Neonatal EEG classification using atomic decomposition

The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classification of neonatal EEG which can be mainly divided into two parts: (1) classification of neonatal EEG seizure from nonseizure, and (2) classifying neonatal background EEG into several grades based on the severity of the injury using atomic decomposition. Atomic decomposition techniques use redundant time-frequency dictionaries for sparse signal representations or approximations. The first novel contribution of this thesis is the development of a novel time-frequency dictionary coherent with the neonatal EEG seizure states. This dictionary was able to track the time-varying nature of the EEG signal. It was shown that by using atomic decomposition and the proposed novel dictionary, the neonatal EEG transition from nonseizure to seizure states could be detected efficiently. The second novel contribution of this thesis is the development of a neonatal seizure detection algorithm using several time-frequency features from the proposed novel dictionary. It was shown that the time-frequency features obtained from the atoms in the novel dictionary improved the seizure detection accuracy when compared to that obtained from the raw EEG signal. With the assistance of a supervised multiclass SVM classifier and several timefrequency features, several methods to automatically grade EEG were explored. In summary, the novel techniques proposed in this thesis contribute to the application of advanced signal processing techniques for automatic assessment of neonatal EEG recordings.

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.

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.

Reporting on data monitoring committees in neonatal randomised controlled trials is inconsistent

Aim: To evaluate the reported use of Data Monitoring Committees (DMCs), the frequency of interim analysis, pre-specified stopping rules and early trial termination in neonatal randomised controlled trials (RCTs). Methods: We reviewed neonatal RCTs published in four high impact general medical journals, specifically looking at safety issues including documented involvement of a DMC, stated interim analysis, stopping rules and early trial termination. We searched all journal issues over an 11-year period (2003-2013) and recorded predefined parameters on each item for RCTs meeting inclusion criteria. Results: Seventy neonatal trials were identified in four general medical journals: Lancet, New England Journal of Medicine (NEJM), British Medical Journal and Journal of American Medical Association (JAMA). 43 (61.4%) studies reported the presence of a DMC, 36 (51.4%) explicitly mentioned interim analysis; stopping rules were reported in 15 (21.4%) RCTs and 7 (10%) trials were terminated early. The NEJM most frequently reported these parameters compared to the other three journals reviewed. Conclusion: While the majority of neonatal RCTs report on DMC involvement and interim analysis there is still scope for improvement. Clear documentation of safety related issues should be a central component of reporting in neonatal trials involving newborn infants.

Phenobarbital reduces EEG amplitude and propagation of neonatal seizures but does not alter performance of automated seizure detection

Objective: Phenobarbital increases electroclinical uncoupling and our preliminary observations suggest it may also affect electrographic seizure morphology. This may alter the performance of a novel seizure detection algorithm (SDA) developed by our group. The objectives of this study were to compare the morphology of seizures before and after phenobarbital administration in neonates and to determine the effect of any changes on automated seizure detection rates. Methods: The EEGs of 18 term neonates with seizures both pre- and post-phenobarbital (524 seizures) administration were studied. Ten features of seizures were manually quantified and summary measures for each neonate were statistically compared between pre- and post-phenobarbital seizures. SDA seizure detection rates were also compared. Results: Post-phenobarbital seizures showed significantly lower amplitude (p < 0.001) and involved fewer EEG channels at the peak of seizure (p < 0.05). No other features or SDA detection rates showed a statistical difference. Conclusion: These findings show that phenobarbital reduces both the amplitude and propagation of seizures which may help to explain electroclinical uncoupling of seizures. The seizure detection rate of the algorithm was unaffected by these changes. Significance: The results suggest that users should not need to adjust the SDA sensitivity threshold after phenobarbital administration.