Clinical outcome prediction with an automated EEG trend, Brain State of the Newborn, after perinatal asphyxia.

Objective: To evaluate the utility of a fully automated deep learning -based quantitative measure of EEG background, Brain State of the Newborn (BSN), for early prediction of clinical outcome at four years of age.

Methods: The EEG monitoring data from eighty consecutive newborns was analyzed using the automatically computed BSN trend. BSN levels during the first days of life (a of total 5427 hours) were compared to four clinical outcome categories: favorable, cerebral palsy (CP), CP with epilepsy, and death.

A growth chart of brain function from infancy to adolescence based on EEG.

Background: In children, objective, quantitative tools that determine functional neurodevelopment are scarce and rarely scalable for clinical use. Direct recordings of cortical activity using routinely acquired electroencephalography (EEG) offer reliable measures of brain function.

Methods: We developed and validated a measure of functional brain age (FBA) using a residual neural network-based interpretation of the paediatric EEG.

Optimization of time series features to estimate brain age in children from electroencephalography.

Functional brain age measures in children, derived from the electroencephalogram (EEG), offer direct and objective measures in assessing neurodevelopmental status. Here we explored the effectiveness of 32 preselected 'handcrafted' EEG features in predicting brain age in children. These features were benchmarked against a large library of highly comparative multivariate time series features (>7000 features).

Inter-site generalizability of EEG based age prediction algorithms in the preterm infant.

. To overcome the effects of site differences in EEG-based brain age prediction in preterm infants..

An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation.

Background: Electroencephalogram (EEG) monitoring is recommended as routine in newborn neurocritical care to facilitate early therapeutic decisions and outcome predictions. EEG's larger-scale implementation is, however, hindered by the shortage of expertise needed for the interpretation of spontaneous cortical activity, the EEG background. We developed an automated algorithm that transforms EEG recordings to quantified interpretations of EEG background and provides simple intuitive visualisations in patient monitors.

Bedside tracking of functional autonomic age in preterm infants.

Background: Preterm birth predisposes infants to adverse outcomes that, without early intervention, impacts their long-term health. To assist bedside monitoring, we developed a tool to track the autonomic maturation of the preterm by assessing heart rate variability (HRV) changes during intensive care.

Methods: Electrocardiogram (ECG) recordings were longitudinally recorded in 67 infants (26-38 weeks postmenstrual age (PMA)).

Facilitating early parent-infant emotional connection improves cortical networks in preterm infants.

Exposure to environmental adversities during early brain development, such as preterm birth, can affect early brain organization. Here, we studied whether development of cortical activity networks in preterm infants may be improved by a multimodal environmental enrichment via bedside facilitation of mother-infant emotional connection. We examined functional cortico-cortical connectivity at term age using high-density electroencephalography recordings in infants participating in a randomized controlled trial of Family Nurture Intervention (FNI).

Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels.

Objective: To develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units.

Methods: A deep learning-based algorithm was designed and trained using 53 EEG recordings from a long-term (a)EEG monitoring in 30 near-term neonates. The results were validated using an independent dataset from 30 polysomnography recordings.

Modelling herd immunity requirements in Queensland: impact of vaccination effectiveness, hesitancy and variants of SARS-CoV-2.

Long-term control of SARS-CoV-2 outbreaks depends on the widespread coverage of effective vaccines. In Australia, two-dose vaccination coverage of above 90% of the adult population was achieved. However, between August 2020 and August 2021, hesitancy fluctuated dramatically.

Risk of sustained SARS-CoV-2 transmission in Queensland, Australia.

We used an agent-based model Covasim to assess the risk of sustained community transmission of SARSCoV-2/COVID-19 in Queensland (Australia) in the presence of high-transmission variants of the virus. The model was calibrated using the demographics, policies, and interventions implemented in the state. Then, using the calibrated model, we simulated possible epidemic trajectories that could eventuate due to leakage of infected cases with high-transmission variants, during a period without recorded cases of locally acquired infections, known in Australian settings as "zero community transmission".

Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy.

Neonatal seizure detection algorithms (SDA) are approaching the benchmark of human expert annotation. Measures of algorithm generalizability and non-inferiority as well as measures of clinical efficacy are needed to assess the full scope of neonatal SDA performance. We validated our neonatal SDA on an independent data set of 28 neonates.

Why monitor the neonatal brain-that is the important question.

A key goal of neonatal neurocritical care is improved outcomes, and brain monitoring plays an essential role. The recent NEST trial reported no outcome benefits using aEEG monitoring compared to clinical seizure identification among neonates treated for seizures. However, the study failed to prove the effects of monitoring on seizure treatment in the first place.

