Advanced Solutions for Neonatal Sleep Analysis and the Effects of Maturation

Worldwide approximately 11% of the babies are born before 37 weeks of gestation. The survival rates of these prematurely born infants have steadily increased during the last decades as a result of the technical and medical progress in the neonatal intensive care units (NICUs). The focus of the NICUs has therefore gradually evolved
from increasing life chances to improving quality of life. In this respect, promoting and supporting optimal brain development is crucial.
Because these neonates are born during a period of rapid growth and development of the brain, they are susceptible to brain damage and therefore vulnerable to adverse neurodevelopmental outcome. In order to identify patients at risk of long-term disabilities, close monitoring of the neurological function during the first critical weeks is a primary concern in the current NICUs. 
Electroencephalography (EEG) is a valuable tool for continuous noninvasive brain monitoring at the bedside. The brain waves and patterns in the neonatal EEG provide interesting information about the newborn brain function. However, visual interpretation is a time-consuming and tedious task requiring expert knowledge. This indicates a need for automated analysis of the neonatal EEG characteristics. The work presented in this thesis aims at contributing to this.

The first part of this thesis focuses on the development of algorithms to automatically classify sleep stages in preterm babies. In total three different strategies are proposed. 
In the first method, the problem is traditionally approached and a new set of EEG complexity features is combined with a classification algorithm. This analysis demonstrates that the complexity of the EEG signal is fundamentally different dependent on the vigilance state of the infant. Building on this finding, a novel tensor-based approach 
that detects quiet sleep in an unsupervised manner is presented.
Finally, a deep convolutional neural network to classify neonatal sleep stages is implemented. This end-to-end model optimizes the feature extraction and classification model simultaneously, avoiding the challenging task of feature engineering.   

The second part concentrates on the quantification of functional brain maturation in preterm infants. 
We establish that the complexity of the EEG time series is significantly positively correlated with the postmenstrual age of the neonate. Moreover, these promising biomarkers of brain maturity are used to develop a brain-age model. This model can accurately estimate the infant's age and thereby assess the functional brain maturation. 
In addition, the relationship between the early functional and structural brain development is investigated based on two complementary neuromonitoring modalities, EEG and MRI. Regression models show that the brain activity during the first postnatal days is related to the size and growth of the cerebellum in the subsequent weeks.
At last, the influence of the thyroid function on the developing brain is examined in extremely premature infants. No significant association was observed between the change in free thyroxine concentrations during the first week of life and maturational features extracted from the EEG at term equivalent age. To shed more light on the precise relationship between thyroid function and brain maturation, prospective studies with a more homogeneous dataset are needed in the future.