Building an Open Source Classifier for the Neonatal EEG Background: A Systematic Feature-Based Approach From Expert Scoring to Clinical Visualization.
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ABSTRACT:
SUBMITTER: Moghadam SM
PROVIDER: S-EPMC8200402 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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:A quantitative and objective assessment of background electroencephalograph (EEG) in sick neonates remains an everyday clinical challenge. We studied whether long range temporal correlations quantified by detrended fluctuation analysis (DFA) could be used in the neonatal EEG to distinguish different grades of abnormality in the background EEG activity. Long-term EEG records of 34 neonates were collected after perinatal asphyxia, and their background was scored in 1 h epochs (8 h in each neonate) as mild, moderate or severe. We applied DFA on 15 min long, non-overlapping EEG epochs (n = 1088) filtered from 3 to 8 Hz. Our formal feasibility study suggested that DFA exponent can be reliably assessed in only part of the EEG epochs, and in only relatively short time scales (10-60 s), while it becomes ambiguous if longer time scales are considered. This prompted further exploration whether paradigm used for quantifying multifractal DFA (MF-DFA) could be applied in a more efficient way, and whether metrics from MF-DFA paradigm could yield useful benchmark with existing clinical EEG gradings. Comparison of MF-DFA metrics showed a significant difference between three visually assessed background EEG grades. MF-DFA parameters were also significantly correlated to interburst intervals quantified with our previously developed automated detector. Finally, we piloted a monitoring application of MF-DFA metrics and showed their evolution during patient recovery from asphyxia. Our exploratory study showed that neonatal EEG can be quantified using multifractal metrics, which might offer a suitable parameter to quantify the grade of EEG background, or to monitor changes in brain state that take place during long-term brain monitoring.
| S-EPMC4407610 | biostudies-literature
Project description:This report describes a set of neonatal electroencephalogram (EEG) recordings graded according to the severity of abnormalities in the background pattern. The dataset consists of 169 hours of multichannel EEG from 53 neonates recorded in a neonatal intensive care unit. All neonates received a diagnosis of hypoxic-ischaemic encephalopathy (HIE), the most common cause of brain injury in full term infants. For each neonate, multiple 1-hour epochs of good quality EEG were selected and then graded for background abnormalities. The grading system assesses EEG attributes such as amplitude, continuity, sleep-wake cycling, symmetry and synchrony, and abnormal waveforms. Background severity was then categorised into 4 grades: normal or mildly abnormal EEG, moderately abnormal EEG, majorly abnormal EEG, and inactive EEG. The data can be used as a reference set of multi-channel EEG for neonates with HIE, for EEG training purposes, or for developing and evaluating automated grading algorithms.
| S-EPMC10006081 | biostudies-literature
Project description:A key feature of normal neonatal EEG at term age is interhemispheric synchrony (IHS), which refers to the temporal co-incidence of bursting across hemispheres during trace alternant EEG activity. The assessment of IHS in both clinical and scientific work relies on visual, qualitative EEG assessment without clearly quantifiable definitions. A quantitative measure, activation synchrony index (ASI), was recently shown to perform well as compared to visual assessments. The present study was set out to test whether IHS is stable enough for clinical use, and whether it could be an objective feature of EEG normality. We analyzed 31 neonatal EEG recordings that had been clinically classified as normal (n = 14) or abnormal (n = 17) using holistic, conventional visual criteria including amplitude, focal differences, qualitative synchrony, and focal abnormalities. We selected 20-min epochs of discontinuous background pattern. ASI values were computed separately for different channel pair combinations and window lengths to define them for the optimal ASI intraindividual stability. Finally, ROC curves were computed to find trade-offs related to compromised data lengths, a common challenge in neonatal EEG studies. Using the average of four consecutive 2.5-min epochs in the centro-occipital bipolar derivations gave ASI estimates that very accurately distinguished babies clinically classified as normal vs. abnormal. It was even possible to draw a cut-off limit (ASI~3.6) which correctly classified the EEGs in 97% of all cases. Finally, we showed that compromising the length of EEG segments from 20 to 5 min leads to increased variability in ASI-based classification. Our findings support the prior literature that IHS is an important feature of normal neonatal brain function. We show that ASI may provide diagnostic value even at individual level, which strongly supports its use in prospective clinical studies on neonatal EEG as well as in the feature set of upcoming EEG classifiers.
