Seizure burden and neurodevelopmental outcome in neonates with hypoxic-ischemic encephalopathy.

Aim: To examine the relationship between electrographic seizures and long-term outcome in neonates with hypoxic-ischemic encephalopathy (HIE). Method: Full-term neonates with HIE born in Cork University Maternity Hospital from 2003 to 2006 (pre-hypothermia era) and 2009 to 2012 (hypothermia era) were included in this observational study. All had early continuous electroencephalography monitoring. All electrographic seizures were annotated. The total seizure burden and hourly seizure burden were calculated. Outcome (normal/abnormal) was assessed at 24 to 48 months in surviving neonates using either the Bayley Scales of Infant and Toddler Development, Third Edition or the Griffiths Mental Development Scales; a diagnosis of cerebral palsy or epilepsy was also considered an abnormal outcome. Results: Continuous electroencephalography was recorded for a median of 57.1 hours (interquartile range 33.5-80.5h) in 47 neonates (31 males, 16 females); 29 out of 47 (62%) had electrographic seizures and 25 out of 47 (53%) had an abnormal outcome. The presence of seizures per se was not associated with abnormal outcome (p=0.126); however, the odds of an abnormal outcome increased over ninefold (odds ratio [OR] 9.56; 95% confidence interval [95% CI] 2.43-37.67) if a neonate had a total seizure burden of more than 40 minutes (p=0.001), and eightfold (OR: 8.00; 95% CI: 2.06-31.07) if a neonate had a maximum hourly seizure burden of more than 13 minutes per hour (p=0.003). Controlling for electrographic HIE grade or treatment with hypothermia did not change the direction of the relationship between seizure burden and outcome. Interpretation: In HIE, a high electrographic seizure burden is significantly associated with abnormal outcome, independent of HIE severity or treatment with hypothermia.

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.

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.

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.

Impurity profiling and synthesis of precursors to the hallucinogenic amphetamine DOB

DOB (4‐bromo‐2,5‐dimethoxyamphetamine) is a newly emerging hallucinogenic amphetamine that sparked serious health warnings in Ireland, following its first seizure back in 2003. Known more commonly as “snowball”, this drug is highly potent and may be used as a substitute to ecstasy (MDMA) and lysergic acid diethylamide (LSD). To date, the work carried out on the impurity profiling of DOB is limited in comparison to amphetamine, methamphetamine and MDMA. In this work, the impurity profile of 4‐bromo‐2,5‐dimethoxyphenyl‐2‐propanone (4‐Br‐2,5‐P2P) is explored. This ketone is a direct precursor to DOB. Its more versatile non‐bromo analogue, 2,5‐ dimethoxyphenyl‐2‐propanone (2,5‐P2P) is also examined, as in addition to DOB, it may be used in the synthesis of a range of several other hallucinogenic amphetamines. A number of different routes to both 2,5‐P2P and 4‐Br‐2,5‐P2P were investigated. For each of these routes, the impurities produced were carefully isolated. Following isolation, the impurities were fully characterised (by 1H‐NMR/13C‐NMR spectroscopy, IR, MS), in order to aid structure elucidation. Compounds not easily resolved by flash column chromatography were analysed by LC‐MS and/or independently synthesised for the purpose of attaining reference standards. Adaptation of the well‐known ‘phenylacetic acid route’ to synthesis of both 2,5‐P2P and 4‐Br‐2,5‐P2P, was found to provide low yields of the expected ketone products. Four impurities were isolated during the preparation of both ketones. The yield of one of these impurities (possessing a dibenzylketone core), was greatly influenced by the amount of acetic anhydride reagent used during the reaction. Having carried out the reaction with several different equivalents of acetic anhydride, it was found that formation of the ‘dibenzylketone’ could not be eliminated. This may increase its likelihood of being detected in the final drug product. The ‘Darzens route’, having very recently emerged as a synthetic route to amphetamine and MDMA precursors, was discovered to be a viable route for manufacture of 2,5‐P2P and 4‐Br‐2,5‐P2P. Despite execution of the reaction being more tedious, the route provides superior yields (≈50–60%) to those achieved using the ‘phenylacetic acid route’ (≈35–38%). Incorporation of a bromine atom (at the aromatic 4‐position) is required at some stage during synthesis of DOB. The bromination of many intermediates/starting materials was therefore also examined in detail. Bromination of the acid starting material 2,5‐dimethoxyphenylacetic acid (2,5‐PAA) was found to be clean and high yielding. This was in stark contrast to the bromination of the benzaldehyde starting material, the ketone precursor 2,5‐P2P and the dibenzylketone‐based impurity. Numerous brominated products were isolated from each of these reactions, many of which were novel compounds, and previously unreported as impurities in the literature. The unpredictable/nondescript nature of these brominations is likely to have a significant impact on the impurity profile of illicitly produced DOB.

