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. In this cross-sectional study, we included 1056 children with typical development ranging in age from 1 month to 18 years. We analysed a 10- to 15-min segment of 18-channel EEG recorded during light sleep (N1 and N2 states).

FINDINGS

The FBA had a weighted mean absolute error (wMAE) of 0.85 years (95% CI: 0.69-1.02; n = 1056). A two-channel version of the FBA had a wMAE of 1.51 years (95% CI: 1.30-1.73; n = 1056) and was validated on an independent set of EEG recordings (wMAE = 2.27 years, 95% CI: 1.90-2.65; n = 723). Group-level maturational delays were also detected in a small cohort of children with Trisomy 21 (Cohen's d = 0.36, p = 0.028).

INTERPRETATION

A FBA, based on EEG, is an accurate, practical and scalable automated tool to track brain function maturation throughout childhood with accuracy comparable to widely used physical growth charts.

FUNDING

This research was supported by the National Health and Medical Research Council, Australia, Helsinki University Diagnostic Center Research Funds, Finnish Academy, Finnish Paediatric Foundation, and Sigrid Juselius Foundation.

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. 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.

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

OBJECTIVE

To assess and overcome the effects of site differences in EEG -based brain age prediction in preterm infants. &#xD;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 training used 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. Successful validation was defined as no significant differences in error between site 1 (cross-validation) and site 2.&#xD;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 vs 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 vs MAE site 2 = 1.1 weeks, n = 36), resulting in a validated age predictor (p = 0.68). The selected features closely aligned with features selected when trained on a combination of data from site 1 and site 2.&#xD;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.&#xD.

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). 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.

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.

METHODS

In this method-development and proof-of-concept study, we collected visually classified EEGs from infants recovering from birth asphyxia or stroke. We used unsupervised learning methods to explore latent EEG characteristics, which guided the supervised training of a deep learning-based classifier. We assessed the classifier performance using cross-validation and an external validation dataset. We constructed a novel measure of cortical function, brain state of the newborn (BSN), from the novel EEG background classifier and a previously published sleep-state classifier. We estimated clinical utility of the BSN by identification of two key items in newborn brain monitoring, the onset of continuous cortical activity and sleep-wake cycling, compared with the visual interpretation of the raw EEG signal and the amplitude-integrated (aEEG) trend.

FINDINGS

We collected 2561 h of EEG from 39 infants (gestational age 35·0-42·1 weeks; postnatal age 0-7 days). The external validation dataset included 105 h of EEG from 31 full-term infants. The overall accuracy of the EEG background classifier was 92% in the whole cohort (95% CI 91-96; range 85-100 for individual infants). BSN trend values were closely related to the onset of continuous EEG activity or sleep-wake cycling, and BSN levels showed robust difference between aEEG categories. The temporal evolution of the BSN trends showed early diverging trajectories in infants with severely abnormal outcomes.

INTERPRETATION

The BSN trend can be implemented in bedside patient monitors as an EEG interpretation that is intuitive, transparent, and clinically explainable. A quantitative trend measure of brain function might harmonise practices across medical centres, enable wider use of brain monitoring in neurocritical care, and might facilitate clinical intervention trials.

FUNDING

European Training Networks Funding Scheme, the Academy of Finland, Finnish Pediatric Foundation (Lastentautiensäätiö), Aivosäätiö, Sigrid Juselius Foundation, HUS Children's Hospital, HUS Diagnostic Center, National Health and Medical Research Council of Australia.

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. In addition, we constructed Sleep State Trend (SST), a bedside-ready means for visualizing classifier outputs.

RESULTS

The accuracy of quiet sleep detection in the training data was 90%, and the accuracy was comparable (85-86 %) in all bipolar derivations available from the 4-electrode recordings. The algorithm generalized well to a polysomnography dataset, showing 81% overall accuracy despite different signal derivations. SST allowed an intuitive, clear visualization of the classifier output.

CONCLUSIONS

Fluctuations in sleep states can be detected at high fidelity from a single EEG channel, and the results can be visualized as a transparent and intuitive trend in the bedside monitors.

SIGNIFICANCE

The Sleep State Trend (SST) may provide caregivers and clinical studies a real-time view of sleep state fluctuations and its cyclicity.

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. This raised the question of whether settings with low naturally derived immunity, such as Queensland where less than [Formula: see text] 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'.

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). 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.

