Background: Biological age is a key concept in the development of biomarkers of health and disease. We develop a prediction of the functional autonomic age (FAA) from infancy to adolescence based on the ECG-derived tachogram recorded at the onset of N2 sleep. Methods: A cohort of ECG recordings from 1004 typically developing infants, children and adolescents (age range: 1 month to 17 years) was used to train feature-based and deep neural network-based regression models for the prediction of FAA. Weighted mean absolute error (wMAE) was used to define accuracy and evaluated with 10-fold cross-validation. Effect size was used to compare model accuracies and linear regression was used to evaluate confounds. The combination of FAA with an EEG-based estimate of functional brain age (FBA) was also tested. Results: A feature-based FAA had a wMAE of 1.78 years (95 %CI: 1.62-1.93, n = 1004) and was comparable to deep neural network regression (wMAE = 1.85 years, 95 %CI: 1.66-1.98). Accuracy was affected by age and age2 (t = -4.97, p < 0.001 and t = 9.66, p < 0.001, respectively) with smaller errors at younger ages, but not biological sex (t = -0.660, p = 0.510). Combining the FAA with a functional brain age derived from the EEG resulted improved accuracy, with neural networks-based methods superior (wMAE of 0.81 years, 95 %CI: 0.73-0.88, effect size D = 0.77, 95 %CI: 0.70-0.84, n = 1004). Conclusion: FAA derived from the tachogram accurately represents age from infancy to adolescence. The combination of FAA with FBA improves age prediction accuracy.

Paediatric brain activity can be measured effectively during light sleep; a vigilance state that manifests with similar phenomenology on the EEG across childhood. Here, we describe a curated dataset of EEG and ECG recordings from 1032 subjects from 2 months to 16 years of age (Helsinki Kids 1 K - HK1K). All subjects had age-appropriate EEG and ECG recordings, along with typical neurodevelopment, as confirmed by a clinical review of their medical records over the four years following the recording. These data can be used to define normative ranges of paediatric EEG/ECG, train foundation models of paediatric EEG/ECG, and generate age prediction algorithms that underpin measures of brain age gap.

Aim: To assess the inter-rater agreement and accuracy of human experts’ estimate of EEG maturational age (EMA) and a computer algorithm’s estimate of EMA over the first days after birth in a cohort of normally developing preterm infants. In addition, we explore the influence of post-natal age (PNA) on EMA estimates. Methods: Analysis was performed on EEG records from newborns determined appropriate for gestational age (GA) with favorable neurodevelopment at 2 years of age and without significant neurological compromise at time of EEG monitoring. Three 1h epochs of EEG were selected from 29 newborns with GA ranging from 23 to <32 weeks, within 72 h of birth. EEG epochs were visually assessed by two pediatric neurologists and a computer algorithm. In addition, the full, long-duration EEG recording of each newborn was assessed by one pediatric neurologist. EMA estimates were compared to GA using Pearson’s correlation coefficient (r) and bias and standard deviation of error (SDE). Intra-newborn agreements for the EMA estimates were assessed using standard deviation. Linear mixed-effects models were used to quantify the effect of PNA on EMA estimates. Results: The algorithm provides a more accurate estimate of GA using 1 h EEG epochs for correlation and bias: algorithm r = 0.83 vs. experts r = 0.60 and 0.66, p < 0.05 for n = 29; algorithm bias = −0.8 days vs. experts’ bias = 3.6 and 7.0 days, p < 0.01 for n = 29. SDE of 8.7 days for the algorithm was not significantly lower compared to the experts’ SDE = 12.4 and 13.2 days, p > 0.05. The algorithm has higher intra-newborn agreement compared to the experts: algorithm SDE = 4.9 days vs. experts SDE = 7.4 and 7.4 days, p = 0.027. For the two experts, increasing PNA is associated with an increase in EMA estimates of 6.6 days/days and 3.7 days/days. The assessment of full, long-duration EEG recordings improved the experts’ estimate of EMA (r = 0.82; SDE = 9.2 days). Conclusions: Automated analysis outperforms visual interpretation of the EEG at estimating EMA for short-duration EEG recordings. PNA is an important factor in EMA estimates.

