Diagnostic value of amplitude-integrated electroencephalogram in neonatal seizures.
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ABSTRACT:
SUBMITTER: Zhang L
PROVIDER: S-EPMC5560306 | biostudies-other | 2011 Aug
REPOSITORIES: biostudies-other
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Project description:BackgroundWhile great attention has been paid to motor and cognitive impairments in children with neonatal Hypoxic-Ischemic Encephalopathy (HIE), sleep related circadian rhythm problems, although commonly present, are often neglected. Subsequently, no early clinical indicators have been reported to correlate with sleep-related circadian dysfunction during development.MethodsIn this study, we first analyzed patterns of the amplitude integrated electroencephalogram (aEEG) in a cohort of newborns with various degrees of HIE. Next, during follow-ups, we collected information of sleep and circadian related problems in these patients and performed correlation analysis between aEEG parameters and different sleep/circadian disorders.ResultsA total of 101 neonates were included. Our results demonstrated that abnormal aEEG background pattern is significantly correlated with circadian rhythmic (r = 0.289, P = 0.01) and breathing issues during sleep (r = 0.237, P = 0.037). In contrast, the establishment of sleep-wake cycle (SWC) showed no correlation with sleep/circadian problems. Detailed analysis showed that summation of aEEG score, along with low base voltage (r = 0.272, P = 0.017 and r = -0.228, P = 0.048, respectively), correlates with sleep circadian problems. In contrast, background pattern (BP) score highly correlates with sleep breathing problem (r = 0.319, P = 0.004).ConclusionAbnormal neonatal aEEG pattern is correlated with circadian related sleep problems. Our study thus provides novel insights into predictive values of aEEG in sleep-related circadian problems in children with HIE.
| S-EPMC8449491 | biostudies-literature
Project description:ObjectiveIncreasing animal studies supported the harmful effects of prolonged or frequent neonatal seizures in developing brain, including increased risk of later epilepsy. Various nonlinear analytic measures had been applied to investigate the change of brain complexity with age. This study focuses on clarifying the relationship between later epilepsy and the changes of electroencephalogram (EEG) complexity in neonatal seizures.MethodsEEG signals from 19 channels of the whole brain from 32 neonates below 2 months old were acquired. The neonates were classified into 3 groups: 9 were normal controls, 9 were neonatal seizures without later epilepsy, and 14 were neonatal seizures with later epilepsy. Sample entropy (SamEn), multiscale entropy (MSE) and complexity index (CI) were analyzed.ResultsAlthough there was no significant change in SamEn, the CI values showed significantly decreased over Channels C3, C4, and Cz in patients with neonatal seizures and later epilepsy compared with control group. More multifocal epileptiform discharges in EEG, more abnormal neuroimaging findings, and higher incidence of future developmental delay were noted in the group with later epilepsy.ConclusionsDecreased MSE and CI values in patients with neonatal seizures and later epilepsy may reflect the mixed effects of acute insults, underlying brain immaturity, and prolonged seizures-related injuries. The analysis of MSE and CI can therefore provide a quantifiable and accurate way to decrypt the mystery of neonatal seizures, and could be a promising predictor.
| S-EPMC4676749 | biostudies-literature
Project description:IntroductionPerinatal management and prognostic value of clinical evaluation and diagnostic tools have changed with the generalization of therapeutic hypothermia (TH) in infants with hypoxic-ischemic encephalopathy (HIE).Aimto ascertain the prognostic value of amplitude integrated electroencephalogram (aEEG) in neonates with HIE considering hours of life and treatment with TH.MethodsA systematic review was performed. Inclusion criteria were studies including data of neonates with HIE, treated or not with TH, monitored with aEEG and with neurodevelopmental follow-up of at least 12 months. The period of bibliographic search was until February 2016. No language restrictions were initially applied. Consulted databases were MEDLINE, Scopus, CINHAL and the Spanish language databases GuiaSalud and Bravo. Article selection was performed by two independent reviewers. Quality for each individual paper selected was evaluated using QUADAS-2. Review Manager (RevMan) version 5.3 software was used. Forest plots were constructed to graphically show sensitivity and specificity for all included studies, separating patients treated or not with hypothermia. Summary statistics were estimated using bivariate models and random effects approaches with the R package MADA from summary ROC curves. Meta-regression was used to estimate heterogeneity and trends.Resultsfrom the 403 articles initially identified, 17 were finally included and critically reviewed. In infants not treated with hypothermia the maximum reliability of an abnormal aEEG background to predict death or moderate/severe disability was at 36 hours of life, when a positive post-test probability of 97.90% was achieved (95%CI 88.40 to 99.40%). Positive likelihood ratio (+LR) at these hours of life was 26.60 (95%CI 4.40 to 94.90) and negative likelihood ratio (-LR) was 0.23 (95%CI 0.10 to 0.44). A high predictive value was already present at 6 hours of life in this group of patients, with a positive post-test probability of 88.20% (95%CI 79.80 to 93%) and a +LR of 4.34 (95%CI 2.31 to 7.73). In patients treated with TH the maximum predictive reliability was achieved at 72 hours of life (post-test probability of 95.70%, 95%CI 84.40 to 98.50%). +LR at this age was 24.30 (95%CI 5.89 to 71.30) and-LR was 0.40 (95%CI 0.25 to 0.57). Predictive value of aEEG at 6 hours of life was low in these patients (59.10%, 95%CI 55.70 to 63%).ConclusionThis study confirms that aEEG´s background activity, as recorded during the first 72 hours after birth, has a strong predictive value in infants with HIE treated or not with TH. Predictive values of traces throughout the following 72 hours are a helpful guide when considering and counselling parents about the foreseeable long-term neurological outcome.