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”. We also examined how the threat of new variants reduces given a range of vaccination levels. Specifically, the model calibration covered the first-wave period from early March 2020 to May 2020. Predicted epidemic trajectories were simulated from early February 2021 to late March 2021. Our simulations showed that one infected agent with the ancestral (A.2.2) variant has a 14% chance of crossing a threshold of sustained community transmission (SCT) (i.e., > 5 infections per day, more than 3 days in a row), assuming no change in the prevailing preventative and counteracting policies. However, one agent carrying the alpha (B.1.1.7) variant has a 43% chance of crossing the same threshold; a threefold increase with respect to the ancestral strain; while, one agent carrying the delta (B.1.617.2) variant has a 60% chance of the same threshold, a fourfold increase with respect to the ancestral strain. The delta variant is 50% more likely to trigger SCT than the alpha variant. Doubling the average number of daily tests from ∼ 6,000 to 12,000 results in a decrease of this SCT probability from 43 to 33% for the alpha variant. However, if the delta variant is circulating we would need an average of 100,000 daily tests to achieve a similar decrease in SCT risk. Further, achieving a full-vaccination coverage of 70% of the adult population, with a vaccine with 70% effectiveness against infection, would decrease the probability of SCT from a single seed of alpha from 43 to 20%, on par with the ancestral strain in a naive population. In contrast, for the same vaccine coverage and same effectiveness, the probability of SCT from a single seed of delta would decrease from 62 to 48%, a risk slightly above the alpha variant in a naive population. Our results demonstrate that the introduction of even a small number of people infected with high-transmission variants dramatically increases the probability of sustained community transmission in Queensland. Until very high vaccine coverage is achieved, a swift implementation of policies and interventions, together with high quarantine adherence rates, will be required to minimise the probability of sustained community transmission.

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. In this review, we analyse the evidence base on the use of EEG for the assessment of neonates with FGR or SGA. We found consistent findings that FGR/SGA is associated with measurable changes in the EEG that present immediately after birth and persist into childhood. Early manifestations of FGR/SGA in the EEG include changes in spectral power, symmetry/synchrony, sleep-wake cycling, and the continuity of EEG amplitude. Later manifestations of FGR/SGA into infancy and early childhood include changes in spectral power, sleep architecture, and EEG amplitude. FGR/SGA infants had poorer neurodevelopmental outcomes than appropriate for gestational age controls. The EEG has the potential to identify FGR/SGA infants and assess the functional correlates of neurological damage. IMPACT: FGR/SGA neonates have significantly different EEG activity compared to AGA neonates. EEG differences persist into childhood and are associated with adverse neurodevelopmental outcomes. EEG has the potential for early identification of brain impairment 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. Artefact types included: device interference, EMG, movement, electrode pop, and non-cortical biological rhythms. Performance was assessed by prediction statistics and further validated on a separate independent dataset of 13 term infants (143 h of 3-channel recording). EEG pre-processing steps, and other post-processing steps such as averaging probability over a temporal window, were also included in the algorithm.

RESULTS

The Residual Deep Neural Network showed high accuracy (95%) when distinguishing periods of clean, artefact-free EEG from any kind of artefact, with a median accuracy for individual patient of 91% (IQR: 81%-96%). The accuracy in identifying the five different types of artefacts ranged from 57%-92%, with electrode pop being the hardest to detect and EMG being the easiest. This reflected the proportion of artefact available in the training dataset. Misclassification as clean was low for each artefact type, ranging from 1%-11%. The detection accuracy was lower on the validation set (87%). We used the algorithm to show that EEG channels located near the vertex were the least susceptible to artefact.

CONCLUSION

Artefacts can be accurately and reliably identified in the neonatal EEG using a deep learning algorithm. Artefact detection algorithms can provide continuous bedside quality assessment and support EEG review by clinicians or analysis algorithms.

The interplay between the immune system and viruses

The human immune response can be divided into two arms: innate and adaptive immunity. The innate immune system consists of "hard-wired" responses encoded by host germline genes. In contrast, the adaptive response consists of gene elements that are somatically rearranged to assemble antigen-binding molecules with specificity for individual foreign structures. In contrast to the adaptive immune system, which depends upon T and B lymphocytes, innate immune protection is a task performed by cells of both hematopoietic and non-hematopoietic origin. Hematopoietic cells involved in innate immune responses include macrophages, dendritic cells, mast cell, neutrophils, eosinophils, natural killer (NK) cells and natural killer T cells. The induction of an adaptive immune response begins when a pathogen is ingested by an Antigen Presenting Cell (APC), such as the Dendritic cell (DC), in the infected tissue. DCs bridge the gap between first line innate responses and powerful adaptive immune responses, by internalizing, processing and presenting antigens on Major Histocompatibility Complex (MHC) and MHC-like molecules to the adaptive immune cells In addition to DCs, macrophages and B cells are deemed antigen presenting cells (Llewelyn & Cohen, 2002).

Building an Open Source Classifier for the Neonatal EEG Background: A Systematic Feature-Based Approach From Expert Scoring to Clinical Visualization

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.

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. Three classifier designs were evaluated in the detection of deep sleep (N3 state): support vector machine (SVM), Long Short-Term Memory neural network, and convolutional neural network (CNN).

Results

Deep sleep was accurately identified from other states with all classifier variants. The SVM classifier based on a combination of movement and electrocardiography features had the highest performance (AUC 97.6%). A SVM classifier based on only movement features had comparable accuracy (AUC 95.0%). The feature-independent CNN resulted in roughly comparable accuracy (AUC 93.3%).

Conclusion

Automated non-invasive tracking of sleep state cycling is technically feasible using measurements from a piezo element situated under a bed mattress.

Significance

An open source infant deep sleep detector of this kind allows quantitative, continuous bedside assessment of infant’s sleep cycling.