Background: Nasal continuous positive airway pressure (CPAP) injuries are common for premature infants. Clinical use of three-dimensional (3D) scanning is established in adult medicine, but the possibilities in neonatal care are still emerging. Custom printed CPAP devices have the potential to reduce injuries and disfigurement in this vulnerable population. Aim: We sought to identify the most feasible portable 3D scanner for use in the neonatal intensive care environment towards the development of custom-fitting CPAP devices for premature infants. Methods: Four handheld 3D scanners were assessed and compared, Artec Leo, Revopoint POP 2, iPad Pro/ Metascan, and iPhone/Scandy Pro. Trained neonatal clinicians (medical and nursing) undertook mock scans in a simulated neonatal intensive care environment. Results: Sixty scans were performed by 13 neonatal clinicians (four medical/nurse practitioners and nine nurses). The median mean absolute error was 0.21 mm (interquartile range [IQR]: 0.19-0.26), 0.17 mm (IQR: 0.15-0.21), and 1.08 mm (IQR: 1.0-1.63) for Artec Leo, Revopoint POP 2, and Scandy Pro, respectively. Scan times were the quickest for Artec Leo at 22.9 sec (IQR: 18.5-27), followed by Revopoint POP 2 at 25.2 sec (IQR: 22-34.4). Artec Leo was rated most expensive, but Revopoint POP 2 was rated more ergonomic. Both app-based 3D scanners (Metascan and Scandy Pro) presented data security issues. Conclusions: Artec Leo and Revopoint POP 2 were identified as most feasible for use to perform 3D scans on premature infants in the neonatal intensive care environment. Crown Copyright (c) 2024 Published by Elsevier Ltd on behalf of Australian College of Critical Care Nurses Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Early structural and molecular development of the human cortex is extensively studied, but little is known about the development of neuronal activity across cortical regions. We used dense array electroencephalography recordings and a machine learning-based measure, functional brain age (FBA), to study spatiotemporally resolved maturation of cortical activity across the birth transition in human infants (male and female). We found clear spatial FBA gradients indicating more mature frontal cortical activity relative to other brain regions (geometric axis), as well as more mature activity in association cortices relative to sensory cortices (hierarchical axis). The frontal advance was explained by more mature bursting characteristics, a hallmark of early endogenous neuronal activity. The findings jointly support an advanced maturation of neuronal ensemble activity in cortical regions that are preparing to host synergistic, large-scale network interactions, a key global characteristic of mature brain function.

The human brain is a complex organ comprising billions of interconnected neurons, which enables interaction with both physical and social environments. Neural dynamics of the whole brain go far beyond just the sum of its individual elements; a property known as "synergy". Previously it has been shown that synergy is crucial for many complex brain functions and cognition, however, it remains unknown how and when the large number of discrete neurons evolve into the unified system able to support synergistic interactions. Here we analyzed high-density electroencephalography data from the late fetal period to one month after term age. We found that the human brain transitions from a redundancy-dominated to a synergy-dominated system around birth. Frontal regions lead the emergence of a synergistic scaffold comprised of overlapping subsystems, while the integration of sensory areas developed gradually, from occipital to central regions. Strikingly, early developmental trajectories of brain synergy were modulated by environmental enrichment associated with enhanced mother-infant interactions, and the level of synergy near term equivalent age was associated with later neurocognitive development.

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. The time dependent changes in BSN-based prediction for different outcomes were assessed by positive/negative predictive value (PPV/NPV) and by estimating the area under the receiver operating characteristic curve (AUC). Results: The BSN values were closely aligned with four visually determined EEG categories (p < 0.001), as well as with respect to clinical milestones of EEG recovery in perinatal Hypoxic Ischemic Encephalopathy (HIE; p < 0.003). Favorable outcome was related to a rapid recovery of the BSN trend, while worse outcomes related to a slow BSN recovery. Outcome predictions with BSN were accurate from 6 to 48 hours of age: For the favorable outcome, the AUC ranged from 95 to 99% (peak at 12 hours), and for the poor outcome the AUC ranged from 96 to 99% (peak at 12 hours). The optimal BSN levels for each PPV/NPV estimate changed substantially during the first 48 hours, ranging from 20 to 80. Conclusions: We show that the BSN provides an automated, objective, and continuous measure of brain activity in newborns. Significance: The BSN trend discloses the dynamic nature that exists in both cerebral recovery and outcome prediction, supports individualized patient care, rapid stratification and early prognosis. (c) 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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 physiologically reliable measures of brain function. Here, we develop a novel measure of functional brain age (FBA) using a residual neural network based interpretation of the pediatric EEG. We show that the FBA from a 10 to 15 minute segment of 18-channel EEG during light sleep (stages 1 and 2) in typically developing children and adolescents was strongly associated with chronological age (R 2 = 0.96, 95%CI: 0.94 - 0.96, n = 1062, age range: 1 month to 18 years). The mean absolute error (MAE) between FBA and age was 0.6 years ( n = 1062), with an MAE of 2.1 years following validation on an independent set of EEG recordings ( n = 723). The FBA detected group level maturational delays in a small cohort of children with abnormal neurodevelopment ( p = 0.00053, n = 40). Our work offers a practical, scalable and powerful automated tool for tracking maturation of brain function throughout childhood with an accuracy comparable to that of widely used physical growth charts.