| S-EPMC5089691 | biostudies-literature
Project description:PURPOSE:Many neonates undergo electroencephalogram (EEG) monitoring to identify and manage acute symptomatic seizures. Information about brain function contained in the EEG background data may also help predict neurobehavioral outcomes. For EEG background features to be useful as prognostic indicators, the interpretation of these features must be standardized across electroencephalographers. We aimed at determining the interrater and intrarater agreement among electroencephalographers interpreting neonatal EEG background patterns. METHODS:Five neonatal electroencephalographers reviewed 5-to-7.5-minute epochs of EEG from full-term neonates who underwent continuous conventional EEG monitoring. The EEG assessment tool used to classify background patterns was based on the American Clinical Neurophysiology Society's guideline for neonatal EEG terminology. Interrater and intrarater agreement were measured using Kappa coefficients. RESULTS:Interrater agreement was consistently highest for voltage (binary: substantial, kappa = 0.783; categorical: moderate, kappa = 0.562), seizure presence (fair-substantial; kappa = 0.375-0.697), continuity (moderate; kappa = 0.481), burst voltage (moderate; kappa = 0.574), suppressed background presence (moderate-substantial; kappa = 0.493-0.643), delta activity presence (fair-moderate; kappa = 0.369-0.432), theta activity presence (fair-moderate; kappa = 0.347-0.600), presence of graphoelements (fair; kappa = 0.381), and overall impression (binary: moderate, kappa = 0.495; categorical: fair-moderate, kappa = 0.347, 0.465). Agreement was poor or inconsistent for all other patterns. Intrarater agreement was variable, with highest average agreement for voltage (binary: substantial, kappa = 0.75; categorical: substantial, kappa = 0.714) and highest consistent agreement for continuity (moderate-substantial; kappa = 0.43-0.67) and overall impression (moderate-substantial; kappa = 0.42-0.68). CONCLUSIONS:This study demonstrates substantial variability in neonatal EEG background interpretation across electroencephalographers, indicating a need for educational and technological strategies aimed at improving performance.
| S-EPMC6322680 | biostudies-literature
Project description:Neonatal seizures are a common neurologic emergency for which therapies have not significantly changed in decades. Improvements in diagnosis and pathophysiologic understanding of the distinct features of acute symptomatic seizures and neonatal-onset epilepsies present exceptional opportunities for development of precision therapies with potential to improve outcomes. Herein, we discuss the pathophysiology of neonatal seizures and review the evidence for currently available treatment. We present emerging therapies in clinical and preclinical development for the treatment of acute symptomatic neonatal seizures. Lastly, we discuss the role of precision therapies for genetic neonatal-onset epilepsies and address barriers and goals for developing new therapies for clinical care.
| S-EPMC8608938 | biostudies-literature
Project description:The yield of epileptiform abnormalities in serial electroencephalography (EEG) studies has not been addressed in a population-based setting for subjects with incident epilepsy or a single unprovoked seizure, raising the possibility of methodologic limitations such as selection bias. Our aim was to address these limitations by assessing the yield and predictors of epileptiform abnormalities for the first and subsequent EEG recording in a study of incident epilepsy or single unprovoked seizure in Rochester, Minnesota.We used the resources of the Rochester Epidemiology Project to identify all 619 residents of Rochester, Minnesota, born in 1920 or later with a diagnosis of incident epilepsy (n = 478) or single unprovoked seizure (n = 141) between 1960 and 1994, who had at least one EEG study. Information on all EEG studies and their results was obtained by comprehensive review of medical records.Among subjects with epilepsy, the cumulative yield of epileptiform abnormalities was 53% after the first EEG study and 72% after the third. Among subjects with a single unprovoked seizure, the cumulative yield was 39% after the first EEG study and 68% after the third. Young age at diagnosis and idiopathic etiology were risk factors for finding epileptiform abnormalities across all EEG recordings.Although the cumulative yield of epileptiform abnormalities increases over successive EEG recordings, there is a decrease in the increment for each additional EEG study after the first EEG study. This is most evident in incident epilepsy and in younger subjects. Clinically it may be worthwhile to consider that the probability of finding an epileptiform abnormality after the third nonepileptiform EEG recording is low.