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. Error analysis was used to determine the accuracy of PMA assessments and intraclass correlation (ICC) was used to assess agreement between experts.

RESULTS

EEG recordings from a PMA range 25 to 38 weeks were successfully interpreted. In 179 recordings from 62 infants interpreted by all human readers, there was moderate agreement between experts (aEEG ICC = 0.724; 95%CI:0.658-0.781 and EEG ICC = 0.517; 95%CI:0.311-0.664). In 149 recordings from 61 infants interpreted by all human readers and the FBA algorithm, random and systematic errors in visual interpretation of PMA were significantly higher than the computational FBA estimate. Tracking of maturation in individual infants showed stable FBA trajectories, but the trajectories of the experts' PMA estimate were more likely to be obscured by random errors. The accuracy of visual interpretation of PMA estimation was compromised by neurodevelopmental outcome for both aEEG and EEG review.

INTERPRETATION

Visual assessment of infant maturity is possible from the EEG or aEEG, with an average of human experts providing the highest accuracy. Tracking PMA of individual infants was hampered by errors in experts' estimates. FBA provided the most accurate maturity assessment and has potential as a biomarker of early outcome.

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).

Methods

We used a dataset of 177 EEG recordings from 65 preterm infants to train a multivariable prediction of functional brain age (FBA) from EEG. The FBA was validated on an independent set of 99 EEG recordings from 42 preterm infants. The difference between FBA and postmenstrual age (PMA) was evaluated as a predictor for neurodevelopmental outcome.

Results

The FBA correlated strongly with the PMA of an infant, with a median prediction error of less than 1 week. Moreover, individual babies follow well‐defined individual trajectories. The accuracy of the FBA applied to the validation set was statistically equivalent to the training set accuracy. In a subgroup of infants with repeated EEG recordings, a persistently negative predicted age difference was associated with poor neurodevelopmental outcome.

Interpretation

The FBA enables the tracking of functional neurodevelopment in preterm infants. This establishes proof of principle for growth charts for brain function, a new tool to assist clinical management and identify infants who will benefit most from early intervention.

Time-Varying EEG Correlations Improve Automated Neonatal Seizure Detection

The aim of this study was to develop methods for detecting the nonstationary periodic characteristics of neonatal electroencephalographic (EEG) seizures by adapting estimates of the correlation both in the time (spike correlation; SC) and time–frequency domain (time–frequency correlation; TFC). These measures were incorporated into a seizure detection algorithm (SDA) based on a support vector machine to detect periods of seizure and nonseizure. The performance of these nonstationary correlation measures was evaluated using EEG recordings from 79 term neonates annotated by three human experts. The proposed measures were highly discriminative for seizure detection (median [Formula: see text]: 0.933 IQR: 0.821–0.975, median [Formula: see text]: 0.883 IQR: 0.707–0.931). The resultant SDA applied to multi-channel recordings had a median AUC of 0.988 (IQR: 0.931–0.998) when compared to consensus annotations, outperformed two state-of-the-art SDAs [Formula: see text] and was noninferior to the human expert for 73/79 of neonates.

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. These infants had favourable neurodevelopment at 2 years of age and were without significant neurological compromise at time of EEG monitoring. Quantitative EEG was generated using features representing EEG power, discontinuity, spectral distribution, and inter-hemispheric connectivity. We found rapid changes in cortical activity over the 3 days distinct from slower changes associated with gestational age: for many features, evolution over 1 day after birth is equivalent to approximately 1 to 2.5 weeks of maturation. Considerable changes in the EEG immediately after birth implies that postnatal adaption significantly influences cerebral activity for early preterm infants. Postnatal age, in addition to gestational age, should be considered when analysing preterm EEG within the first few days after birth.

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 large individual variability in seizure occurrence over time and between etiologies challenges group comparisons, while the absence of clinical signs mandates quantification of seizure occurrence with continuous multi-channel EEG monitoring. A biologically sound approach that views neonatal seizures as a functional cot-side biomarker rather than an object to treat can overcome these challenges.

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

•

Machine learning approach enables accurate detection of bursts in preterm EEG.

•

Features of amplitude and spectral shape capture discriminating information.

•

Improves reliability of estimates of inter-burst intervals.

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. Using a consensus annotation, feature selection removed redundant features and a support vector machine combined features. Area under the receiver operator characteristic (AUC) and Cohen’s kappa (κ) evaluated performance within a cross-validation procedure. Results: The proposed channel-independent method improves AUC by 4–5% over existing methods (p < 0.001, n=36), with median (95% confidence interval) AUC of 0.989 (0.973–0.997) and sensitivity–specificity of 95.8–94.4%. Agreement rates between the detector and experts’ annotations, κ=0.72 (0.36–0.83) and κ=0.65 (0.32–0.81), are comparable to inter-rater agreement, κ=0.60 (0.21–0.74). Conclusions: Automating the visual identification of bursts in preterm EEG is achievable with a high level of accuracy. Multiple features, combined using a data-driven approach, improves on existing single-feature methods.

