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Found 9 papers, 1 papers with code
Neonatal EEG graded for severity of background abnormalities in hypoxic-ischaemic encephalopathy
For each neonate, multiple 1-hour epochs of good quality EEG were selected and then graded for background abnormalities.
Tracé alternant detector for grading hypoxic-ischemic encephalopathy in neonatal EEG
These results validate how detecting the presence or absence of TA can be used to quantify the grade of HIE injury in neonatal EEG and open up the possibility of a clinically-meaningful grading system.
Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture
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.
Deep Learning for EEG Seizure Detection in Preterm Infants
An AUC of 88. 3% was obtained when tested on preterm EEG as compared to 96. 6% obtained when tested on term EEG.
Identifying trace alternant activity in neonatal EEG using an inter-burst detection approach
This study presents a novel approach for detecting TA activity by first detecting the inter-bursts and then processing the temporal map of the bursts and inter-bursts.
Grading the severity of hypoxic-ischemic encephalopathy in newborn EEG using a convolutional neural network
Electroencephalography (EEG) is a valuable clinical tool for grading injury caused by lack of blood and oxygen to the brain during birth.
Suitability of an inter-burst detection method for grading hypoxic-ischemic encephalopathy in newborn EEG
Electroencephalography (EEG) is an important clinical tool for grading injury caused by lack of oxygen or blood to the brain during birth.
Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance
Two deep convolutional networks are compared with a shallow SVM-based neonatal seizure detector, which relies on the extraction of hand-crafted features.
Neonatal Seizure Detection using Convolutional Neural Networks
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.
