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Automated neonatal EEG background analysis for prediction of neurological outcome of premature infants.

Around 10 percent of all human births is premature, which means about 15 million babies are born before 37 completed weeks of gestation. About one third of the admissions to the Neonatal Intensive Care Unit (NICU) consists of this patient group. Due to complications, 1 million babies die from premature delivery, and it is therefore the most important cause of neonatal death. In general, premature and immature babies have a high risk for neurological abnormalities by maturation in extra-uterine life. Even though improved health care has increased the survival changes of these neonates, they are sensitive to brain damage and consequently, neurocognitive disabilities.

Nowadays, critical information about the brain development can be extracted from the electroencephalography (EEG). Clinical experts visually assess evolving EEG characteristics over both short and long periods to evaluate maturation of patients at risk and, if necessary, to start neuroprotective treatment. However, (semi-) automated monitoring of objective and quantitative EEG variables and its validated use is nearly non-existent in neonates, whereas high need exists in the NICUs. To bridge this gap, the aim of this PhD research was to develop supporting software for the automatic analysis of preterm EEG patterns.

The first part of this work investigates the ability to quantify the maturational change in EEG discontinuity. Early cortical brain activity alternates between two activity modes: periods of relative quiescence (interburst intervals or IBIs) are interrupted by spontaneous activity transients (bursts). This EEG pattern will evolve into a more continuous pattern as a biomarker of maturation. An accurate burst detection algorithm is developed using the multichannel line length (LL) information. Based on the LL characteristics, the suppression curve (SC) is derived representing the ‘level of discontinuity’. Both SC properties and IBI lengths show statistically significant correlation with postmenstrual age. Moreover, this developmental shift from intermittent to continuous activity can be captured in a line length histogram. A subset of relevant histogram features is derived and combined into a data-driven EEG index, which holds promise to facilitate automated EEG assessment.

The second part of this work explored the functional brain connectivity in both healthy patients and patients with developmental neurological disorders. Evaluation of interhemispheric symmetry and synchrony still rely on visual, qualitative EEG assessment without clearly quantifiable definitions. Symmetry characteristics were investigated by the channel symmetry index, and served as input for one-class Support Vector Machine classifiers to distinguish pathological from physiological asymmetry. Interhemispheric synchrony (IHS) was estimated using a quantitative measure, the activation synchrony index (ASI). At term age, it showed to be an objective feature of normal neonatal brain function by significant correlation to clinical, visual classification of normal vs. abnormal. Moreover, a robust and statistically significant increase in ASI is observed with early development of synchrony in cortical activations. Hence, ASI-based metrics provide diagnostic value, even at individual level, which strongly supports its use as a functional biomarker.


In conclusion, the EEG patterns can be assessed over longer time intervals and patients at risk can be identified by automated means. The knowledge and expertise of medical experts is aggregated in the implemented algorithms, which adapt automatically at an individual patient level. A feature set of EEG indexes is reported and promising for implementation of preterm developmental growth charts. This feature set provides a unique set of classifiers to detect deviations from normal brain maturation. In this way, automated analysis would add significantly to the normal manual assessment and could therefore become of high value for devising a markedly improved neonatal assessment capability at the NICU. Furthermore, our work opens up possibilities for a more objective and reliable quantification of therapeutic interventions, paving the way forward for more precise administration of medications.

Date:4 Jul 2011 →  31 Dec 2015
Keywords:Classificatie, Prematuur, Electroencephalogram
Disciplines:Modelling, Biological system engineering, Signal processing, Neurosciences, Biological and physiological psychology, Cognitive science and intelligent systems, Developmental psychology and ageing, Biomaterials engineering, Biomechanical engineering, Medical biotechnology, Other (bio)medical engineering, Multimedia processing
Project type:PhD project