Our Publications

OBJECTIVE:

To develop a standardised scheme for assessing normal and abnormal electroencephalography (EEG) features of preterm infants. To assess the interobserver agreement of this assessment scheme.

METHODS:

We created a standardised EEG assessment scheme for 6 different post-menstrual age (PMA) groups using 4 EEG categories. Two experts, not involved in the development of the scheme, evaluated this on 24 infants <32 weeks gestational age (GA) using random 2 hour EEG epochs. Where disagreements were found, the features were checked and modified. Finally, the two experts independently evaluated 2 hour EEG epochs from an additional 12 infants <37 weeks GA. The percentage of agreement was calculated as the ratio of agreements to the sum of agreements plus disagreements.

RESULTS:

Good agreement in all patients and EEG feature category was obtained, with a median agreement between 80% and 100% over the 4 EEG assessment categories. No difference was found in agreement rates between the normal and abnormal features (p = 0.959).

CONCLUSIONS:

We developed a standard EEG assessment scheme for preterm infants that shows good interobserver agreement.

SIGNIFICANCE:

This will provide information to Neonatal Intensive Care Unit (NICU) staff about brain activity and maturation. We hope this will prove useful for many centres seeking to use neuromonitoring during critical care for preterm infants.

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PURPOSE:Preterm twins are at higher risk of neurodisability than preterm singletons, with monochorionic-diamniotic (MCDA) twins at higher risk than dichorionic-diamniotic (DCDA) twins. The impact of genetic influences on EEG concordance in preterm twins <32 weeks of gestational age is not established. This study aims to investigate EEG concordance in preterm MCDA and dichorionic-diamniotic twins during maturation.

METHODS:Infants <32 weeks of gestational age had multichannel EEG recordings for up to 72 postnatal hours, with repeat recordings at 32 and 35 weeks of postmenstrual age. Twin pairs had synchronous recordings. Mathematical EEG features were generated to represent EEG power, discontinuity, and symmetry. Intraclass correlations, while controlling for gestational age, estimated similarities within twins.

RESULTS:EEGs from 10 twin pairs, 4 MCDA and 6 dichorionic-diamniotic pairs, and 10 age-matched singleton pairs were analyzed from a total of 36 preterm infants. For MCDA twins, 17 of 22 mathematical EEG features had significant (>0.6; P < 0.05) intraclass correlations at one or more time points, compared with 2 of 22 features for DCDA twins and 0 of 22 for singleton pairs. For MCDA twins, all 10 features of discontinuity and all four features of symmetry were significant at one or more time-points. Three features of the MCDA twins (spectral power at 3-8 Hz, EEG skewness at 3-15 Hz, and kurtosis at 3-15 Hz) had significant intraclass correlations over all three time points.

CONCLUSIONS:Preterm twin EEG similarities are subtle but clearly evident through mathematical analysis. MCDA twins showed stronger EEG concordance across different postmenstrual ages, thus confirming a strong genetic influence on preterm EEG activity at this early development stage.

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This review describes the maturational features of the baseline electroencephalogram (EEG) in the neurologically healthy preterm infant. Features such as continuity, sleep state, synchrony and transient waveforms are described, even from extremely preterm infants and includes abundant illustrated examples. The physiological significance of these EEG features and their relationship to neurodevelopment are highlighted where known. This review also demonstrates the importance of multichannel conventional EEG monitoring for preterm infants as many of the features described are not apparent if limited channel EEG monitors are used.

CONCLUSION:

This review aims to provide healthcare professionals in the neonatal intensive care unit with guidance on the more common normal maturational features seen in the EEG of preterm infants.

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OBJECTIVE:

To investigate the frequency and characteristics of electrographic seizures in preterm infants in the early postnatal period.

STUDY DESIGN:

Infants <32 weeks gestational age (GA) (n = 120) were enrolled for continuous multichannel electroencephalography (EEG) recording initiated as soon as possible after birth and continued for approximately up to 72 hours of age. Electrographic seizures were identified visually, annotated, and analyzed. Quantitative descriptors of the temporal evolution of seizures, including total seizure burden, seizure duration, and maximum seizure burden, were calculated.

