Detection of sleep is, however, challenging during this period of rapid brain maturation, because of the biological and technical variability in EEG background patterns. Moreover, multiple sleep-EEG studies indicate that newborn state recognition is more comprehensively assessed when integrating
Future directions
Studies of qualitative and quantitative assessment of preterm sleep have not been widely reported and improvements in neonatal medicine (Kangaroo Care, Newborn Individualized Developmental Care and Assessment Program (NIDCAP), non-invasive ventilation [86]) over the last decade have changed the sleeping conditions of those vulnerable preterm infants and ask for a more recent and updated assessment of preterm and neonatal sleep to complement those data. Further attempts to fully automate EEG
Conclusion
Sequential EEG-sleep analysis during the neonatal period provides crucial insights into functional brain integrity and documents deviations of the biologically pre-programmed process of sleep ontogenesis. Visual assessment of neonatal sleep-EEG, with integration of both cerebral and non-cerebral measures to better define neonatal state, is considered the gold standard, however future studies on inter-rater agreement are definitely needed to improve its validity.
Developing qualitative and
Conflict of interest
None
Funding
This research was funded by the Wellcome Trust Centre [grant number 098461/Z/12/Z] (Sleep, Circadian Rhythms & Neuroscience Institute), the RCUK Digital Economy Programme [grant number EP/G036861/1] (Oxford Centre for Doctoral Training in Healthcare Innovation), IWT [grant number TBM 110697-NeoGuard], Bijzonder Onderzoeksfonds KU Leuven (BOF): The effect of perinatal stress on the later outcome in preterm babies [grant number C24/15/036], iMinds Medical Information Technologies (SBO-2016),
Acknowledgments
The authors would like to thank the parents and infants involved in this study and the staff at the UZ Leuven NICU.
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