Establishing a standardised approach for the measurement of neonatal noxious-evoked brain activity in response to an acute somatic nociceptive heel lance stimulus.

Background: Electroencephalography (EEG) can be used in neonates to measure brain activity changes that are evoked by noxious events, such as clinically required immunisations, cannulation and heel lancing for blood tests. EEG provides an alternative approach to infer pain experience in infants compared with more commonly used behavioural and physiological pain assessments. Establishing the generalisability and construct validity of these measures will help corroborate the use of brain-derived outcomes to evaluate the efficacy of new or existing pharmacological and non-pharmacological methods to treat neonatal pain.

A machine learning artefact detection method for single-channel infant event-related potential studies.

. Automated detection of artefact in stimulus-evoked electroencephalographic (EEG) data recorded in neonates will improve the reproducibility and speed of analysis in clinical research compared with manual identification of artefact. Some studies use very short, single-channel epochs of EEG data with little recorded EEG per infant-for example because the clinical vulnerability of the infants limits access for recording.

Resting state electroencephalographic brain activity in neonates can predict age and is indicative of neurodevelopmental outcome.

Objective: Electroencephalography (EEG) can be used to estimate neonates' biological brain age. Discrepancies between postmenstrual age and brain age, termed the brain age gap, can potentially quantify maturational deviation. Existing brain age EEG models are not well suited to clinical cot-side use for estimating neonates' brain age gap due to their dependency on relatively large data and pre-processing requirements.

The PiNe box: Development and validation of an electronic device to time-lock multimodal responses to sensory stimuli in hospitalised infants.

Recording multimodal responses to sensory stimuli in infants provides an integrative approach to investigate the developing nervous system. Accurate time-locking across modalities is essential to ensure that responses are interpreted correctly, and could also improve clinical care, for example, by facilitating automatic and objective multimodal pain assessment. Here we develop and assess a system to time-lock stimuli (including clinically-required heel lances and experimental visual, auditory and tactile stimuli) to electrophysiological research recordings and data recorded directly from a hospitalised infant's vital signs monitor.

Functional brain maturation and sleep organisation in neonates with congenital heart disease.

Objective: Neonates with Congenital Heart Disease (CHD) have structural delays in brain development. To evaluate whether functional brain maturation and sleep-wake physiology is also disturbed, the Functional Brain Age (FBA) and sleep organisation on EEG during the neonatal period is investigated.

Methods: We compared 15 neonates with CHD who underwent multichannel EEG with healthy term newborns of the same postmenstrual age, including subgroup analysis for d-Transposition of the Great Arteries (d-TGA) (n = 8).

A Deep Shared Multi-Scale Inception Network Enables Accurate Neonatal Quiet Sleep Detection With Limited EEG Channels.

In this paper, we introduce a new variation of the Convolutional Neural Network Inception block, called Sinc, for sleep stage classification in premature newborn babies using electroencephalogram (EEG). In practice, there are many medical centres where only a limited number of EEG channels are recorded. Existing automated algorithms mainly use multi-channel EEGs which perform poorly when fewer numbers of channels are available.

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 on deep learning networks and hidden Markov models (HMM) to improve the accuracy of sleep stage classification in term neonates.

Approach: The classification performance was evaluated on quiet sleep (QS) and active sleep (AS) stages, each with two sub-states, using multichannel EEG data recorded from sixteen neonates with postmenstrual age of 38-40 weeks.

Applying a data-driven approach to quantify EEG maturational deviations in preterms with normal and abnormal neurodevelopmental outcomes.

Premature babies are subjected to environmental stresses that can affect brain maturation and cause abnormal neurodevelopmental outcome later in life. Better understanding this link is crucial to developing a clinical tool for early outcome estimation. We defined maturational trajectories between the Electroencephalography (EEG)-derived 'brain-age' and postmenstrual age (the age since the last menstrual cycle of the mother) from longitudinal recordings during the baby's stay in the Neonatal Intensive Care Unit.

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 network is developed to perform 2- and 4-class sleep classification in neonates. The network takes as input an 8-channel 30 s EEG segment and outputs the sleep state probabilities.

A Bayesian parametric model for quantifying brain maturation from sleep-EEG in the vulnerable newborn baby.

Newborn babies, particularly preterms, can exhibit early deviations in sleep maturation as seen by Electroencephalogram (EEG) recordings. This may be indicative of cognitive problems by school-age. The current 'clinically-driven' approach uses separate algorithms to first extract sleep states and then predict EEG 'brain-age'.

Automated EEG sleep staging in the term-age baby using a generative modelling approach.

Objective: We develop a method for automated four-state sleep classification of preterm and term-born babies at term-age of 38-40 weeks postmenstrual age (the age since the last menstrual cycle of the mother) using multichannel electroencephalogram (EEG) recordings. At this critical age, EEG differentiates from broader quiet sleep (QS) and active sleep (AS) stages to four, more complex states, and the quality and timing of this differentiation is indicative of the level of brain development. However, existing methods for automated sleep classification remain focussed only on QS and AS sleep classification.

Review of sleep-EEG in preterm and term neonates.

Neonatal sleep is a crucial state that involves endogenous driven brain activity, important for neuronal survival and guidance of brain networks. Sequential EEG-sleep analysis in preterm infants provides insights into functional brain integrity and can document deviations of the biologically pre-programmed process of sleep ontogenesis during the neonatal period. Visual assessment of neonatal sleep-EEG, with integration of both cerebral and non-cerebral measures to better define neonatal state, is still considered the gold standard.

An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation.

Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ([Formula: see text] age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment.