Impact of the smoke-free workplace legislation on smoking behaviour, risk perception & stigmatisation

The Republic of Ireland became the first European country to implement nationwide smoke-free workplace legislation. Aims: To determine prevalence of smoking among bar workers and estimate the impact of the smoke-free workplace legislation on their smoking behaviour to that of a comparable general population sample. To approximate the influence of tobacco control measures on risk perception of second-hand smoke (SHS) among the general population. To explore the de-normalisation of smoking behaviour and the potential increased stigmatisation of smokers and their smoking. Methods: Prevalence estimates and behavioural changes were examined among a random sample of bar workers before and 1 year after the smoke-free legislation; comparisons made with a general population sub-sample. Changes in risk knowledge related to SHS exposure were based on general population data. Qualitative interviews were conducted among a purposive sample of smokers and non-smokers four years after the implementation of the legislation. Results: Smoking prevalence was extremely high among bar workers. Smoking prevalence dropped in bar workers and significantly among the general population 1 year post ban while cigarette consumption dropped significantly among bar workers. Disparity in knowledge between smokers and non-smoker of risk associated with SHS exposure reduced. Lack of understanding of the risk of ear infections in children posed by SHS exposure was notable. Evidence for advanced de-normalisation of smoking behaviour and intensification of stigma because of the introduction of the legislation was dependent on many factors, quality of smoking facilities played a key role. Conclusions: Ireland’s smoke-free legislation was associated with a drop in prevalence and cigarette consumption. Disparity in knowledge between smokers and non-smokers of the risk posed by SHS exposure reduced however the risk of ear infections in children needs to be effectively disseminated. The proliferation of ‘good’ smoking areas may diminish the potential to reduce smoking behaviour and de-normalise smoking.

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.