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Found 6 papers, 1 papers with code
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.
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.
Machine learning without a feature set for detecting bursts in the EEG of preterm infants
Deep neural networks enable learning directly on the data without the domain knowledge needed to construct a feature set.
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.
NEURAL: quantitative features for newborn EEG using Matlab
Methods: We detail quantitative features frequently used in neonatal EEG analysis and present a Matlab software package together with exact implementation details for all features.
Medical Physics Neurons and Cognition Applications
