2026-09-22T05:07:15Zhttps://cora.ucc.ie/server/oai/request

oai:cora.ucc.ie:10468/179252025-10-02T02:02:57Zcom_10468_388com_10468_5com_10468_88com_10468_1col_10468_9859col_10468_9978col_10468_8983

Wieczorek, Sebastian Amann, Andreas Papanikolaou, Georgios 2025-10-01T09:22:21Z 2025-10-01T09:22:21Z 2024 2024 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. Not peer reviewed Accepted Version application/pdf Papanikolaou, G. 2024. Non-linear dynamics and critical transitions in neonatal brain. MSc Thesis, University College Cork. 74 https://hdl.handle.net/10468/17925 en University College Cork © 2024, Georgios Papanikolaou. https://creativecommons.org/publicdomain/zero/1.0/ Non linear dynamics Critical transitions Epilepsy Early warning signals Non-linear dynamics and critical transitions in neonatal brain Masters thesis (Research) Masters MSc - Master of Scienceinfo:eu-repo/semantics/openAccess