To assess whether computational electroencephalogram (EEG) measures during the first day of life correlate to clinical outcomes in infants with perinatal asphyxia with or without hypoxic-ischemic encephalopathy (HIE). We analyzed four-channel EEG monitoring data from 91 newborn infants after perinatal asphyxia. Altogether 42 automatically computed amplitude- and synchrony-related EEG features were extracted as 2-hourly average at very early (6 h) and early (24 h) postnatal age; they were correlated to the severity of HIE in all infants, and to four clinical outcomes available in a subcohort of 40 newborns: time to full oral feeding (nasogastric tube NGT), neonatal brain MRI, Hammersmith Infant Neurological Examination (HINE) at three months, and Griffiths Scales at two years. At 6 h, altogether 14 (33

Study objectives:To develop a non-invasive and practical wearable method for long-term tracking of infants' sleep. Methods:An infant wearable, NAPping PAnts (NAPPA), was constructed by combining a diaper cover and a movement sensor (triaxial accelerometer and gyroscope), allowing either real-time data streaming to mobile devices or offline feature computation stored in the sensor memory. A sleep state classifier (wake, N1/REM, N2/N3) was trained and tested for NAPPA recordings (N = 16649 epochs of 30 s), using hypnograms from co-registered polysomnography (PSG) as a training target in 33 infants (age 2 weeks to 18 months; Mean = 4). User experience was assessed from an additional group of 16 parents. Results:Overnight NAPPA recordings were successfully performed in all infants. The sleep state classifier showed good overall accuracy (78 %; Range 74-83 %) when using a combination of five features related to movement and respiration. Sleep depth trends were generated from the classifier outputs to visualise sleep state fluctuations, which closely aligned with PSG-derived hypnograms in all infants. Consistently positive parental feedback affirmed the effectiveness of the NAPPA-design. Conclusions:NAPPA offers a practical and feasible method for out-of-hospital assessment of infants' sleep behaviour. It can directly support large-scale quantitative studies and development of new paradigms in scientific research and infant healthcare. Moreover, NAPPA provides accurate and informative computational measures for body positions, respiration rates, and activity levels, each with their respective clinical and behavioural value.

Background Automated computational measures of EEG have the potential for large-scale application. We hypothesised that a predefined measure of early EEG-burst shape (increased burst sharpness) could predict neurodevelopmental impairment (NDI) and mental developmental index (MDI) at 2 years of age over-and-above that of brain ultrasound. Methods We carried out a secondary analysis of data from extremely preterm infants collected for an RCT (SafeBoosC-II). Two hours of single-channel cross-brain EEG was used to analyse burst sharpness with an automated algorithm. The co-primary outcomes were moderate-or-severe NDI and MDI. Complete data were available from 58 infants. A predefined statistical analysis was adjusted for GA, sex and no, mild–moderate, and severe brain injury as detected by cranial ultrasound. Results Nine infants had moderate-or-severe NDI and the mean MDI was 87 ± 17.3 SD. The typical burst sharpness was low (negative values) and varied relatively little (mean –0.81 ± 0.11 SD), but the odds ratio for NDI was increased by 3.8 ( p = 0.008) and the MDI was reduced by –3.2 points ( p = 0.14) per 0.1 burst sharpness units increase (+1 SD) in the adjusted analysis. Conclusion This study confirms the association between EEG-burst measures in preterm infants and neurodevelopment in childhood. Importantly, this was by a priori defined analysis. Impact A fully automated, computational measure of EEG in the first week of life was predictive of neurodevelopmental impairment at 2 years of age. This confirms many previous studies using expert reading of EEG. Only single-channel EEG data were used, adding to the applicability. EEG was recorded by several different devices thus this measure appears to be robust to differences in electrodes, amplifiers and filters. The likelihood ratio of a positive EEG test, however, was only about 2, suggesting little immediate clinical value.

