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.

Voltage properties of optically injected long wavelength VCSELs: Theory and experiment

Future high speed communications networks will transmit data predominantly over optical fibres. As consumer and enterprise computing will remain the domain of electronics, the electro-optical conversion will get pushed further downstream towards the end user. Consequently, efficient tools are needed for this conversion and due to many potential advantages, including low cost and high output powers, long wavelength Vertical Cavity Surface Emitting Lasers (VCSELs) are a viable option. Drawbacks, such as broader linewidths than competing options, can be mitigated through the use of additional techniques such as Optical Injection Locking (OIL) which can require significant expertise and expensive equipment. This thesis addresses these issues by removing some of the experimental barriers to achieving performance increases via remote OIL. Firstly, numerical simulations of the phase and the photon and carrier numbers of an OIL semiconductor laser allowed the classification of the stable locking phase limits into three distinct groups. The frequency detuning of constant phase values (ø) was considered, in particular ø = 0 where the modulation response parameters were shown to be independent of the linewidth enhancement factor, α. A new method to estimate α and the coupling rate in a single experiment was formulated. Secondly, a novel technique to remotely determine the locked state of a VCSEL based on voltage variations of 2mV−30mV during detuned injection has been developed which can identify oscillatory and locked states. 2D & 3D maps of voltage, optical and electrical spectra illustrate corresponding behaviours. Finally, the use of directly modulated VCSELs as light sources for passive optical networks was investigated by successful transmission of data at 10 Gbit/s over 40km of single mode fibre (SMF) using cost effective electronic dispersion compensation to mitigate errors due to wavelength chirp. A widely tuneable MEMS-VCSEL was established as a good candidate for an externally modulated colourless source after a record error free transmission at 10 Gbit/s over 50km of SMF across a 30nm single mode tuning range. The ability to remotely set the emission wavelength using the novel methods developed in this thesis was demonstrated.

Trophic role of small cetaceans and seals in Irish waters

Understanding the role of marine mammals in specific ecosystems and their interactions with fisheries involves, inter alia, an understanding of their diet and dietary requirements. In this thesis, the foraging ecology of seven marine mammal species that regularly occur in Irish waters was investigated by reconstructing diet using hard parts from digestive tracts and scats. Of the species examined, two (striped and Atlantic white-sided dolphin) can be considered offshore species or species inhabiting neritic waters, while five others usually inhabit more coastal areas (white-beaked dolphin, harbour porpoise, harbour seal and grey seal); the last species studied was the bottlenose dolphin whose population structure is more complex, with coastal and offshore populations. A total of 13,028 prey items from at least 81 different species (62 fish species, 14 cephalopods, four crustaceans, and a tunicate) were identified. 28% of the fish species were identified using bones other than otoliths, highlighting the importance of using all identifiable structures to reconstruct diet. Individually, each species of marine mammal presented a high diversity of prey taxa, but the locally abundant Trisopterus spp. were found to be the most important prey item for all species, indicating that Trisopterus spp. is probably a key species in understanding the role of these predators in Irish waters. In the coastal marine mammals, other Gadiformes species (haddock, pollack, saithe, whiting) also contributed substantially to the diet; in contrast, in pelagic or less coastal marine mammals, prey was largely comprised of planktivorous fish, such as Atlantic mackerel, horse mackerel, blue whiting, and mesopelagic prey. Striped dolphins and Atlantic white-sided dolphins are offshore small cetaceans foraging in neritic waters. Differences between the diet of striped dolphins collected in drift nets targeting tuna and stranded on Irish coasts showed a complex foraging behaviour; the diet information shows that although this dolphin forages mainly in oceanic waters it may occasionally forage on the continental shelf, feeding on available prey. The Atlantic white-sided dolphin diet showed that this species prefers to feed over the continental edge, where planktivorous fish are abundant. Some resource partitioning was found in bottlenose dolphins in Irish waters consistent with previous genetic and stable isotope analysis studies. Bottlenose dolphins in Irish waters appears to be generalist feeders consuming more than 30 prey species, however most of the diet comprised a few locally abundant species, especially gadoid fish including haddock/pollack/saithe group and Trisopterus spp., but the contribution of Atlantic hake, conger eels and the pelagic planktivorous horse mackerel were also important. Stomach content information suggests that three different feeding behaviours might occur in bottlenose dolphin populations in Irish waters; firstly a coastal behaviour, with animals feeding on prey that mainly inhabit areas close to the coast; secondly an offshore behaviour where dolphins feed on offshore species such as squid or mesopelagic fish; and a third more complex behaviour that involves movements over the continental shelf and close to the shelf edge. The other three coastal marine mammal species (harbour porpoise, harbour seal and grey seal) were found to be feeding on similar prey and competition for food resources among these sympatric species might occur. Both species of seals were found to have a high overlap (more than 80%) in their diet composition, but while grey seals feed on large fish (>110mm), harbour seals feed mostly on smaller fish (<110mm), suggesting some spatial segregation in foraging. Harbour porpoises and grey seals are potentially competing for the same food resource but some differences in prey species were found and some habitat partitioning might occur. Direct interaction (by catch) between dolphins and fisheries was detected in all species. Most of the prey found in the stomach contents from both stranded and by catch dolphins were smaller sizes than those targeted by commercial fisheries. In fact, the total annual food consumption of the species studied was found to be very small (225,160 tonnes) in comparison to fishery landings for the same area (~2 million tonnes). However, marine mammal species might be indirectly interacting with fisheries, removing forage fish. Incorporating the dietary information obtained from the four coastal species, an ECOPATH food web model was established for the Irish Sea, based on data from 2004. Five trophic levels were found, with bottlenose dolphins and grey and harbour seals occurring at the highest trophic level. A comparison with a previous model based on 1973 data suggests that while the overall Irish Sea ecosystem appears to be “maturing”, some indices indicate that the 2004 fishery was less efficient and was targeting fish at higher trophic levels than in 1973, which is reflected in the mean trophic level of the catch. Depletion or substantial decrease of some of the Irish Sea fish stocks has resulted in a significant decline in landings in this area. The integration of diet information in mass-balance models to construct ecosystem food-webs will help to understand the trophic role of these apex predators within the ecosystem.