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 is 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 re- duced, 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 diffi- cult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, represents an attempt to move automated neurological event de- tection systems forward, by identifying some of the major obstacles in deploying EEG systems in ambulatory and clinical environments so that the EEG technologies can move out of 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, sig- nal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. Subsequently, additional physiological signals, in the form of gyroscopes, are used to de- tect head-movements and in doing so, bring additional information to the head-movement artefact detection task. 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 detection classifiers in order to significantly reduce the number of false de- tections in the epileptiform activity detection system. 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, modest advances are made detecting and removing EEG artefacts and in doing so, the performance of the underlying diagnostic technologies are improved, bringing us one step closer to their deployment in the real- world, clinical domain.