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. HIE severity was graded using EEG. HRV features investigated include: mean NN interval (mean NN), standard deviation of NN interval (SDNN), triangular interpolation (TINN), high-frequency (HF), low-frequency (LF), very low-frequency (VLF), and LF/HF ratio. Linear mixed model comparisons were used.

RESULTS

118 infants (pre-TH: n = 44, TH: n = 74) were assessed. The majority of HRV features decreased with increasing EEG grade. Infants with moderate HIE undergoing TH had significantly different HRV features compared with the pre-TH group (HF: P = 0.016, LF/HF ratio: P = 0.006). In the pre-TH group, LF/HF ratio was significantly different between moderate and severe HIE grades (P = 0.002). In the TH group, significant differences were observed between moderate and severe HIE grades for SDNN: P = 0.020, TINN: P = 0.005, VLF: P = 0.029, LF: P = 0.010, and HF: P = 0.006.

CONCLUSION

The HF component of HRV is increased in infants with moderate HIE undergoing TH.

Seizure burden and neurodevelopmental outcome in neonates with hypoxic-ischemic encephalopathy

AIM

To examine the relationship between electrographic seizures and long-term outcome in neonates with hypoxic-ischemic encephalopathy (HIE).

METHOD

Full-term neonates with HIE born in Cork University Maternity Hospital from 2003 to 2006 (pre-hypothermia era) and 2009 to 2012 (hypothermia era) were included in this observational study. All had early continuous electroencephalography monitoring. All electrographic seizures were annotated. The total seizure burden and hourly seizure burden were calculated. Outcome (normal/abnormal) was assessed at 24 to 48 months in surviving neonates using either the Bayley Scales of Infant and Toddler Development, Third Edition or the Griffiths Mental Development Scales; a diagnosis of cerebral palsy or epilepsy was also considered an abnormal outcome.

RESULTS

Continuous electroencephalography was recorded for a median of 57.1 hours (interquartile range 33.5-80.5h) in 47 neonates (31 males, 16 females); 29 out of 47 (62%) had electrographic seizures and 25 out of 47 (53%) had an abnormal outcome. The presence of seizures per se was not associated with abnormal outcome (p=0.126); however, the odds of an abnormal outcome increased over ninefold (odds ratio [OR] 9.56; 95% confidence interval [95% CI] 2.43-37.67) if a neonate had a total seizure burden of more than 40 minutes (p=0.001), and eightfold (OR: 8.00; 95% CI: 2.06-31.07) if a neonate had a maximum hourly seizure burden of more than 13 minutes per hour (p=0.003). Controlling for electrographic HIE grade or treatment with hypothermia did not change the direction of the relationship between seizure burden and outcome.

INTERPRETATION

In HIE, a high electrographic seizure burden is significantly associated with abnormal outcome, independent of HIE severity or treatment with hypothermia.

Artefact detection in neonatal EEG

Artefact detection is an important component of any automated EEG analysis. It is of particular importance in analyses such as sleep state detection and EEG grading where there is no null state. We propose a general artefact detection system (GADS) based on the analysis of the neonatal EEG. This system aims to detect both major and minor artefacts (a distinction based primarily on amplitude). As a result, a two-stage system was constructed based on 14 features extracted from EEG epochs at multiple time scales: [2, 4, 16, 32]s. These features were combined in a support vector machine (SVM) in order to determine the presence of absence of artefact. The performance of the GADS was estimated using a leave-one-out cross-validation applied to a database of hour long recordings from 51 neonates. The median AUC was 1.00 (IQR: 0.95-1.00) for the detection of major artefacts and 0.89 (IQR: 0.83-0.95) for the detection of minor artefacts.

SOCS3 revisited: a broad regulator of disease, now ready for therapeutic use?

Since their discovery, SOCS have been characterised as regulatory cornerstones of intracellular signalling. While classically controlling the JAK/STAT pathway, their inhibitory effects are documented across several cascades, underpinning their essential role in homeostatic maintenance and disease. After 20 years of extensive research, SOCS3 has emerged as arguably the most important family member, through its regulation of both cytokine- and pathogen-induced cascades. In fact, low expression of SOCS3 is associated with autoimmunity and oncogenesis, while high expression is linked to diabetes and pathogenic immune evasion. The induction of SOCS3 by both viruses and bacteria and its impact upon inflammatory disorders, underscores this protein's increasing clinical potential. Therefore, with the aim of highlighting SOCS3 as a therapeutic target for future development, this review revisits its multi-faceted immune regulatory functions and summarises its role in a broad ranges of diseases.

Short-Term Effects of Phenobarbitone on Electrographic Seizures in Neonates

BACKGROUND

Phenobarbitone is the most common first-line anti-seizure drug and is effective in approximately 50% of all neonatal seizures.

OBJECTIVE

To describe the response of electrographic seizures to the administration of intravenous phenobarbitone in neonates using seizure burden analysis techniques.

METHODS

Multi-channel conventional EEG, reviewed by experts, was used to determine the electrographic seizure burden in hourly epochs. The maximum seizure burden evaluated 1 h before each phenobarbitone dose (T-1) was compared to seizure burden in periods of increasing duration after each phenobarbitone dose had been administered (T+1, T+2 to seizure offset). Differences were analysed using linear mixed models and summarized as means and 95% CI.

