no code implementations • 28 May 2021 • Alison O'Shea, Gordon Lightbody, Geraldine Boylan, Andriy Temko
The system performance is assessed on a large database of continuous EEG recordings of 834h in duration; this is further validated on a held-out publicly available dataset and compared with two baseline SVM based systems.
no code implementations • 8 Jun 2018 • Alison O'Shea, Gordon Lightbody, Geraldine Boylan, Andriy Temko
Two deep convolutional networks are compared with a shallow SVM-based neonatal seizure detector, which relies on the extraction of hand-crafted features.
no code implementations • 8 Jun 2018 • Mark O'Sullivan, Sergi Gomez, Alison O'Shea, Eduard Salgado, Kevin Huillca, Sean Mathieson, Geraldine Boylan, Emanuel Popovici, Andriy Temko
The system aims to increase the demographic of clinicians capable of diagnosing abnormalities in neonatal EEG.
no code implementations • 18 Sep 2017 • Alison O'Shea, Gordon Lightbody, Geraldine Boylan, Andriy Temko
This study presents a novel end-to-end architecture that learns hierarchical representations from raw EEG data using fully convolutional deep neural networks for the task of neonatal seizure detection.