Development of sleep-wake cycles in premature infants and its impact on further outcome

Due to the development of neonatal intensive care the number of surviving premature infants increased significantly. Due to common complications and the non-physiological environment in the intensive care unit (NICU) the immature brain undergoes a fair amount of external stress stimuli, which has a great impact on later cognitive development. Increasingly data show, that a delayed emergence of sleep-wake-cycling in newborns can be the first sign of brain injury and might be associated with later adverse neurodevelopmental outcome. Studies have shown that clearly defined sleep states can be identified from 31-32 weeks of gestation onwards. But a few studies show, that also extremely premature infants (e.g. infants born at 24-25 weeks of gestation) already show cyclical variations of the background pattern within amplitude-integrated EEG (aEEG; a time-compressed, simplified EEG) and conventional EEG. This might resemble early sleep-wake-states and their presence correlates to the integrity of the central nervous system, although no clearly defined "sleep states" according to the classical definition can be identified. Complex EEG analysis needs the use of automated methods to exclude personal bias and to ensure gestational age specific data analysis. The newly developed NLEO algorithm was specially designed for EEG analysis of premature infants. Conventional EEG within this study will be analyzed visually and with the automated algorithm. In our research project we will study the emergence of Sleep-wake-cycling in extremely premature infants and its impact on their neurodevelopmental outcome prospectively. Premature infants below 29 weeks of gestation will be included and measured for a 3 hour period every second week with conventional and aEEG and Videomonitoring. Neuromotor and cognitive developmental outcome will be followed until the age of 5 years with standardized tests. The different sleep and wake states will be derived from analysis of the conventional Video-EEG, aEEG and polysomnographic measurements. Visual analysis will include assessment of amplitudes and frequencies as well as the latencies and durations of EEG-Bursts and Interburst intervals. The automated NLEO-algorithm will be firstly used for comparison with above described visual analysis and secondly to find regions of interest involved in the organization of these early sleep states. The aim of this study is first to understand and analyze in detail the emergence of sleep-wake cycling including its disturbances in premature infants born below 29 weeks of gestation and to compare automated NLEO algorithm to conventional visual analysis methods. Secondly to correlate neurodevelopmental outcome to the emergence of sleep-wake-cycling. This will allow us to study early development of EEG features in a cohort with no disease or drug bias, thus relating NICU experience to later cognitive outcomes. We will be able to relate complications such as intraventricular haemorrhage to EEG changes in progress and their impact on neurodevelopment. In an international and interdisciplinary cooperation between physicians, electrophysiologist, mathematitians and engineers we will be able to deduct unique conclusions, in this fragile patient population, providing important prognostic information for patient, parents and physicians.

Due to development of neonatal intensive care the number of surviving premature infants increased significantly over the last years, but brain injury due to prematurity, infections, hypoxia-ischemia, intraventricular hemorrhage and periventricular leucomalacia and their influence on neurodevelopmental outcome of these infants, remain the biggest challenge in neonatal care. Early identification of infants suffering from brain injury will improve their care and also neurodevelopmental outcome on the long run. Brain activity and maturation displayed by electroencephalography (EEG) is one of the most sensitive markers of brain injury in this patient cohort. Especially delayed emergence of sleep-wake-cycling in preterms can be the first sign of brain injury. Clearly defined sleep-states can be identified due to literature known so far - from 32 weeks of gestation onwards. Increasing numbers of studies show, that also extremely premature infants (<26 weeks of gestation) may already show early sleep-states, but mainly only within simplified EEG-method as for example amplitude- integrated EEG (aEEG). aEEG displays a simplified, only one- or two- channel time- compressed EEG which is increasingly used within neonatal intensive care units . In our project we were aiming to study the emergence of sleep-wake-cycling in more detail and with the more detailed method of full EEG in addition to aEEG - including respiratory and heart rate in extremely premature infants. Premature infants <29 weeks of gestation were included and measured for a 3 hour period every second week with conventional and amplitude-integrated EEG. Extensive conventional visual analysis of the EEG including analysis of duration of bursts (=active periods within the EEG), interburst-intervals (IBIs) and regional amplitude- distribution was done, showing linear shortening of interburst-intervals and lengthening of burst durations and decrease of amplitude heights when gestational is increasing, with shorter IBIs in active sleep compared to quiet sleep episodes. Also new analytical methods (including newly developed automatic EEG algorithms for analysis) were used and compared to conventional visual analysis, showing good correlation with visual analysis. In an international and interdisciplinary cooperation between physicians, electrophysiologist and mathematicians we were able to provide new descriptive reference data for emergence of sleep-wake-cycling in this fragile patient population, and correlating these information with the neurodevelopmental outcome data we will be able to provide important prognostic information for patients, parents and physicians. This clarifies maturation and development of sleep-wake-cycling in preterm infants, will help to identify patients at risk for brain injury or infants suffering already from brain injury, and will help to improve targeted neuroprotective care in these infants.