RESULTS

Nineteen neonates had electrographic seizures and met the inclusion criteria for the study. Thirty-one doses were studied. The maximum seizure burden was significantly reduced 1 h after the administration of phenobarbitone (T+1) [-14.0 min/h (95% CI: -19.6, -8.5); p < 0.001]. The percentage reduction was 74% (IQR: 36-100). This reduction was temporary and not significant within 4 h of administrating phenobarbitone. Subgroup analysis showed that only phenobarbitone doses at 20 mg/kg resulted in a significant reduction in the maximum seizure burden from T-1 to T+1 (p = 0.002).

CONCLUSIONS

Phenobarbitone significantly reduced seizures within 1 h of administration as assessed with continuous multi-channel EEG monitoring in neonates. The reduction was not permanent and seizures were likely to return within 4 h of treatment.

The temporal characteristics of seizures in neonatal hypoxic ischemic encephalopathy treated with hypothermia

PURPOSE

The characteristics of electrographic seizures in newborns with hypoxic-ischaemic encephalopathy (HIE) treated with therapeutic hypothermia (TH) are poorly described. This retrospective, observational study provides reference data on the characteristics of seizures and their evolution over time in newborns with HIE receiving whole-body TH.

METHOD

The cohort under analysis included 23 infants with HIE and seizures defined by multi-channel EEG recordings. Clinical presentation, details of TH and antiepileptic drugs used were recorded. Time from first to last-recorded electrographic seizure (seizure period) was calculated. Temporal characteristics of seizures - total burden, duration, number, burden in minutes per hour, distribution of burden over time (temporal evolution), time from seizure onset to maximum seizure burden (Tmsb), T1, and time from Tmsb to seizure offset, T2 - were analysed.

RESULTS

The median age at electrographic seizure onset was 13.1h (IQR: 11.4 to 22.0). Tmsb was reached at a median age of 19.4 hours (IQR: 12.2 to 29.7). Median seizure period was 16.5h (IQR: 7.0 to 49.7), median number of seizures per hour was 1.9 (IQR: 1.0 to 3.3). The seizure burden was 4.0 min/h (IQR: 2.0 to 7.0). There was no consistent pattern in the temporal evolution of seizures in neonates treated with TH. The skewness was neither positive nor negative (p-value=0.15), there was no difference between the duration of T1 and T2 (p-value=0.09) and no difference in the seizure burden between T1 and T2 (p=0.09). There was an association between Tmsb and Phenobarbital (PB) administration (r=0.76, p-value<0.001).

CONCLUSION

There is no consistent temporal evolution of seizure burden in neonates treated with TH. Seizures are diffuse, and their characteristics are variable.

Robustness of time frequency distribution based features for automated neonatal EEG seizure detection

In this paper we examined the robustness of a feature-set based on time-frequency distributions (TFDs) for neonatal EEG seizure detection. This feature-set was originally proposed in literature for neonatal seizure detection using a support vector machine (SVM). We tested the performance of this feature-set with a smoothed Wigner-Ville distribution and modified B distribution as the underlying TFDs. The seizure detection system using time-frequency signal and image processing features from the TFD of the EEG signal using modified B distribution was able to achieve a median receiver operator characteristic area of 0.96 (IQR 0.91-0.98) tested on a large clinical dataset of 826 h of EEG data from 18 full-term newborns with 1389 seizures. The mean AUC was 0.93.

Interobserver agreement for neonatal seizure detection using multichannel EEG

Objective

To determine the interobserver agreement (IOA) of neonatal seizure detection using the gold standard of conventional, multichannel EEG.

Methods

A cohort of full‐term neonates at risk of acute encephalopathy was included in this prospective study. The EEG recordings of these neonates were independently reviewed for seizures by three international experts. The IOA was estimated using statistical measures including Fleiss' kappa and percentage agreement assessed over seizure events (event basis) and seizure duration (temporal basis).

Results

A total of 4066 h of EEG recordings from 70 neonates were reviewed with an average of 2555 seizures detected. The IOA was high with temporal assessment resulting in a kappa of 0.827 (95% CI: 0.769–0.865; n = 70). The median agreement was 83.0% (interquartile range [IQR]: 76.6–89.5%; n = 33) for seizure and 99.7% (IQR: 98.9–99.8%; n = 70) for nonseizure EEG. Analysis of events showed a median agreement of 83.0% (IQR: 72.9–86.6%; n = 33) for seizures with 0.018 disagreements per hour (IQR: 0.000–0.090 per hour; n = 70). Observers were more likely to disagree when a seizure was less than 30 sec. Overall, 33 neonates were diagnosed with seizures and 28 neonates were not, by all three observers. Of the remaining nine neonates with contradictory EEG detections, seven presented with low total seizure burden.

Interpretation

The IOA is high among experts for the detection of neonatal seizures using conventional, multichannel EEG. Agreement is reduced when seizures are rare or have short duration. These findings support EEG‐based decision making in the neonatal intensive care unit, inform EEG interpretation guidelines, and provide benchmarks for seizure detection algorithms.

