{"id":{"repo_id":"cork","oai_identifier":"oai:cora.ucc.ie:10468/17925"},"canonical_url":"https://search.dev.ndltd.org/etd/cork/oai:cora.ucc.ie:10468/17925","repository":{"repo_id":"cork","name":"University College Cork","base_url":"https://cora.ucc.ie/server/oai/request"},"display":{"title":"Non-linear dynamics and critical transitions in neonatal brain","abstract":"Neonatal 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.","abstract_html":"Neonatal 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.","abstract_has_math":false,"creators":["Papanikolaou, Georgios"],"institution":"University College Cork","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Wieczorek, Sebastian","Amann, Andreas"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024","date_published":"2024","updated_at":"2026-07-24T01:48:31Z","subjects":["Non linear dynamics","Critical transitions","Epilepsy","Early warning signals"],"languages":["en"],"rights":["© 2024, Georgios Papanikolaou."],"rights_urls":["https://creativecommons.org/publicdomain/zero/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10468/17925","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Wieczorek, Sebastian","Amann, Andreas"]},{"key":"dc:creator","label":"Author","values":["Papanikolaou, Georgios"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-10-01T09:22:21Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-10-01T09:22:21Z"]},{"key":"dc:date.issued","label":"Date","values":["2024"]},{"key":"dc:publisher","label":"Institution","values":["University College Cork"]},{"key":"dc:type","label":"Dc Type","values":["Masters thesis (Research)"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["MSc - Master of Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Non linear dynamics","Critical transitions","Epilepsy","Early warning signals"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["© 2024, Georgios Papanikolaou."]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://creativecommons.org/publicdomain/zero/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10468/17925"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Neonatal 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."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Non-linear dynamics and critical transitions in neonatal brain"]}]}],"canonical_facts":{"dc:contributor.advisor":["Wieczorek, Sebastian","Amann, Andreas"],"dc:creator":["Papanikolaou, Georgios"],"dc:date.accessioned":["2025-10-01T09:22:21Z"],"dc:date.available":["2025-10-01T09:22:21Z"],"dc:date.issued":["2024"],"dc:description.abstract":["Neonatal 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."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10468/17925"],"dc:language.iso":["en"],"dc:publisher":["University College Cork"],"dc:rights":["© 2024, Georgios Papanikolaou."],"dc:rights.uri":["https://creativecommons.org/publicdomain/zero/1.0/"],"dc:subject":["Non linear dynamics","Critical transitions","Epilepsy","Early warning signals"],"dc:title":["Non-linear dynamics and critical transitions in neonatal brain"],"dc:type":["Masters thesis (Research)"],"dc:type.qualificationlevel":["Masters"],"dc:type.qualificationname":["MSc - Master of Science"]},"updated_at":"2026-07-24T01:48:31Z"}