Neonatal brain monitoring in the neonatal intensive care units (NICU) requires a continuous review of the spontaneous cortical activity, i.e., the electroencephalograph (EEG) background activity. This needs development of bedside methods for an automated assessment of the EEG background activity. In this paper, we present development of the key components of a neonatal EEG background classifier, starting from the visual background scoring to classifier design, and finally to possible bedside visualization of the classifier results. A dataset with 13,200 5-minute EEG epochs (8–16 channels) from 27 infants with birth asphyxia was used for classifier training after scoring by two independent experts. We tested three classifier designs based on 98 computational features, and their performance was assessed with respect to scoring system, pre- and post-processing of labels and outputs, choice of channels, and visualization in monitor displays. The optimal solution achieved an overall classification accuracy of 97% with a range across subjects of 81–100%. We identified a set of 23 features that make the classifier highly robust to the choice of channels and missing data due to artefact rejection. Our results showed that an automated bedside classifier of EEG background is achievable, and we publish the full classifier algorithm to allow further clinical replication and validation studies.

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). Results showed that age predictors based on handcrafted EEG features consistently outperformed a generic set of time series features. These findings suggest that optimization of brain age estimation in children benefits from careful preselection of EEG features that are related to age and neurodevelopmental trajectory. This approach shows potential for clinical translation in the future.Clinical Relevance—Handcrafted EEG features provide an accurate functional neurodevelopmental biomarker that tracks brain function maturity in children.

The measurement of heart rate variability (HRV) in preterm infants provides important information on function to clinicians. Measuring the underlying electrocardiogram (ECG) in the neonatal intensive care unit is a challenge and there is a trade off between extracting accurate measurements of the HRV and the amount of ECG processed due to contamination. Knowledge on the effects of 1) quantization in the time domain and 2) missing data on the calculation of HRV features will inform clinical implementation. In this paper, we studied multiple 5 minute epochs from 148 ECG recordings on 56 extremely preterm infants. We found that temporal adjustment of NN peaks improves the estimate of the NN interval resulting in HRV features (m = 9) that are better correlated with age (median percentage increase in correlation of individual features: 0.2%, IQR: 0.0 to 5.6%; correlation with age predictor and age from 0.721 to 0.787). Improved (sub-sample) quantization of the NN intervals (via interpolation) reduced the overall value of HRV features (median percentage reduction in feature value: -1.3%, IQR: -18.8 to 0.0; m = 9), primarily through a reduction in the energy of high-frequency oscillations. HRV features were also robust to missing data, with measures such as mean NN, fractal dimension and the smoothed nonlinear energy operator (SNEO) less susceptible to missing data than features such as VLF, LF, and HF. Furthermore, age predictions derived from a combination of HRV measures were more robust to missing data than individual HRV measures.Clinical Relevance—Poor quantization in time when estimating the NN peak and the presence of missing data confound HRV measures, particularly spectral measures.

Objective. To overcome the effects of site differences in EEG-based brain age prediction in preterm infants. Approach. We used a 'bag of features' with a combination function estimated using support vector regression (SVR) and feature selection (filter then wrapper) to predict post-menstrual age (PMA). The SVR was trained on a dataset containing 138 EEG recordings from 37 preterm infants (site 1). A separate set of 36 EEG recordings from 36 preterm infants was used to validate the age predictor (site 2). The feature distributions were compared between sites and a restricted feature set was constructed using only features that were not significantly different between sites. The mean absolute error between predicted age and PMA was used to define the accuracy of prediction and successful validation was defined as no significant differences in error between site 1 (cross-validation) and site 2.Main results. The age predictor based on all features and trained on site 1 was not validated on site 2 (p < 0.001; MAE site 1 = 1.0 weeks, n = 59 versus MAE site 2 = 2.1 weeks, n = 36). The MAE was improved by training on a restricted features set (MAE site 1 = 1.0 weeks, n = 59 versus MAE site 2 = 1.1 weeks, n = 36), resulting in a validated age predictor when applied to site 2 (p = 0.68). The features selected from the restricted feature set when training on site 1 closely aligned with features selected when trained on a combination of data from site 1 and site 2. Significance. The ability of EEG classifiers, such as brain age prediction, to maintain accuracy on data collected at other sites may be challenged by unexpected, site-dependent differences in EEG signals. Permitting a small amount of data leakage between sites improves generalization, leading towards universal methods of EEG interpretation in preterm infants.