Validation of an automated seizure detection algorithm for term neonates

OBJECTIVE

The objective of this study was to validate the performance of a seizure detection algorithm (SDA) developed by our group, on previously unseen, prolonged, unedited EEG recordings from 70 babies from 2 centres.

METHODS

EEGs of 70 babies (35 seizure, 35 non-seizure) were annotated for seizures by experts as the gold standard. The SDA was tested on the EEGs at a range of sensitivity settings. Annotations from the expert and SDA were compared using event and epoch based metrics. The effect of seizure duration on SDA performance was also analysed.

RESULTS

Between sensitivity settings of 0.5 and 0.3, the algorithm achieved seizure detection rates of 52.6-75.0%, with false detection (FD) rates of 0.04-0.36FD/h for event based analysis, which was deemed to be acceptable in a clinical environment. Time based comparison of expert and SDA annotations using Cohen's Kappa Index revealed a best performing SDA threshold of 0.4 (Kappa 0.630). The SDA showed improved detection performance with longer seizures.

CONCLUSION

The SDA achieved promising performance and warrants further testing in a live clinical evaluation.

SIGNIFICANCE

The SDA has the potential to improve seizure detection and provide a robust tool for comparing treatment regimens.

Neonatal seizure detection using atomic decomposition with a novel dictionary

Atomic decomposition (AD) can be used to efficiently decompose an arbitrary signal. In this paper, we present a method to detect neonatal electroencephalogram (EEG) seizure based on AD via orthogonal matching pursuit using a novel, application-specific, dictionary. The dictionary consists of pseudoperiodic Duffing oscillator atoms which are designed to be coherent with the seizure epochs. The relative structural complexity (a measure of the rate of convergence of AD) is used as the sole feature for seizure detection. The proposed feature was tested on a large clinical dataset of 826 h of EEG data from 18 full-term newborns with 1389 seizures. The seizure detection system using the proposed dictionary was able to achieve a median receiver operator characteristic area of 0.91 (IQR 0.87-0.95) across 18 neonates.

Automated analysis of multi-channel EEG in preterm infants

OBJECTIVE

To develop and validate two automatic methods for the detection of burst and interburst periods in preterm eight-channel electroencephalographs (EEG). To perform a detailed analysis of interobserver agreement on burst and interburst periods and use this as a benchmark for the performance of the automatic methods. To examine mathematical features of the EEG signal and their potential correlation with gestational age.

METHODS

Multi-channel EEG from 36 infants, born at less than 30 weeks gestation was utilised, with a 10 min artifact-free epoch selected for each subject. Three independent expert observers annotated all EEG activity bursts in the dataset. Two automatic algorithms for burst/interburst detection were applied to the EEG data and their performances were analysed and compared with interobserver agreement. A total of 12 mathematical features of the EEG signal were calculated and correlated with gestational age.

RESULTS

The mean interobserver agreement was found to be 77% while mean algorithm/observer agreement was 81%. Six of the mathematical features calculated (spectral entropy, Higuchi fractal dimension, spectral edge frequency, variance, extrema median and Hilberts transform amplitude) were found to have significant correlation with gestational age.

CONCLUSIONS

Automatic detection of burst/interburst periods has been performed in multi-channel EEG of 36 preterm infants. The algorithm agreement with expert observers is found to be on a par with interobserver agreement. Mathematical features of EEG have been calculated which show significant correlation with gestational age.

SIGNIFICANCE

Automatic analysis of preterm multi-channel EEG is possible. The methods described here have the potential to be incorporated into a fully automatic system to quantitatively assess brain maturity from preterm EEG.

Early postnatal EEG features of perinatal arterial ischaemic stroke with seizures

Background

Stroke is the second most common cause of seizures in term neonates and is associated with abnormal long-term neurodevelopmental outcome in some cases.

Objective

To aid diagnosis earlier in the postnatal period, our aim was to describe the characteristic EEG patterns in term neonates with perinatal arterial ischaemic stroke (PAIS) seizures.

Design

Retrospective observational study.

Patients

Neonates >37 weeks born between 2003 and 2011 in two hospitals.

Method

Continuous multichannel video-EEG was used to analyze the background patterns and characteristics of seizures. Each EEG was assessed for continuity, symmetry, characteristic features and sleep cycling; morphology of electrographic seizures was also examined. Each seizure was categorized as electrographic-only or electroclinical; the percentage of seizure events for each seizure type was also summarized.

Results

Nine neonates with PAIS seizures and EEG monitoring were identified. While EEG continuity was present in all cases, the background pattern showed suppression over the infarcted side; this was quite marked (>50% amplitude reduction) when the lesion was large. Characteristic unilateral bursts of theta activity with sharp or spike waves intermixed were seen in all cases. Sleep cycling was generally present but was more disturbed over the infarcted side. Seizures demonstrated a characteristic pattern; focal sharp waves/spike-polyspikes were seen at frequency of 1–2 Hz and phase reversal over the central region was common. Electrographic-only seizure events were more frequent compared to electroclinical seizure events (78 vs 22%).