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.

An in-depth characterisation of neonatal seizures by early continuous video-EEG analysis

Introduction Seizures are harmful to the neonatal brain; this compels many clinicians and researchers to persevere further in optimizing every aspects of managing neonatal seizures. Aims To delineate the seizure profile between non-cooled versus cooled neonates with hypoxic-ischaemic encephalopathy (HIE), in neonates with stroke, the response of seizure burden to phenobarbitone and to quantify the degree of electroclinical dissociation (ECD) of seizures. Methods The multichannel video-EEG was used in this research study as the gold standard to detect seizures, allowing accurate quantification of seizure burden to be ascertained in term neonates. The entire EEG recording for each neonate was independently reviewed by at least 1 experienced neurophysiologist. Data were expressed in medians and interquartile ranges. Linear mixed models results were presented as mean (95% confidence interval); p values <0.05 were deemed as significant. Results Seizure burden in cooled neonates was lower than in non-cooled neonates [60(39-224) vs 203(141-406) minutes; p=0.027]. Seizure burden was reduced in cooled neonates with moderate HIE [49(26-89) vs 162(97-262) minutes; p=0.020] when compared with severe HIE. In neonates with stroke, the background pattern showed suppression over the infarcted side and seizures demonstrated a characteristic pattern. Compared with 10 mg/kg, phenobarbitone doses at 20 mg/kg reduced seizure burden (p=0.004). Seizure burden was reduced within 1 hour of phenobarbitone administration [mean (95% confidence interval): -14(-20 to -8) minutes/hour; p<0.001], but seizures returned to pre-treatment levels within 4 hours (p=0.064). The ECD index in cooled, non-cooled neonates with HIE, stroke and in neonates with other diagnoses were 88%, 94%, 64% and 75% respectively. Conclusions Further research exploring the treatment effects on seizure burden in the neonatal brain is required. A change to our current treatment strategy is warranted as we continue to strive for more effective seizure control, anchored with use of the multichannel EEG as the surveillance tool.

Treatment trials for neonatal seizures: the effect of design on sample size

Neonatal seizures are common in the neonatal intensive care unit. Clinicians treat these seizures with several anti-epileptic drugs (AEDs) to reduce seizures in a neonate. Current AEDs exhibit sub-optimal efficacy and several randomized control trials (RCT) of novel AEDs are planned. The aim of this study was to measure the influence of trial design on the required sample size of a RCT. We used seizure time courses from 41 term neonates with hypoxic ischaemic encephalopathy to build seizure treatment trial simulations. We used five outcome measures, three AED protocols, eight treatment delays from seizure onset (Td) and four levels of trial AED efficacy to simulate different RCTs. We performed power calculations for each RCT design and analysed the resultant sample size. We also assessed the rate of false positives, or placebo effect, in typical uncontrolled studies. We found that the false positive rate ranged from 5 to 85% of patients depending on RCT design. For controlled trials, the choice of outcome measure had the largest effect on sample size with median differences of 30.7 fold (IQR: 13.7–40.0) across a range of AED protocols, Td and trial AED efficacy (p<0.001). RCTs that compared the trial AED with positive controls required sample sizes with a median fold increase of 3.2 (IQR: 1.9–11.9; p<0.001). Delays in AED administration from seizure onset also increased the required sample size 2.1 fold (IQR: 1.7–2.9; p<0.001). Subgroup analysis showed that RCTs in neonates treated with hypothermia required a median fold increase in sample size of 2.6 (IQR: 2.4–3.0) compared to trials in normothermic neonates (p<0.001). These results show that RCT design has a profound influence on the required sample size. Trials that use a control group, appropriate outcome measure, and control for differences in Td between groups in analysis will be valid and minimise sample size.