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Found 7 papers, 1 papers with code
Sparse-Denoising Methods for Extracting Desaturation Transients in Cerebral Oxygenation Signals of Preterm Infants
Our analysis, using a synthetic NIRS-like dataset, showed that a LPF_TVD method outperformed the modified SSA_DCT method: median mean-squared error of 0. 97 (95% CI: 0. 86 to 1. 07) was lower for the LPF_TVD method compared to the modified SSA_DCT method of 1. 48 (95% CI: 1. 33 to 1. 63), P<0. 001.
Random Convolution Kernels with Multi-Scale Decomposition for Preterm EEG Inter-burst Detection
Linear classifiers with random convolution kernels are computationally efficient methods that need no design or domain knowledge.
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.
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.
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.
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.
