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.

Photonic integrated circuits for the generation of coherent optical signals

The demand for optical bandwidth continues to increase year on year and is being driven primarily by entertainment services and video streaming to the home. Current photonic systems are coping with this demand by increasing data rates through faster modulation techniques, spectrally efficient transmission systems and by increasing the number of modulated optical channels per fibre strand. Such photonic systems are large and power hungry due to the high number of discrete components required in their operation. Photonic integration offers excellent potential for combining otherwise discrete system components together on a single device to provide robust, power efficient and cost effective solutions. In particular, the design of optical modulators has been an area of immense interest in recent times. Not only has research been aimed at developing modulators with faster data rates, but there has also a push towards making modulators as compact as possible. Mach-Zehnder modulators (MZM) have proven to be highly successful in many optical communication applications. However, due to the relatively weak electro-optic effect on which they are based, they remain large with typical device lengths of 4 to 7 mm while requiring a travelling wave structure for high-speed operation. Nested MZMs have been extensively used in the generation of advanced modulation formats, where multi-symbol transmission can be used to increase data rates at a given modulation frequency. Such nested structures have high losses and require both complex fabrication and packaging. In recent times, it has been shown that Electro-absorption modulators (EAMs) can be used in a specific arrangement to generate Quadrature Phase Shift Keying (QPSK) modulation. EAM based QPSK modulators have increased potential for integration and can be made significantly more compact than MZM based modulators. Such modulator designs suffer from losses in excess of 40 dB, which limits their use in practical applications. The work in this thesis has focused on how these losses can be reduced by using photonic integration. In particular, the integration of multiple lasers with the modulator structure was considered as an excellent means of reducing fibre coupling losses while maximising the optical power on chip. A significant difficultly when using multiple integrated lasers in such an arrangement was to ensure coherence between the integrated lasers. The work investigated in this thesis demonstrates for the first time how optical injection locking between discrete lasers on a single photonic integrated circuit (PIC) can be used in the generation of coherent optical signals. This was done by first considering the monolithic integration of lasers and optical couplers to form an on chip optical power splitter, before then examining the behaviour of a mutually coupled system of integrated lasers. By operating the system in a highly asymmetric coupling regime, a stable phase locking region was found between the integrated lasers. It was then shown that in this stable phase locked region the optical outputs of each laser were coherent with each other and phase locked to a common master laser.

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.