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.

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.

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.

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

Unlabelled: 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.

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.

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.

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.

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.

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.