Conclusions

Focal electrographic and electroclinical seizures with ipsilateral suppression of the background activity and focal sharp waves are strong indicators of PAIS. Approximately 80% of seizure events were the result of clinically unsuspected seizures in neonates with PAIS. Prolonged and continuous multichannel video-EEG monitoring is advocated for adequate seizure surveillance.

Measuring brain activity cycling (BAC) in long term EEG monitoring of preterm babies

Measuring fluctuation of vigilance states in early preterm infants undergoing long term intensive care holds promise for monitoring their neurological well-being. There is currently, however, neither objective nor quantitative methods available for this purpose in a research or clinical environment. The aim of this proof-of-concept study was, therefore, to develop quantitative measures of the fluctuation in vigilance states or brain activity cycling (BAC) in early preterm infants. The proposed measures of BAC were summary statistics computed on a frequency domain representation of the proportional duration of spontaneous activity transients (SAT%) calculated from electroencephalograph (EEG) recordings. Eighteen combinations of three statistics and six frequency domain representations were compared to a visual interpretation of cycling in the SAT% signal. Three high performing measures (band energy/periodogram: R = 0.809, relative band energy/nonstationary frequency marginal: R = 0.711, g-statistic/nonstationary frequency marginal: R = 0.638) were then compared to a grading of sleep wake cycling based on the visual interpretation of the amplitude-integrated EEG trend. These measures of BAC are conceptually straightforward, correlate well with the visual scores of BAC and sleep wake cycling, are robust enough to cope with the technically compromised monitoring data available in intensive care units, and are recommended for further validation in prospective studies.

Monitoring neonatal seizures

Neonatal seizures are a neurological emergency and prompt treatment is required. Seizure burden in neonates can be very high, status epilepticus a frequent occurrence, and the majority of seizures do not have any clinical correlate. Detection of neonatal seizures is only possible with continuous electroencephalogram (EEG) monitoring. EEG interpretation requires special expertise that is not available in most neonatal intensive care units (NICUs). As a result, a simplified method of EEG recording incorporating an easy-to-interpret compressed trend of the EEG output (amplitude integrated EEG) from one of the EEG output from one or two channels has emerged as a popular way to monitor neurological function in the NICU. This is not without limitations; short duration and low amplitude seizures can be missed, artefacts are problematic and may mimic seizure-like activity and only a restricted area of the brain is monitored. Continuous multichannel EEG is the gold standard for detecting seizures and monitoring response to therapy but expert interpretation of the EEG output is generally not available. Some centres have set up remote access for neurophysiologists to the cot-side EEG, but reliable interpretation is wholly dependent on the 24 h availability of experts, an expensive solution. A more practical solution for the NICU without such expertise is an automated seizure detection system. This review outlines the current state of the art regarding cot-side monitoring of neonatal seizures in the NICU.

An automated system for grading EEG abnormality in term neonates with hypoxic-ischaemic encephalopathy

Automated analysis of the neonatal EEG has the potential to assist clinical decision making for neonates with hypoxic-ischaemic encephalopathy. This paper proposes a method of automatically grading the degree of abnormality in an hour long epoch of neonatal EEG. The automated grading system (AGS) was based on a multi-class linear classifier grading of short-term epochs of EEG which were converted into a long-term grading of EEG using a majority vote operation. The features used in the AGS were summary measurements of two sub-signals extracted from a quadratic time-frequency distribution: the amplitude modulation and instantaneous frequency. These sub-signals were based on a model of EEG as a multiplication of a coloured random process with a slowly varying pseudo-periodic waveform and may be related to macroscopic neurophysiological function. The 4 grade AGS had a classification accuracy of 83% compared to human annotation of the EEG (level of agreement, κ = 0.76). Features estimated on the developed sub-signals proved more effective at grading the EEG than measures based solely on the EEG and the incorporation of additional sub-grades based on EEG states into the AGS also improved performance.

Cooling and seizure burden in term neonates: an observational study

OBJECTIVE

To investigate any possible effect of cooling on seizure burden, the authors quantified the recorded electrographic seizure burden based on multichannel video-EEG recordings in term neonates with hypoxic-ischaemic encephalopathy (HIE) who received cooling and in those who did not.

STUDY DESIGN

Retrospective observational study.

PATIENTS

Neonates >37 weeks gestation born between 2003 and 2010 in two hospitals.

METHODS

Off-line analysis of prolonged continuous multichannel video-EEG recordings was performed independently by two experienced encephalographers. Comparison between the recorded electrographic seizure burden in non-cooled and cooled neonates was assessed. Data were treated as non-parametric and expressed as medians with interquartile ranges (IQR).

RESULTS

One hundred and seven neonates with HIE underwent prolonged continuous multichannel EEG monitoring. Thirty-seven neonates had electrographic seizures, of whom 31 had EEG recordings that were suitable for the analysis (16 non-cooled and 15 cooled). Compared with non-cooled neonates, multichannel EEG monitoring commenced at an earlier postnatal age in cooled neonates (6 (3-9) vs 15 (5-20) h)and continued for longer (88 (75-101) vs 55 (41-60) h). Despite this increased opportunity to capture seizures in cooled neonates, the recorded electrographic seizure burden in the cooled group was significantly lower than in the non-cooled group (60 (39-224) vs 203 (141-406) min). Further exploratory analysis showed that the recorded electrographic seizure burden was only significantly reduced in cooled neonates with moderate HIE (49 (26-89) vs 162 (97-262) min).

