Copper-catalysed asymmetric sulfide oxidation

The focus of this thesis is the preparation of enantiopure sulfoxides by means of copper-catalysed asymmetric sulfoxidation, with particular emphasis on the synthesis of aryl benzyl and aryl alkyl sulfoxides. Chapter 1 contains a review of the methods employed for the asymmetric synthesis of sulfoxides, compounds with many applications in stereoselective synthesis and in some cases with pharmaceutical application. Chapter 1 describes asymmetric oxidation, including metal-catalysed, non metal-catalysed and enzyme-catalysed, in addition to synthetic approaches via nucleophilic substitution of appropriately substituted precursors. Kinetic resolution in oxidation of sulfoxides to the analogous sulfones is also discussed; in certain cases, access to enantioenriched sulfoxides can be achieved via a combination of asymmetric sulfoxidation and complementary kinetic resolution. The design and synthesis of a series of sulfides to enable exploration of the substituent effects of the copper-mediated oxidation was undertaken, and oxidation to the racemic sulfoxides and sulfones to provide reference samples was conducted. Oxidation of the sulfides using copper-Schiff base catalysis was undertaken leading to enantioenriched sulfoxides. The procedure employed is clean, inexpensive, not air-sensitive and utilises aqueous hydrogen peroxide as oxidant. Extensive investigation of the influence of the reaction conditions such as solvent, temperature, copper salt and ligand was undertaken to lead to the optimised conditions. While the direct attachment of one aryl substituent to the sulfide is essential for efficient enantiocontrol, in the case of the second substituent the enantiocontol is dependent on the steric rather than electronic features of the substituent. Significantly, use of naphthyl-substituted sulfides results in excellent enantiocontrol; notably 97% ee, obtained in the oxidation of 2-naphthyl benzyl sulfide, represents the highest enantioselectivity reported to date for a copper-mediated sulfur oxidation. Some insight into the mechanistic features of the copper-mediated sulfur oxidation has been developed based on this work, although further investigation is required to establish the precise nature of the catalytic species responsible for asymmetric sulfur oxidation. Full experimental details, describing the synthesis and structural characterisation, and determination of enantiopurity are included in chapter 3.

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.