36 citations · 38 across the 8 of their papers we have counts for
8 papers
Neonatal EEG graded for severity of background abnormalities in hypoxic-ischaemic encephalopathy
John M O'Toole, Sean R Mathieson, Sumit A Raurale +4
This report describes a set of neonatal electroencephalogram (EEG) recordings graded according to the severity of abnormalities in the background pattern. The dataset consists of 1…
Sparse-Denoising Methods for Extracting Desaturation Transients in Cerebral Oxygenation Signals of Preterm Infants
Minoo Ashoori, Eugene M. Dempsey, Fiona B. McDonald +1
Preterm infants are at high risk of developing brain injury in the first days of life as a consequence of poor cerebral oxygen delivery. Near-infrared spectroscopy (NIRS) is an est…
Random Convolution Kernels with Multi-Scale Decomposition for Preterm EEG Inter-burst Detection
Christopher Lundy, John M. O'Toole
Linear classifiers with random convolution kernels are computationally efficient methods that need no design or domain knowledge. Unlike deep neural networks, there is no need to h…
Tracé alternant detector for grading hypoxic-ischemic encephalopathy in neonatal EEG
Sumit A. Raurale, Geraldine B. Boylan, Sean R. Mathieson +3
Electroencephalography (EEG) is an important clinical tool to capture sleep-wake cycling. It can also be used for grading injury, known as hypoxic-ischaemic encephalopathy(HIE), ca…
Grading the severity of hypoxic-ischemic encephalopathy in newborn EEG using a convolutional neural network
Sumit A. Raurale, Geraldine B. Boylan, Gordon Lightbody +1
Electroencephalography (EEG) is a valuable clinical tool for grading injury caused by lack of blood and oxygen to the brain during birth. This study presents a novel end-to-end arc…
Identifying trace alternant activity in neonatal EEG using an inter-burst detection approach
Sumit A. Raurale, Geraldine B. Boylan, Gordon Lightbody +1
Electroencephalography (EEG) is an important clinical tool for reviewing sleep-wake cycling in neonates in intensive care. Trace alternant (TA)-a characteristic pattern of EEG acti…
Machine learning without a feature set for detecting bursts in the EEG of preterm infants
John M. O'Toole, Geraldine B. Boylan
Deep neural networks enable learning directly on the data without the domain knowledge needed to construct a feature set. This approach has been extremely successful in almost all…
Suitability of an inter-burst detection method for grading hypoxic-ischemic encephalopathy in newborn EEG
Sumit A. Raurale, Saif Nalband, Geraldine B. Boylan +2
Electroencephalography (EEG) is an important clinical tool for grading injury caused by lack of oxygen or blood to the brain during birth. Characteristics of low-voltage waveforms,…