Dynamic classifiers for neonatal brain monitoring

Show simple item record

dc.contributor.advisor Lightbody, Gordon, en
dc.contributor.advisor Marnane, William P. en
dc.contributor.advisor Boylan, Geraldine B. en
dc.contributor.advisor Temko, Andriy en
dc.contributor.author Ahmed, Rehan
dc.date.accessioned 2016-09-05T11:33:37Z
dc.date.available 2016-09-05T11:33:37Z
dc.date.issued 2016
dc.date.submitted 2016
dc.identifier.citation Ahmed, R. 2016. Dynamic classifiers for neonatal brain monitoring. PhD Thesis, University College Cork. en
dc.identifier.endpage 173 en
dc.identifier.uri http://hdl.handle.net/10468/3063
dc.description.abstract Brain 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.sponsorship 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 © 2016, Rehan Ahmed. en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/ en
dc.subject Automated neonatal seizure detection en
dc.subject Classifier en
dc.subject Dynamic classifiers en
dc.subject Machine learning en
dc.subject Neonatal EEG en
dc.subject Brain monitoring en
dc.subject Support vector machines en
dc.subject Gaussian mixture models en
dc.subject Gaussian dynamic time warping kernel en
dc.subject Sequential classifier en
dc.subject Supervector kernel en
dc.title Dynamic classifiers for neonatal brain monitoring en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PHD (Engineering) en
dc.internal.availability Full text available en
dc.check.info No embargo required en
dc.description.version Accepted Version
dc.contributor.funder Science Foundation Ireland en
dc.description.status Not peer reviewed en
dc.internal.school 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
ucc.workflow.supervisor g.lightbody@ucc.ie
dc.internal.conferring Autumn 2016 en


Files in this item

This item appears in the following Collection(s)

Show simple item record

© 2016, Rehan Ahmed. Except where otherwise noted, this item's license is described as © 2016, Rehan Ahmed.
This website uses cookies. By using this website, you consent to the use of cookies in accordance with the UCC Privacy and Cookies Statement. For more information about cookies and how you can disable them, visit our Privacy and Cookies statement