Artefact detection and removal algorithms for EEG diagnostic systems

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dc.contributor.advisor Marnane, William P. en
dc.contributor.advisor Lightbody, Gordon en
dc.contributor.advisor Boylan, Geraldine B. en O'Regan, Simon H. 2014-02-17T16:46:46Z 2014-02-17T16:46:46Z 2013 2013
dc.identifier.citation O'Regan, S. H. 2013. Artefact detection and removal algorithms for EEG diagnostic systems. PhD Thesis, University College Cork. en
dc.identifier.endpage 216
dc.description.abstract 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. en
dc.description.sponsorship Science Foundation Ireland (SFI/07/SRC/I1169); Science Foundation Ireland (SFI/10/IN.1/B3036) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher University College Cork en
dc.rights © 2013, Simon H. O'Regan en
dc.rights.uri en
dc.subject Artefact en
dc.subject Artifact en
dc.subject Artefact detection en
dc.subject Electroencephalogram en
dc.subject Epilepsy en
dc.subject Algorithms en
dc.subject BCI en
dc.subject Artefact removal en
dc.subject Artifact removal en
dc.subject Artefact rejection en
dc.subject Respiratory signals en
dc.subject Multimodal en
dc.subject Physiological signals en
dc.subject Gyroscopes en
dc.subject Movement en
dc.subject Head-movement detection en
dc.subject Seizure detection en
dc.subject Head-movement artefact en
dc.subject Epilepsy detection en
dc.subject EEG signal processing en
dc.subject Neonatal seizure en
dc.subject Biomedical signal processing en
dc.subject Machine learning en
dc.subject Epileptiform detection en
dc.subject Epileptiform activity detection en
dc.subject Spike detection en
dc.subject Brain-computer interface en
dc.subject Blind source separation en
dc.subject Supervised learning en
dc.subject Neonatal seizure detection en
dc.subject Epileptiform activity en
dc.subject.lcsh Electroencephalography en
dc.subject.lcsh Convulsions in children en
dc.subject.lcsh Epilepsy in children en
dc.title Artefact detection and removal algorithms for EEG diagnostic systems en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PHD (Engineering) en
dc.internal.availability Full text available en No embargo required en
dc.description.version Accepted Version
dc.contributor.funder Science Foundation Ireland en
dc.description.status Not peer reviewed en Electrical and Electronic Engineering en
dc.check.type No Embargo Required
dc.check.reason No embargo required en
dc.check.opt-out Not applicable en
dc.thesis.opt-out false
dc.check.embargoformat Not applicable en
dc.internal.conferring Spring Conferring 2014 en

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© 2013, Simon H. O'Regan Except where otherwise noted, this item's license is described as © 2013, Simon H. O'Regan
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