Dynamic classifiers for neonatal brain monitoring

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dc.check.embargoformatNot applicableen
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dc.contributor.advisorLightbody, Gordon,en
dc.contributor.advisorMarnane, William P.en
dc.contributor.advisorBoylan, Geraldine B.en
dc.contributor.advisorTemko, Andriyen
dc.contributor.authorAhmed, Rehan
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2016-09-05T11:33:37Z
dc.date.available2016-09-05T11:33:37Z
dc.date.issued2016
dc.date.submitted2016
dc.description.abstractBrain injury due to lack of oxygen or impaired blood flow around the time of birth, may cause long term neurological dysfunction or death in severe cases. The treatments need to be initiated as soon as possible and tailored according to the nature of the injury to achieve best outcomes. The Electroencephalogram (EEG) currently provides the best insight into neurological activities. However, its interpretation presents formidable challenge for the neurophsiologists. Moreover, such expertise is not widely available particularly around the clock in a typical busy Neonatal Intensive Care Unit (NICU). Therefore, an automated computerized system for detecting and grading the severity of brain injuries could be of great help for medical staff to diagnose and then initiate on-time treatments. In this study, automated systems for detection of neonatal seizures and grading the severity of Hypoxic-Ischemic Encephalopathy (HIE) using EEG and Heart Rate (HR) signals are presented. It is well known that there is a lot of contextual and temporal information present in the EEG and HR signals if examined at longer time scale. The systems developed in the past, exploited this information either at very early stage of the system without any intelligent block or at very later stage where presence of such information is much reduced. This work has particularly focused on the development of a system that can incorporate the contextual information at the middle (classifier) level. This is achieved by using dynamic classifiers that are able to process the sequences of feature vectors rather than only one feature vector at a time.en
dc.description.sponsorshipScience Foundation Ireland (SFI 10/IN.1/B3036)en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationAhmed, R. 2016. Dynamic classifiers for neonatal brain monitoring. PhD Thesis, University College Cork.en
dc.identifier.endpage173en
dc.identifier.urihttps://hdl.handle.net/10468/3063
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2016, Rehan Ahmed.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectAutomated neonatal seizure detectionen
dc.subjectClassifieren
dc.subjectDynamic classifiersen
dc.subjectMachine learningen
dc.subjectNeonatal EEGen
dc.subjectBrain monitoringen
dc.subjectSupport vector machinesen
dc.subjectGaussian mixture modelsen
dc.subjectGaussian dynamic time warping kernelen
dc.subjectSequential classifieren
dc.subjectSupervector kernelen
dc.thesis.opt-outfalse
dc.titleDynamic classifiers for neonatal brain monitoringen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePHD (Engineering)en
ucc.workflow.supervisorg.lightbody@ucc.ie
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