| S-EPMC4167205 | biostudies-literature
Project description:Aging is associated with reduced slow wave (SW) density (number SW/min in nonrapid-eye movement sleep) and amplitude. It has been proposed that an age-related decrease in SW density may be due to a reduction in electroencephalogram (EEG) amplitude instead of a decline in the capacity to generate SW. Here, we propose a data-driven approach to adapt SW amplitude criteria to age and sex. We predicted that the adapted criteria would reduce age and sex differences in SW density and SW characteristics but would not abolish them. A total of 284 healthy younger and older adults participated in one night of sleep EEG recording. We defined age- and sex-adapted SW criteria in a first cohort of younger (n = 97) and older (n = 110) individuals using a signal-to-noise ratio approach. We then used these age- and sex-specific criteria in an independent second cohort (n = 77, 38 younger and 39 older adults) to evaluate age and sex differences on SW density and SW characteristics. After adapting SW amplitude criteria, we showed maintenance of an age-related difference for SW density whereas the sex-related difference vanished. Indeed, older adults produced less SW compared with younger adults. Specifically, the adapted SW amplitude criteria increased the probability of occurrence of low amplitude SW (<80 µV) for older men especially. Our results thereby confirm an age-related decline in SW generation rather than an artifact in the detection amplitude criteria. As for the SW characteristics, the age- and sex-adapted criteria display reproducible effects across the two independent cohorts suggesting a more reliable inventory of the SW.
| S-EPMC7953219 | biostudies-literature
Project description:Many seizures in neonates are due to early-onset epilepsy, which is often difficult to diagnose, especially to explore the causes. Recently, the development of next-generation sequencing (NGS) has led to the discovery of a large number of genes involved in epilepsy. This may improve prompt detection of early-onset epilepsy in neonates. This study aimed at analyzing the genotype-phenotype correlations in neonates with seizures in a bid to improve the understanding of genetic diagnosis of early-onset epilepsy. Clinical features and prognosis of 15 children who underwent genetic testing having had unexplained seizures from February 2016 to May 2018 in Children's Hospital of Chongqing Medical University were analyzed retrospectively. The salient findings were: poor response to stimulus and abnormal electroencephalogram (EEG) in the initial period were observed in the group with concomitant genetic abnormalities. Despite the recent progress in genetic technology, molecular diagnosis for neonatal-onset epilepsy can be challenging due to genetic and phenotypic heterogeneities. However, some genotypes are associated with specific clinical manifestations and EEG patterns. Therefore, in-depth understanding of genotype-phenotype correlations would be useful to clinicians managing neonates with early-onset seizures.
| S-EPMC6888710 | biostudies-literature
Project description:Continuous electroencephalogram monitoring is associated with lower mortality in critically ill patients; however, it is underused due to the resource-intensive nature of manually interpreting prolonged streams of continuous electroencephalogram data. Here, we present a novel real-time, machine learning-based alerting and monitoring system for epilepsy and seizures that dramatically reduces the amount of manual electroencephalogram review.MethodsWe developed a custom data reduction algorithm using a random forest and deployed it within an online cloud-based platform, which streams data and communicates interactively with caregivers via a web interface to display algorithm results. We developed real-time, machine learning-based alerting and monitoring system for epilepsy and seizures on continuous electroencephalogram recordings from 77 patients undergoing routine scalp ICU electroencephalogram monitoring and tested it on an additional 20 patients.ResultsWe achieved a mean seizure sensitivity of 84% in cross-validation and 85% in testing, as well as a mean specificity of 83% in cross-validation and 86% in testing, corresponding to a high level of data reduction. This study validates a platform for machine learning-assisted continuous electroencephalogram analysis and represents a meaningful step toward improving utility and decreasing cost of continuous electroencephalogram monitoring. We also make our high-quality annotated dataset of 97 ICU continuous electroencephalogram recordings public for others to validate and improve upon our methods.
| S-EPMC8280012 | biostudies-literature
Project description:Tissue microRNAs (miRNAs) can detect cancers and predict prognosis. Several recent studies reported that tissue, plasma, and saliva miRNAs share similar expression profiles. In this study, we investigated the diagnostic value of salivary miRNAs (including whole saliva and saliva supernatant) for detection of esophageal cancer. By Agilent microarray, six deregulated miRNAs from whole saliva samples from seven patients with esophageal cancer and three healthy controls were selected. The six selected miRNAs were subjected to validation of their expression levels by RT-qPCR using both whole saliva and saliva supernatant samples from an independent set of 39 patients with esophageal cancer and 19 healthy controls.
2012-10-03 | E-GEOD-41268 | biostudies-arrayexpress