Neuromonitoring modalities predicting neurological impairment in pediatric congenital heart disease: a systematic review.

Unlabelled: The increased risk of neurodevelopmental impairment in children with congenital heart disease (CHD) has been established, but the search for targeted neurological predictors of adverse outcome is ongoing. This systematic review reports on the utility of three functional neuromonitoring modalities, Near-infrared Spectroscopy (NIRS), electroencephalography (EEG) and biochemical biomarkers, in predicting either clinical neurodevelopmental outcome or structural brain abnormalities after pediatric CHD surgery. Medline, Embase, CENTRAL, Web of Science, clinicaltrials.

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.

A multi-task and multi-channel convolutional neural network for semi-supervised neonatal artefact detection.

. Automated artefact detection in the neonatal electroencephalogram (EEG) is crucial for reliable automated EEG analysis, but limited availability of expert artefact annotations challenges the development of deep learning models for artefact detection. This paper proposes a semi-supervised deep learning approach for artefact detection in neonatal EEG that requires few labelled data by training a multi-task convolutional neural network (CNN).

Partial wavelet coherence as a robust method for assessment of neurovascular coupling in neonates with hypoxic ischemic encephalopathy.

In neonates with hypoxic ischemic encephalopathy, the computation of wavelet coherence between electroencephalogram (EEG) power and regional cerebral oxygen saturation (rSO2) is a promising method for the assessment of neurovascular coupling (NVC), which in turn is a promising marker for brain injury. However, instabilities in arterial oxygen saturation (SpO2) limit the robustness of previously proposed methods. Therefore, we propose the use of partial wavelet coherence, which can eliminate the influence of SpO2.

Assessing Neurovascular Coupling Using Wavelet Coherence in Neonates with Asphyxia.

Brain monitoring is important in neonates with asphyxia in order to assess the severity of hypoxic ischaemic encephalopathy (HIE) and identify neonates at risk of adverse neurodevelopmental outcome. Previous studies suggest that neurovascular coupling (NVC), quantified as the interaction between electroencephalography (EEG) and near-infrared spectroscopy (NIRS)-derived regional cerebral oxygen saturation (rSO) is a promising biomarker for HIE severity and outcome. In this study, we explore how wavelet coherence can be used to assess NVC.

Cerebral oxygenation and body position in the preterm infant: A systematic review and meta-analysis.

Aim: After preterm birth, supine head midline position is supported for stable cerebral blood flow (CBF) and prevention of intraventricular haemorrhage (IVH), while prone position supports respiratory function and enables skin-to-skin care. The prone compared to supine position could lead to a change in near-infrared derived cerebral tissue oxygen saturation (rScO2), which is a surrogate for cerebral blood flow (CBF). By monitoring rScO2 neonatologists aim to stabilise CBF during intensive care and prevent brain injury.

Automated detection and removal of flat line segments and large amplitude fluctuations in neonatal electroencephalography.

Background: Artefact removal in neonatal electroencephalography (EEG) by visual inspection generally depends on the expertise of the operator, is time consuming and is not a consistent pre-processing step to the pipeline for the automated EEG analysis. Therefore, there is the need for the automated detection and removal of artefacts in neonatal EEG, especially of distinct and predominant artefacts such as flat line segments (mainly caused by instrumental error where contact between electrodes and head box is lost) and large amplitude fluctuations (related to neonatal movements).

Method: A threshold-based algorithm for the automated detection and removal of flat line segments and large amplitude fluctuations in neonatal EEG of infants at term-equivalent age is developed.

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.

Relationship Between Early Functional and Structural Brain Developments and Brain Injury in Preterm Infants.

Background: Recent studies explored the relationship between early brain function and brain morphology, based on the hypothesis that increased brain activity can positively affect structural brain development and that excitatory neuronal activity stimulates myelination.

Objective: To investigate the relationship between maturational features from early and serial aEEGs after premature birth and MRI metrics characterizing structural brain development and injury, measured around 30weeks postmenstrual age (PMA) and at term. Moreover, we aimed to verify whether previously developed maturational EEG features are related with PMA.

The effect of early procedural pain in preterm infants on the maturation of electroencephalogram and heart rate variability.

Preterm infants show a higher incidence of cognitive, social, and behavioral problems, even in the absence of major medical complications during their stay in the neonatal intensive care unit (NICU). Several authors suggest that early-life experience of stress and procedural pain could impact cerebral development and maturation resulting in an altered development of cognition, behavior, or motor patterns in later life. However, it remains very difficult to assess this impact of procedural pain on physiological development.

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'.

Cerebral autoregulation and activity after propofol for endotracheal intubation in preterm neonates.

Background: Despite increasing use of propofol in neonates, observations on cerebral effects are limited.

Aim: To investigate cerebral autoregulation (CAR) and activity after propofol for endotracheal intubation in preterm neonates.

Methods: Twenty-two neonates received propofol before intubation as part of a published dose-finding study.

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 visual labelling of the sleep stages is a time consuming task, automated analysis of electroencephalography (EEG) to identify sleep stages is of great interest to clinicians.

Automated EEG background analysis to identify neonates with hypoxic-ischemic encephalopathy treated with hypothermia at risk for adverse outcome: A pilot study.

Background: To improve the objective assessment of continuous video-EEG (cEEG) monitoring of neonatal brain function, the aim was to relate automated derived amplitude and duration parameters of the suppressed periods in the EEG background (dynamic Interburst Interval= dIBIs) after neonatal hypoxic-ischemic encephalopathy (HIE) to favourable or adverse neurodevelopmental outcome.

Methods: Nineteen neonates (gestational age 36-41 weeks) with HIE underwent therapeutic hypothermia and had cEEG-monitoring. EEGs were retrospectively analyzed with a previously developed algorithm to detect the dynamic Interburst Intervals.

A brain-age model for preterm infants based on functional connectivity.

Objective: In this study, the development of EEG functional connectivity during early development has been investigated in order to provide a predictive age model for premature infants.

Approach: The functional connectivity has been assessed via the coherency function (its imaginary part (ImCoh) and its mean squared magnitude (MSC)), the phase locking value ([Formula: see text]) and the Hilbert-Schimdt dependence (HSD) in a dataset of 30 patients, partially described and employed in previous studies (Koolen et al 2016 Neuroscience 322 298-307; Lavanga et al 2017 Complexity 2017 1-13). Infants' post-menstrual age (PMA) ranges from 27 to 42 weeks.

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.

Automatic quiet sleep detection based on multifractality in preterm neonates: Effects of maturation.

This study investigates the multifractal formalism framework for quiet sleep detection in preterm babies. EEG recordings from 25 healthy preterm infants were used in order to evaluate the performance of multifractal measures for the detection of quiet sleep. Results indicate that multifractal analysis based on wavelet leaders is able to identify quiet sleep epochs, but the classifier performances seem to be highly affected by the infant's age.

The Use of Hemodynamic and Cerebral Monitoring to Study Pharmacodynamics in Neonates.

Background: Drugs acting on the cardiovascular and central nervous system often display relatively fast clinical responses, which may differ in neonates compared to children and adults. Introduction of bedside monitoring tools might be of additional value in the pharmacodynamic (PD) assessment of such drugs in neonates.

Methods: We aim to provide an overview of the frequently used monitoring tools to assess drug effects on the hemodynamic status as well as the cerebral circulation, oxygenation and cerebral metabolism in neonates.