Non-linear dynamics and critical transitions in neonatal brain

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dc.contributor.advisorWieczorek, Sebastian
dc.contributor.advisorAmann, Andreas
dc.contributor.authorPapanikolaou, Georgiosen
dc.date.accessioned2025-10-01T09:22:21Z
dc.date.available2025-10-01T09:22:21Z
dc.date.issued2024
dc.date.submitted2024
dc.description.abstractNeonatal seizure detection methods have improved the quality of life for new born babies. However, most models suffer from an increased amount of false positive detections that restricts them from being used live in an Neonatal Intensive Care Unit. If the dynamical mechanisms responsible for transitions to seizure states can be understood, it may be possible to enhance the methods used for the detection of seizure events. One potential candidate to describe the events that trigger a seizure is a `critical transition'. Classical early warning signals, such as increased variance and autocorrelation, have proven to be robust measures for the detection and prediction of critical transitions in many different scenarios. In this study, we examine whether these classical early warning signals are present in EEG recordings of neonatal seizures.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationPapanikolaou, G. 2024. Non-linear dynamics and critical transitions in neonatal brain. MSc Thesis, University College Cork.
dc.identifier.endpage74
dc.identifier.urihttps://hdl.handle.net/10468/17925
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2024, Georgios Papanikolaou.
dc.rights.urihttps://creativecommons.org/publicdomain/zero/1.0/
dc.subjectNon linear dynamics
dc.subjectCritical transitions
dc.subjectEpilepsy
dc.subjectEarly warning signals
dc.titleNon-linear dynamics and critical transitions in neonatal brain
dc.typeMasters thesis (Research)en
dc.type.qualificationlevelMastersen
dc.type.qualificationnameMSc - Master of Science
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