Electroencephalographic studies in growth-restricted and small-for-gestational-age neonates.

Foetal growth restriction (FGR) and being born small for gestational age (SGA) are associated with neurodevelopmental delay. Early diagnosis of neurological damage is difficult in FGR and SGA neonates. Electroencephalography (EEG) has the potential as a tool for the assessment of brain development in FGR/SGA neonates.

Automated detection of artefacts in neonatal EEG with residual neural networks.

Background And Objective: To develop a computational algorithm that detects and identifies different artefact types in neonatal electroencephalography (EEG) signals.

Methods: As part of a larger algorithm, we trained a Residual Deep Neural Network on expert human annotations of EEG recordings from 79 term infants recorded in a neonatal intensive care unit (112 h of 18-channel recording). The network was trained using 10 fold cross validation in Matlab.

An Open Source Classifier for Bed Mattress Signal in Infant Sleep Monitoring.

Objective: To develop a non-invasive and clinically practical method for a long-term monitoring of infant sleep cycling in the intensive care unit.

Methods: Forty three infant polysomnography recordings were performed at 1-18 weeks of age, including a piezo element bed mattress sensor to record respiratory and gross-body movements. The hypnogram scored from polysomnography signals was used as the ground truth in training sleep classifiers based on 20,022 epochs of movement and/or electrocardiography signals.

Reliability and accuracy of EEG interpretation for estimating age in preterm infants.

Objectives: To determine the accuracy of, and agreement among, EEG and aEEG readers' estimation of maturity and a novel computational measure of functional brain age (FBA) in preterm infants.

Methods: Seven experts estimated the postmenstrual ages (PMA) in a cohort of recordings from preterm infants using cloud-based review software. The FBA was calculated using a machine learning-based algorithm.

Automated cot-side tracking of functional brain age in preterm infants.

Objective: A major challenge in the care of preterm infants is the early identification of compromised neurological development. While several measures are routinely used to track anatomical growth, there is a striking lack of reliable and objective tools for tracking maturation of early brain function; a cornerstone of lifelong neurological health. We present a cot-side method for measuring the functional maturity of the newborn brain based on routinely available neurological monitoring with electroencephalography (EEG).

Temporal evolution of quantitative EEG within 3 days of birth in early preterm infants.

For the premature newborn, little is known about changes in brain activity during transition to extra-uterine life. We aim to quantify these changes in relation to the longer-term maturation of the developing brain. We analysed EEG for up to 72 hours after birth from 28 infants born <32 weeks of gestation.

Designing a trial for neonatal seizure treatment.

Neonatal seizures are widely considered a neurological emergency with a need for prompt treatment, yet they are known to present a highly elusive target for bedside clinicians. Recent studies have suggested that the design of a neonatal seizure treatment trial will profoundly influence the sample size, which may readily increase to hundreds or even thousands as the achieved effect size diminishes to clinical irrelevance. The self-limiting and rapidly resolving nature of neonatal seizures diminishes the measurable treatment effect every hour after seizure onset and any effect may potentially be confused with spontaneous resolution, precluding the value of many observational studies.

The effect of reducing EEG electrode number on the visual interpretation of the human expert for neonatal seizure detection.

Objectives: To measure changes in the visual interpretation of the EEG by the human expert for neonatal seizure detection when reducing the number of recording electrodes.

Methods: EEGs were recorded from 45 infants admitted to the neonatal intensive care unit (NICU). Three experts annotated seizures in EEG montages derived from 19, 8 and 4 electrodes.

Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach.

Aim: To develop a method that segments preterm EEG into bursts and inter-bursts by extracting and combining multiple EEG features.

Methods: Two EEG experts annotated bursts in individual EEG channels for 36 preterm infants with gestational age < 30 weeks. The feature set included spectral, amplitude, and frequency-weighted energy features.

Heart rate variability in hypoxic ischemic encephalopathy during therapeutic hypothermia.

Background: Therapeutic hypothermia (TH) aims to ameliorate further injury in infants with moderate and severe hypoxic ischemic encephalopathy (HIE). We aim to assess the effect of TH on heart rate variability (HRV) in infants with HIE.

Methods: Multichannel video-electroencephalography (EEG) and electrocardiography were assessed at 6-72 h after birth in full-term infants with HIE, recruited prior to (pre-TH group) and following (TH group) the introduction of TH in our neonatal unit.