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Found 7 papers, 3 papers with code
Ensemble learning using individual neonatal data for seizure detection
The weighted mean aggregation scheme showed best performance, it was only marginally outperformed by the Dawid--Skene method when local detectors approach performance of a single detector trained on all available data.
Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensors
These data were manually annotated for infant posture and movement based on video recordings of the sessions, and using a novel annotation scheme specifically designed to assess the overall movement pattern of infants in the given age group.
Comparison of end-to-end neural network architectures and data augmentation methods for automatic infant motility assessment using wearable sensors
In addition, we explore the benefits of data augmentation methods in ideal and non-ideal recording conditions.
Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy
Clinical efficacy was tested by comparing how the SDA and human experts quantified seizure burden and identified clinically significant periods of seizure activity in the EEG.
Development of Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels
In addition to training and validating a single EEG channel quiet sleep detector, we constructed Sleep State Trend (SST), a bedside-ready means for visualizing classifier outputs.
Evaluation of self-supervised pre-training for automatic infant movement classification using wearable movement sensors
The recently-developed infant wearable MAIJU provides a means to automatically evaluate infants' motor performance in an objective and scalable manner in out-of-hospital settings.
Modeling 3D Infant Kinetics Using Adaptive Graph Convolutional Networks
Reliable methods for the neurodevelopmental assessment of infants are essential for early detection of medical issues that may need prompt interventions.