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. This raised the question of whether settings with low naturally derived immunity, such as Queensland where less than 0.005% of the population is known to have been infected in 2020, could have achieved herd immunity against 2021’s variants of concern. To address this question, we used the agent-based model Covasim. We simulated outbreak scenarios (with the Alpha, Delta and Omicron variants) and assumed ongoing interventions (testing, tracing, isolation and quarantine). We modelled vaccination using two approaches with different levels of realism. Hesitancy was modelled using Australian survey data. We found that with a vaccine effectiveness against infection of 80%, it was possible to control outbreaks of Alpha, but not Delta or Omicron. With 90% effectiveness, Delta outbreaks may have been preventable, but not Omicron outbreaks. We also estimated that a decrease in hesitancy from 20% to 14% reduced the number of infections, hospitalizations and deaths by over 30%. Overall, we demonstrate that while herd immunity may not be attainable, modest reductions in hesitancy and increases in vaccine uptake may greatly improve health outcomes. This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.

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). Our results identify several large-scale, frequency-specific network effects of FNI, most extensively in the alpha frequency in fronto-central cortical regions. The connectivity strength in this network was correlated to later neurocognitive performance, and it was comparable to healthy term-born infants rather than the infants receiving standard care. These findings suggest that preterm neurodevelopmental care can be improved by a biologically driven environmental enrichment, such as early facilitation of direct human connection.

Objective: Sharing medical data between institutions is difficult in practice due to data protection laws and official procedures within institutions. Therefore, most existing algorithms are trained on relatively small electroencephalogram (EEG) data sets which is likely to be detrimental to prediction accuracy. In this work, we simulate a case when the data can not be shared by splitting the publicly available data set into disjoint sets representing data in individual institutions. Methods and procedures: We propose to train a (local) detector in each institution and aggregate their individual predictions into one final prediction. Four aggregation schemes are compared, namely, the majority vote, the mean, the weighted mean and the Dawid-Skene method. The method was validated on an independent data set using only a subset of EEG channels. Results: The ensemble reaches accuracy comparable to a single detector trained on all the data when sufficient amount of data is available in each institution. Conclusion: The weighted mean aggregation scheme showed best performance, it was only marginally outperformed by the Dawid–Skene method when local detectors approach performance of a single detector trained on all available data. Clinical impact: Ensemble learning allows training of reliable algorithms for neonatal EEG analysis without a need to share the potentially sensitive EEG data between institutions.

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)). Supervised machine learning was used to generate a functional autonomic age (FAA), by combining 50 computed HRV features from successive 5-minute ECG epochs (median of 23 epochs per infant). Performance of the FAA was assessed by correlation to PMA, clinical outcomes and the infant’s functional brain age (FBA), an index of maturation derived from the electroencephalogram. Results The FAA was strongly correlated to PMA ( r = 0.86, 95% CI: 0.83–0.93) with a mean absolute error (MAE) of 1.66 weeks and also accurately estimated FBA (MAE = 1.58 weeks, n = 54 infants). The relationship between PMA and FAA was not confounded by neurodevelopmental outcome ( p = 0.18 , n = 45), sex ( p = 0.88 , n = 56), patent ductus arteriosus ( p = 0.08 , n = 56), IVH ( p = 0.63 , n = 56) or body weight at birth ( p = 0.95 , n = 56). Conclusions The FAA, an index derived from the ubiquitous ECG signal, offers direct avenues towards estimating autonomic maturation at the bedside during intensive care monitoring. Impact The development of a tool to track functional autonomic age in preterm infants based on heart rate variability features in the electrocardiogram provides a rapid and specialized view of autonomic maturation at the bedside. Functional autonomic age is linked closely to postmenstrual age and central nervous system function response, as determined by its relationship to functional brain age from the electroencephalogram. Tracking functional autonomic age during neonatal intensive care unit monitoring offers a unique insight into cardiovascular health in infants born extremely preterm and their maturational trajectories to term age.