activity
20172021
most citedNeonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture

145 citations · 200 across the 2 of their papers we have counts for

collaborators

5 papers

eess.SP202155 cited

Deep Learning for EEG Seizure Detection in Preterm Infants

Alison OShea, Rehan Ahmed, Gordon Lightbody +7

EEG is the gold standard for seizure detection in the newborn infant, but EEG interpretation in the preterm group is particularly challenging; trained experts are scarce and the ta…

cs.LG2021145 cited

Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture

Alison O'Shea, Gordon Lightbody, Geraldine Boylan +1

A deep learning classifier for detecting seizures in neonates is proposed. This architecture is designed to detect seizure events from raw electroencephalogram (EEG) signals as opp…

q-bio.NC2018

Neonatal EEG Interpretation and Decision Support Framework for Mobile Platforms

Mark O'Sullivan, Sergi Gomez, Alison O'Shea +6

This paper proposes and implements an intuitive and pervasive solution for neonatal EEG monitoring assisted by sonification and deep learning AI that provides information about neo…

stat.ML2018

Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance

Alison O'Shea, Gordon Lightbody, Geraldine Boylan +1

This study presents a novel, deep, fully convolutional architecture which is optimized for the task of EEG-based neonatal seizure detection. Architectures of different depths were…

stat.ML2017

Neonatal Seizure Detection using Convolutional Neural Networks

Alison O'Shea, Gordon Lightbody, Geraldine Boylan +1

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 neonat…