Can EEG accurately predict 2-year neurodevelopmental outcome for preterm infants?

OBJECTIVE: Establish if serial, multichannel video electroencephalography (EEG) in preterm infants can accurately predict 2-year neurodevelopmental outcome. DESIGN AND PATIENTS: EEGs were recorded at three time points over the neonatal course for infants <32 weeks' gestational age (GA). Monitoring commenced soon after birth and continued over the first 3 days. EEGs were repeated at approximately 32 and 35 weeks' postmenstrual age (PMA). EEG scores were based on an age-specific grading scheme. Clinical score of neonatal morbidity risk and cranial ultrasound imaging were completed. SETTING: Neonatal intensive care unit at Cork University Maternity Hospital, Ireland. MAIN OUTCOME MEASURES: Bayley Scales of Infant Development III at 2 years' corrected age. RESULTS: Sixty-seven infants were prospectively enrolled in the study and 57 had follow-up available (median GA 28.9 weeks (IQR 26.5-30.4)). Forty had normal outcome, 17 had abnormal outcome/died. All EEG time points were individually predictive of abnormal outcome; however, the 35-week EEG performed best. The area under the receiver operating characteristic curve (AUC) for this time point was 0.91 (95% CI 0.83 to 1), p<0.001. Comparatively, the clinical course AUC was 0.68 (95% CI 0.54 to 0.80, p=0.015), while abnormal cranial ultrasound was 0.58 (95% CI 0.41 to 0.75, p=0.342). CONCLUSION: Multichannel EEG is a strong predictor of 2-year outcome in preterm infants particularly when recorded around 35 weeks' PMA. Infants at high risk of brain injury may benefit from early postnatal EEG recording which, if normal, is reassuring. Postnatal clinical complications can contribute to poor outcome; therefore, we state that a later EEG around 35 weeks has a role to play in prognostication.

Mathematical Analysis of EEG Concordance in Preterm Twin Infants.

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.

A standardised assessment scheme for conventional EEG in preterm infants.

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.

A review of important electroencephalogram features for the assessment of brain maturation in premature infants.

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.

Electrographic Seizures during the Early Postnatal Period in Preterm Infants.

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.

EEG - A Valuable Biomarker of Brain Injury in Preterm Infants.

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.

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

AIM: To develop a method that segments preterm EEG into bursts and inter-bursts by extracting and combining multiple EEG features. METHODS: Two EEG experts annotated bursts in individual EEG channels for 36 preterm infants with gestational age < 30 weeks. The feature set included spectral, amplitude, and frequency-weighted energy features. 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.

Predicting 2-y outcome in preterm infants using early multimodal physiological monitoring.

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.

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.