RESULTS:

Median GA was 28.9 weeks (IQR, 26.6-30.3 weeks) and median birth weight was 1125 g (IQR, 848-1440 g). Six infants (5%; 95% CI, 1.9-10.6%) had electrographic seizures. Median total seizure burden, seizure duration, and maximum seizure burden were 40.3 minutes (IQR, 5.0-117.5 minutes), 49.6 seconds (IQR, 43.4-76.6 seconds), and 10.8 minutes/hour (IQR, 1.6-20.2 minutes/hour), respectively. Seizure burden was highest in 2 infants with significant abnormalities on neuroimaging.

CONCLUSION:

Electrographic seizures are infrequent within the first few days of birth in very preterm infants. Seizures in this population are difficult to detect accurately without continuous multichannel EEG monitoring.

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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.

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This review focuses on the role of electroencephalography (EEG) in monitoring abnormalities of preterm brain function. EEG features of the most common developmental brain injuries in preterm infants, including intraventricular haemorrhage, periventricular leukomalacia, and perinatal asphyxia, are described. We outline the most common EEG biomarkers associated with these injuries, namely seizures, positive rolandic sharp waves, EEG suppression/increased interburst intervals, mechanical delta brush activity, and other deformed EEG waveforms, asymmetries, and asynchronies. The increasing survival rate of preterm infants, in particular those that are very and extremely preterm, has led to a growing demand for a specific and shared characterization of the patterns related to adverse outcome in this unique population. This review includes abundant high-quality images of the EEG patterns seen in premature infants and will provide a valuable resource for everyone working in developmental neuroscience.

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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.

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BACKGROUND:

Preterm infants are at risk of adverse outcome. The aim of this study is to develop a multimodal model, including physiological signals from the first days of life, to predict 2-y outcome in preterm infants.

METHODS:

Infants <32 wk gestation had simultaneous multi-channel electroencephalography (EEG), peripheral oxygen saturation (SpO2), and heart rate (HR) monitoring. EEG grades were combined with gestational age (GA) and quantitative features of HR and SpO2 in a logistic regression model to predict outcome. Bayley Scales of Infant Development-III assessed 2-y neurodevelopmental outcome. A clinical course score, grading infants at discharge as high or low morbidity risk, was used to compare performance with the model.

RESULTS:

Forty-three infants were included: 27 had good outcomes, 16 had poor outcomes or died. While performance of the model was similar to the clinical course score graded at discharge, with an area under the receiver operator characteristic (AUC) of 0.83 (95% confidence intervals (CI): 0.69-0.95) vs. 0.79 (0.66-0.90) (P = 0.633), the model was able to predict 2-y outcome days after birth.

CONCLUSION:

Quantitative analysis of physiological signals, combined with GA and graded EEG, shows potential for predicting mortality or delayed neurodevelopment at 2 y of age.

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BACKGROUND:
The study aims to describe heart rate variability (HRV) in neonatal hypoxic ischemic encephalopathy (HIE) and correlate HRV with electroencephalographic (EEG) grade of HIE and neurodevelopmental outcome.
METHODS:
Multichannel EEG and electrocardiography (ECG) were assessed at 12-48 h after birth in healthy and encephalopathic full-term neonates. EEGs were graded (normal, mild, moderate, and severe). Neurodevelopmental outcome was assessed at 2 y of age. Seven HRV features were calculated using normalized-RR (NN) interval. The correlation of these features with EEG grade and outcome were measured using Spearman’s correlation coefficient.
RESULTS:
HRV was significantly associated with HIE severity (P < 0.05): standard deviation of NN interval (SDNN) (r = -0.62), triangular interpolation of NN interval histogram (TINN) (r = -0.65), mean NN interval (r = -0.48), and the very low frequency (VLF) (r = -0.60), low frequency (LF) (r = -0.67) and high frequency (HF) components of the NN interval (r = -0.60). SDNN at 24 and 48 h were significantly associated (P < 0.05) with neurodevelopmental outcome (r = -0.41 and -0.54, respectively).
CONCLUSION:
HRV is associated with EEG grade of HIE and neurodevelopmental outcome. HRV has potential as a prognostic tool to complement EEG.

