Artefact detection and removal algorithms for EEG diagnostic systems

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dc.check.embargoformatNot applicableen
dc.check.infoNo embargo requireden
dc.check.opt-outNot applicableen
dc.check.reasonNo embargo requireden
dc.check.typeNo Embargo Required
dc.contributor.advisorMarnane, William P.en
dc.contributor.advisorLightbody, Gordonen
dc.contributor.advisorBoylan, Geraldine B.en
dc.contributor.authorO'Regan, Simon H.
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2014-02-17T16:46:46Z
dc.date.available2014-02-17T16:46:46Z
dc.date.issued2013
dc.date.submitted2013
dc.description.abstractThe 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.sponsorshipScience Foundation Ireland (SFI/07/SRC/I1169); Science Foundation Ireland (SFI/10/IN.1/B3036)en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationO'Regan, S. H. 2013. Artefact detection and removal algorithms for EEG diagnostic systems. PhD Thesis, University College Cork.en
dc.identifier.endpage216
dc.identifier.urihttps://hdl.handle.net/10468/1391
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2013, Simon H. O'Reganen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectArtefacten
dc.subjectArtifacten
dc.subjectArtefact detectionen
dc.subjectElectroencephalogramen
dc.subjectEpilepsyen
dc.subjectAlgorithmsen
dc.subjectBCIen
dc.subjectArtefact removalen
dc.subjectArtifact removalen
dc.subjectArtefact rejectionen
dc.subjectRespiratory signalsen
dc.subjectMultimodalen
dc.subjectPhysiological signalsen
dc.subjectGyroscopesen
dc.subjectMovementen
dc.subjectHead-movement detectionen
dc.subjectSeizure detectionen
dc.subjectHead-movement artefacten
dc.subjectEpilepsy detectionen
dc.subjectEEG signal processingen
dc.subjectNeonatal seizureen
dc.subjectBiomedical signal processingen
dc.subjectMachine learningen
dc.subjectEpileptiform detectionen
dc.subjectEpileptiform activity detectionen
dc.subjectSpike detectionen
dc.subjectBrain-computer interfaceen
dc.subjectBlind source separationen
dc.subjectSupervised learningen
dc.subjectNeonatal seizure detectionen
dc.subjectEpileptiform activityen
dc.subject.lcshElectroencephalographyen
dc.subject.lcshConvulsions in childrenen
dc.subject.lcshEpilepsy in childrenen
dc.thesis.opt-outfalse
dc.titleArtefact detection and removal algorithms for EEG diagnostic systemsen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePHD (Engineering)en
ucc.workflow.supervisorl.marnane@ucc.ie
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