145 citations · 215 across the 5 of their papers we have counts for
7 papers
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…
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…
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…
On sound-based interpretation of neonatal EEG
Sergi Gomez, Mark O'Sullivan, Emanuel Popovici +3
Significant training is required to visually interpret neonatal EEG signals. This study explores alternative sound-based methods for EEG interpretation which are designed to allow…
System Level Framework for Assessing the Accuracy of Neonatal EEG Acquisition
Mark O'Sullivan, Emanuel Popovici, Andrea Bocchino +3
Significant research has been conducted in recent years to design low-cost alternatives to the current EEG monitoring systems used in healthcare facilities. Testing such systems on…
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…