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AIM: 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 10min 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.

RESULTS: A total of 12 mathematical features of the EEG signal were calculated and correlated with gestational age. 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. 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.

CONCLUSION: 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.

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AIM: Long-term electroencephalogram (EEG) recording is increasingly being used in the neonatal period, but application and maintenance of the EEG electrodes is challenging, especially in preterm infants. This study proposes a practical method of electrode application that can be used in the neonatal intensive care unit (NICU).

METHODS: EEG recording in preterm infants of <32 weeks of gestational age is often challenging and requires careful preparation and strict adherence to NICU protocols. An effective technique for EEG application in preterm infants is to use prepackaged, sterile, disposable, flat-surfaced EEG electrodes. The use of these electrodes in combination with a continuous positive airway pressure hat provides good security for electrodes and good quality EEG recordings. It also limits the handling of the infant, while strictly adhering to infection control policies. RESULTS: Long-term monitoring for >72 h has been achieved using this technique. Important steps to consider are efficient preparation of the recording machine and materials, careful electrode application and infection control.

CONCLUSION: A fast and effective method of EEG electrode placement is required for neonatal EEG monitoring. The practical techniques described in this article outline a reliable method of EEG electrode placement, suitable for even extremely preterm infants.

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Hypoxic Ischaemic Encephalopathy (HIE) remains a significant cause of neonatal death and long term disability. Heart rate variability (HRV) may help identify the presence and severity of encephalopathy. Our aim was to analyse HRV features in full-term neonates with HIE and assess its ability to grade severity of HIE and predict neurodevelopmental outcome at 2-years of age. This was a retrospective study of healthy full-term neonates and full-term neonates with HIE. All neonates had multichannel EEG and ECG monitoring from as soon as possible after birth. EEGs were graded at 12, 24, and 48 h (mild, moderate, severe) and 1 h epochs of EEG and ECG data were extracted. Features of HRV were calculated from ECG recordings in each epoch. A comparison of HRV features between HIE and healthy groups and within HIE groups (mild/moderate/severe) was performed. The ability of HRV features to predict neurodevelopmental outcome at 2-years of age was also assessed. In total, 44 neonates with HIE and 17 healthy controls were included. Measures of HRV were significantly negatively correlated with EEG grade of HIE severity. HRV was significantly reduced between mild and moderate HIE groups. EEG grade of HIE measured at 12, 24, and 24 h after birth has a strong positive predictive value and reduced HRV at 24 and 48 h has a strong negative predictive value for 2 year neurodevelopmental outcome. HRV features significantly correlate with the grade of HIE severity and may be useful for the prediction of long term outcome.

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Background EEG monitoring provides important information about the neurological status of the preterm infant but is difficult to interpret for most. We aim to automatically detect the typical bursting pattern (trace discontinu) of the preterm EEG and compare the detections with expert manual annotations. Methods The method was based on the single channel EEG method of Palmu et al. but extended to 8-channel recordings for the first time. The EEG signal was first filtered with a Kaiser-window filter and the output of a non-linear energy operator (NLEO) was calculated. The NLEO signal was smoothed and corrected for baseline artefacts. A burst was identified if the resulting signal remained above 1.5µV2 for longer than 1s. Each EEG channel was processed separately and the final outcome was considered to be an activity burst if a burst was detected in 2 or more channels. The method was tested on a database of 24 babies born before 30 weeks gestation (avg 26.5 + –1.7 weeks). For each baby 10 minutes of 8-channel EEG signal was analysed. Results Agreement with the expert burst annotations was on average 77.2% over the 24 subjects (Min:59.6%, Max:90.3%, std-dev:8.5%). Most errors consisted of disagreement over the precise start and end points of a burst. Conclusions Automatic burst detection has been applied for the first time to a large database of preterm 8-channel EEG. Promising results were obtained for automated EEG interpretation. Future work will attempt to reduce the error by use of more sophisticated methods to merge the per-channel detections.