CONCLUSIONS

A decreased seizure burden was seen in neonates with moderate HIE who received cooling. This finding may explain some of the therapeutic benefits of cooling seen in term neonates with moderate HIE.

Chronic hepatitis C infection blocks the ability of dendritic cells to secrete IFN-α and stimulate T-cell proliferation

Dendritic cells (DCs) are likely to play a key role in the compromised T-cell function associated with hepatitis C Virus (HCV) infection. However, studies of DC function in HCV-infected patients to date have yielded conflicting findings possibly because of patient and virus heterogeneity. Here, we report the characterization of monocyte-derived DCs obtained from a homogenous cohort of women who were infected with HCV genotype 1b following exposure to contaminated anti-D immunoglobulin from a single donor source. Patients included in the study had not received anti-viral therapy and all had mild liver disease. We show that phenotypically normal monocyte-derived dendritic cells (MDDCs) (CD11c(+) HLA(-) DR(+) CD1a(+) CD14(lo) ) can be obtained from these patients. These cells respond to both Poly(I:C) and LPS, by up-regulating expression of CD86. They secrete high levels of IL-8 and CCL5 in response to LPS, an indication that the MyD88-dependent and MyD88-independent signalling pathways downstream of TLR4 ligation are functioning normally. However, these cells are poor stimulators of T-cell proliferation in allogeneic mixed lymphocyte reactions. Furthermore, patient MDDCs fail to secrete IFN-α in response to poly(I:C) or IFN-β stimulation. Altered DC function may contribute to impaired cellular immune responses and chronicity of disease following HCV infection in this cohort. An effective therapeutic vaccine for chronic HCV infection will most likely need to target DCs to elicit an appropriate cellular response; therefore, it is important to resolve how the DCs of different patient cohorts respond to stimulation via TLRs.

Incidental walking activity is sufficient to induce time-dependent conditioning of the Achilles tendon

The Achilles tendon has been seen to exhibit time-dependent conditioning when isometric muscle actions were of a prolonged duration, compared to those involved in dynamic activities, such as walking. Since, the effect of short duration muscle activation associated with dynamic activities is yet to be established, the present study aimed to investigate the effect of incidental walking activity on Achilles tendon diametral strain. Eleven healthy male participants refrained from physical activity in excess of the walking required to carry out necessary daily tasks and wore an activity monitor during the 24 h study period. Achilles tendon diametral strain, 2 cm proximal to the calcaneal insertion, was determined from sagittal sonograms. Baseline sonographic examinations were conducted at approximately 08:00 h followed by replicate examinations at 12 and 24 h. Walking activity was measured as either present (1) or absent (0) and a linear weighting function was applied to account for the proximity of walking activity to tendon examination time. Over the course of the day the median (min, max) Achilles tendon diametral strain was -11.4 (4.5, -25.4)%. A statistically significant relationship was evident between walking activity and diametral strain (P<0.01) and this relationship improved when walking activity was temporally weighted (AIC 131 to 126). The results demonstrate that the short yet repetitive loads generated during activities of daily living, such as walking, are sufficient to induce appreciable time-dependant conditioning of the Achilles tendon. Implications arise for the in vivo measurement of Achilles tendon properties and the rehabilitation of tendinopathy.

Effect of oxygen on recovery from maximal exercise in patients with chronic obstructive pulmonary disease

BACKGROUND

The effects of oxygen on recovery from exercise in patients with chronic obstructive pulmonary disease (COPD) are not clearly known. A study was undertaken to determine whether oxygen given after maximal exercise reduced the degree of dynamic hyperinflation and so reduced the perception of breathlessness.

METHODS

Eighteen patients with moderate to severe COPD performed maximal symptom limited exercise on a cycle ergometer. During recovery they received either air or oxygen at identical flow rates in a randomised, single blind, crossover design. Inspiratory capacity, breathing pattern data, dyspnoea intensity, and leg fatigue scores were collected at regular intervals during recovery. At a subsequent visit patients underwent a similar protocol but with a face mask in situ to eliminate the effects of instrumentation.

RESULTS

When oxygen was given the time taken for resolution of dynamic hyperinflation was significantly shorter (mean difference between air and oxygen 6.61(1.65) minutes (95% CI 3.13 to 10.09), p = 0.001). Oxygen did not, however, reduce the perception of breathlessness during recovery nor did it affect the time taken to return to baseline dyspnoea scores in either the instrumented or non-instrumented state (mean difference 2.11 (1.41) minutes (95% CI -0.88 to 5.10), p = 0.15).

CONCLUSIONS

Oxygen reduces the degree of dynamic hyperinflation during recovery from exercise but does not make patients feel less breathless than breathing air. This suggests that factors other than lung mechanics may be important during recovery from exercise, or it may reflect the cooling effect of both air and oxygen.