Dynamic classifiers for neonatal brain monitoring

Brain injury due to lack of oxygen or impaired blood flow around the time of birth, may cause long term neurological dysfunction or death in severe cases. The treatments need to be initiated as soon as possible and tailored according to the nature of the injury to achieve best outcomes. The Electroencephalogram (EEG) currently provides the best insight into neurological activities. However, its interpretation presents formidable challenge for the neurophsiologists. Moreover, such expertise is not widely available particularly around the clock in a typical busy Neonatal Intensive Care Unit (NICU). Therefore, an automated computerized system for detecting and grading the severity of brain injuries could be of great help for medical staff to diagnose and then initiate on-time treatments. In this study, automated systems for detection of neonatal seizures and grading the severity of Hypoxic-Ischemic Encephalopathy (HIE) using EEG and Heart Rate (HR) signals are presented. It is well known that there is a lot of contextual and temporal information present in the EEG and HR signals if examined at longer time scale. The systems developed in the past, exploited this information either at very early stage of the system without any intelligent block or at very later stage where presence of such information is much reduced. This work has particularly focused on the development of a system that can incorporate the contextual information at the middle (classifier) level. This is achieved by using dynamic classifiers that are able to process the sequences of feature vectors rather than only one feature vector at a time.

Statistical methods for polyhedral shape classification with incomplete data - application to cryo-electron tomographic images

A certain type of bacterial inclusion, known as a bacterial microcompartment, was recently identified and imaged through cryo-electron tomography. A reconstructed 3D object from single-axis limited angle tilt-series cryo-electron tomography contains missing regions and this problem is known as the missing wedge problem. Due to missing regions on the reconstructed images, analyzing their 3D structures is a challenging problem. The existing methods overcome this problem by aligning and averaging several similar shaped objects. These schemes work well if the objects are symmetric and several objects with almost similar shapes and sizes are available. Since the bacterial inclusions studied here are not symmetric, are deformed, and show a wide range of shapes and sizes, the existing approaches are not appropriate. This research develops new statistical methods for analyzing geometric properties, such as volume, symmetry, aspect ratio, polyhedral structures etc., of these bacterial inclusions in presence of missing data. These methods work with deformed and non-symmetric varied shaped objects and do not necessitate multiple objects for handling the missing wedge problem. The developed methods and contributions include: (a) an improved method for manual image segmentation, (b) a new approach to 'complete' the segmented and reconstructed incomplete 3D images, (c) a polyhedral structural distance model to predict the polyhedral shapes of these microstructures, (d) a new shape descriptor for polyhedral shapes, named as polyhedron profile statistic, and (e) the Bayes classifier, linear discriminant analysis and support vector machine based classifiers for supervised incomplete polyhedral shape classification. Finally, the predicted 3D shapes for these bacterial microstructures belong to the Johnson solids family, and these shapes along with their other geometric properties are important for better understanding of their chemical and biological characteristics.

MicroRNA expression and function in neonatal hypoxic ischaemic encephalopathy

Hypoxic ischaemic encephalopathy (HIE) is a devastating neonatal condition which affects 2-3 per 1000 infants annually. The current gold standard of treatment - induced hypothermia, has the ability to reduce neonatal mortality and improve neonatal morbidity. However, to be effective it needs to be initiated within the therapeutic window which exists following initial insult until approximately 6 hours after birth. Current methods of assessment which are relied upon to identify infants with HIE are subjective and unreliable. To overcome this issue, an early and reliable biomarker of HIE severity must be identified. MicroRNA (miRNA) are a class of small non-coding RNA molecules which have potential as biomarkers of disease state and potential therapeutic targets. These tiny molecules can modulate gene expression by inhibiting translation of messenger RNA (mRNA) and as a result, can regulate protein synthesis. These miRNA are understood to be released into the circulation during cellular stress, where they are highly stable and relatively easy to quantify. Therefore, these miRNAs may be ideal candidates for biomarkers of HIE severity and may aid in directing the clinical management of these infants. By using both transcriptomic and proteomic approaches to analyse the expression of miRNAs and their potential targets in the umbilical cord blood, I have confirmed that infants with perinatal asphyxia and HIE have a significantly different UCB miRNA signature compared to UCB samples from healthy controls. Finally, I have identified and investigated 2 individual miRNAs; both of which show some potential as classifiers of HIE severity and predictors of long term outcome, particularly when coupled with their downstream targets. While this work will need to be validated and expanded in a new and larger cohort of infants, it suggests the potential of miRNA as biomarkers of neonatal pathological conditions such as HIE.