Neonatal sleep stage identification using long short-term memory learning system
L Fraiwan, M Alkhodari - Medical & Biological Engineering & Computing, 2020 - Springer
Neonatal sleep analysis at the neonatal intensive care units (NICU) is critical for the
diagnosis of any brain growth risks during the early stages of life. In this paper, an …
diagnosis of any brain growth risks during the early stages of life. In this paper, an …
Neonatal EEG sleep stage classification based on deep learning and HMM
Objective. Automatic sleep stage scoring is of great importance for investigating sleep
architecture during infancy. In this work, we introduce a novel multichannel approach based …
architecture during infancy. In this work, we introduce a novel multichannel approach based …
Deep learning approach for ECG-based automatic sleep state classification in preterm infants
Preterm infant neuronal development is related to the distribution of their sleep states. The
distribution changes throughout development. Automated sleep state monitoring can …
distribution changes throughout development. Automated sleep state monitoring can …
Quiet sleep detection in preterm infants using deep convolutional neural networks
Objective. Neonates spend most of their time asleep. Sleep of preterm infants evolves
rapidly throughout maturation and plays an important role in brain development. Since …
rapidly throughout maturation and plays an important role in brain development. Since …
Neonatal sleep state identification using deep learning autoencoders
Neonatal sleep state analysis provides a tool for diagnosis of several possible physiological
disorders in newborns. The sleep state identification is a time consuming procedure where …
disorders in newborns. The sleep state identification is a time consuming procedure where …
[PDF][PDF] EEG-based neonatal sleep stage classification using ensemble learning
Sleep stage classification can provide important information regarding neonatal brain
development and maturation. Visual annotation, using polysomnography (PSG), is …
development and maturation. Visual annotation, using polysomnography (PSG), is …
Time frequency analysis for automated sleep stage identification in fullterm and preterm neonates
This work presents a new methodology for automated sleep stage identification in neonates
based on the time frequency distribution of single electroencephalogram (EEG) recording …
based on the time frequency distribution of single electroencephalogram (EEG) recording …
A convolutional neural network-based decision support system for neonatal quiet sleep detection
Sleep plays an important role in neonatal brain and physical development, making its
detection and characterization important for assessing early-stage development. In this …
detection and characterization important for assessing early-stage development. In this …
A convolutional neural network outperforming state-of-the-art sleep staging algorithms for both preterm and term infants
Objective. To classify sleep states using electroencephalogram (EEG) that reliably works
over a wide range of preterm ages, as well as term age. Approach. A convolutional neural …
over a wide range of preterm ages, as well as term age. Approach. A convolutional neural …
Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals
Automated evaluation of a subject's neurocognitive performance (NCP) is a relevant topic in
neurological and clinical studies. NCP represents the mental/cognitive human capacity in …
neurological and clinical studies. NCP represents the mental/cognitive human capacity in …