Background Despite the availability of continuous conventional electroencephalography (cEEG), accurate diagnosis of neonatal seizures is challenging in clinical practice. Algorithms for decision support in the recognition of neonatal seizures could improve detection. We aimed to assess the diagnostic accuracy of an automated seizure detection algorithm called Algorithm for Neonatal Seizure Recognition (ANSeR). Methods This multicentre, randomised, two-arm, parallel, controlled trial was done in eight neonatal centres across Ireland, the Netherlands, Sweden, and the UK. Neonates with a corrected gestational age between 36 and 44 weeks with, or at significant risk of, seizures requiring EEG monitoring, received cEEG plus ANSeR linked to the EEG monitor displaying a seizure probability trend in real time (algorithm group) or cEEG monitoring alone (nonalgorithm group). The primary outcome was diagnostic accuracy (sensitivity, specificity, and false detection rate) of health-care professionals to identify neonates with electrographic seizures and seizure hours with and without the support of the ANSeR algorithm. Neonates with data on the outcome of interest were included in the analysis. This study is registered with ClinicalTrials.gov, NCT02431780. Findings Between Feb 13, 2015, and Feb 7, 2017, 132 neonates were randomly assigned to the algorithm group and 132 to the non-algorithm group. Six neonates were excluded (four from the algorithm group and two from the non-algorithm group). Electrographic seizures were present in 32 (25•0%) of 128 neonates in the algorithm group and 38 (29•2%) of 130 neonates in the non-algorithm group. For recognition of neonates with electrographic seizures, sensitivity was 81•3% (95% CI 66•7-93•3) in the algorithm group and 89•5% (78•4-97•5) in the non-algorithm group; specificity was 84•4% (95% CI 76•9-91•0) in the algorithm group and 89•1% (82•5-94•7) in the non-algorithm group; and the false detection rate was 36•6% (95% CI 22•7-52•1) in the algorithm group and 22•7% (11•6-35•9) in the non-algorithm group. We identified 659 h in which seizures occurred (seizure hours): 268 h in the algorithm versus 391 h in the nonalgorithm group. The percentage of seizure hours correctly identified was higher in the algorithm group than in the non-algorithm group (177 [66•0%; 95% CI 53•8-77•3] of 268 h vs 177 [45•3%; 34•5-58•3] of 391 h; difference 20•8% [3•6-37•1]). No significant differences were seen in the percentage of neonates with seizures given at least one inappropriate antiseizure medication (37•5% [95% CI 25•0 to 56•3] vs 31•6% [21•1 to 47•4]; difference 5•9% [-14•0 to 26•3]). Interpretation ANSeR, a machine-learning algorithm, is safe and able to accurately detect neonatal seizures. Although the algorithm did not enhance identification of individual neonates with seizures beyond conventional EEG, recognition of seizure hours was improved with use of ANSeR. The benefit might be greater in less experienced centres, but further study is required.

ObjectiveThe aim of this multicentre study was to describe detailed characteristics of electrographic seizures in a cohort of neonates monitored with multichannel continuous electroencephalography (cEEG) in 6 European centres.MethodsNeonates of at least 36 weeks of gestation who required cEEG monitoring for clinical concerns were eligible, and were enrolled prospectively over 2 years from June 2013. Additional retrospective data were available from two centres for January 2011 to February 2014. Clinical data and EEGs were reviewed by expert neurophysiologists through a central server.ResultsOf 214 neonates who had recordings suitable for analysis, EEG seizures were confirmed in 75 (35%). The most common cause was hypoxic-ischaemic encephalopathy (44/75, 59%), followed by metabolic/genetic disorders (16/75, 21%) and stroke (10/75, 13%). The median number of seizures was 24 (IQR 9–51), and the median maximum hourly seizure burden in minutes per hour (MSB) was 21 min (IQR 11–32), with 21 (28%) having status epilepticus defined as MSB>30 min/hour. MSB developed later in neonates with a metabolic/genetic disorder. Over half (112/214, 52%) of the neonates were given at least one antiepileptic drug (AED) and both overtreatment and undertreatment was evident. When EEG monitoring was ongoing, 27 neonates (19%) with no electrographic seizures received AEDs. Fourteen neonates (19%) who did have electrographic seizures during cEEG monitoring did not receive an AED.ConclusionsOur results show that even with access to cEEG monitoring, neonatal seizures are frequent, difficult to recognise and difficult to treat.Oberservation study numberNCT02160171

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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. INTRODUCTIONDespite increased survival of premature infants and some improvements in major disabilities, disability rates remain high particularly in extremely low birthweight infants (1,2). It is simply very hard to mimic the intrauterine environment in the neonatal intensive care unit (NICU). For babies born too soon, too small and too sick and who need intensive care, information about the function of the brain is essential. The electroencephalogram (EEG), which measures cerebral electrical activity recorded from electrodes placed on the scalp in predefined regions, provides critical real-time information about cerebral function. The EEG can also provide real-time markers of cerebral dysfunction, even when it is secondary to systemic disease and macroscopic cerebral lesions are not evident (3).The neonatal EEG contains complex spatiotemporal information that can be difficult to interpret. It is certainly more complex than the interpretation of other vital sign signals such as heart rate or respiratory rate. As a result, in many NICUs around the world, a simpler EEG methodology, using a restricted number of channels (1-2 channels)

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This study provides evidence that alterations of overnight changes of NREM-sleep slow waves during active ESES are reversible when ESES resolves, and that the severity of neuropsychological compromise might be related to the extent of slow wave impairment during ESES. Our findings suggest that analysis of slow waves might serve as a prognostic factor regarding cognitive outcome. ESES may serve as disease model of pathologic slow wave sleep and our results might be expanded to epilepsies with spike wave activation in slow wave sleep not only in children but also in adults.