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Found 6 papers, 1 papers with code
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.
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 EEG Interpretation and Decision Support Framework for Mobile Platforms
The system aims to increase the demographic of clinicians capable of diagnosing abnormalities in neonatal EEG.
Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach
To our knowledge, this framework is the first example of a GAN capable of continuous ABP generation from an input PPG signal that also uses a federated learning methodology.
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.