| S-EPMC4274973 | biostudies-literature
Project description:In human preterm newborns, caffeine increases brain activity and improves neurodevelopmental outcomes. In animal models of hypoxic ischemic brain injury, caffeine pretreatment reduces infarct volume. We studied the relationship between tissue neuroprotection and brain activity after injury to further understand caffeine neuroprotection. Rat dams received caffeine prior to birth or on postnatal day 3 (P3) through P16. Caffeine-treated and -untreated pups underwent the Vannucci procedure (unilateral carotid ligation, global hypoxia) on P2. A subset had EEG recordings. Brain hemispheric infarct volume was measured on P16. P2 hypoxic ischemia (HI) results in histologic brain injury (mean ± standard deviation infarct volume 10.3 ± 4.6%) and transient suppression of EEG activity. Caffeine pretreatment reduces brain injury (mean ± standard deviation infarct volume 1.6 ± 4.5%, p < 0.001) and improves amplitude-integrated EEG (aEEG) and EEG burst duration and amplitude. Caffeine treatment after HI does not reduce infarct volume (mean ± standard deviation 8.3 ± 4.1%, p = 1.0). However, caffeine posttreatment was equally effective at restoring aEEG amplitude and EEG burst duration and amplitude. Thus, caffeine supports brain background electrical activity independent of tissue neuroprotection.
| S-EPMC7486269 | biostudies-literature
Project description:BackgroundDelayed brain function development in small-gestational-age (SGA) infants has been reported. We aimed to quantify rates of immature neonatal EEG patterns and their association with neurodevelopment in SGA full-term neonates.MethodsUsing a cohort design, 50 SGA (birthweight <10th percentile) and 44 appropriate-gestational-age (AGA) term neonates underwent continuous video-EEG recordings lasting >3 h. Seventy-three of them were assessed at 2-years-old using Bayley-III-Scales. For EEG analysis, several segments of discontinuous/alternating EEG tracings were selected.Main outcomes measured(1) Visual analysis (patterns of EEG maturity); (2) Power spectrum in δ, θ, α and β frequency bands; and (3) scores in motor, cognitive and language development.Results(1) SGA infants, compared to AGA, showed: (a) higher percentages of discontinuous EEG, both asynchrony and interhemispheric asymmetry, and bursts with delta-brushes, longer interburst-interval duration and more transients/hour; (b) lower relative power spectrum in δ and higher in α; and (c) lower scores on motor, language and cognitive neurodevelopment. (2) Asymmetry >5%, interburst-interval >5 s, discontinuity >11%, and bursts with delta-brushes >11% were associated with lower scores on Bayley-III.ConclusionsIn this prospective study, SGA full-term neonates showed high rates of immature EEG patterns. Low-birthweight and immaturity EEG were both correlated with low development scores.
| S-EPMC7326702 | biostudies-literature
Project description:To measure the inter-expert and intra-expert agreement in sleep spindle scoring, and to quantify how many experts are needed to build a reliable dataset of sleep spindle scorings.The EEG dataset was comprised of 400 randomly selected 115s segments of stage 2 sleep from 110 sleeping subjects in the general population (57±8, range: 42-72 years). To assess expert agreement, a total of 24 Registered Polysomnographic Technologists (RPSGTs) scored spindles in a subset of the EEG dataset at a single electrode location (C3-M2). Intra-expert and inter-expert agreements were calculated as F1-scores, Cohen's kappa (?), and intra-class correlation coefficient (ICC).We found an average intra-expert F1-score agreement of 72±7% (?: 0.66±0.07). The average inter-expert agreement was 61±6% (?: 0.52±0.07). Amplitude and frequency of discrete spindles were calculated with higher reliability than the estimation of spindle duration. Reliability of sleep spindle scoring can be improved by using qualitative confidence scores, rather than a dichotomous yes/no scoring system.We estimate that 2-3 experts are needed to build a spindle scoring dataset with 'substantial' reliability (?: 0.61-0.8), and 4 or more experts are needed to build a dataset with 'almost perfect' reliability (?: 0.81-1).Spindle scoring is a critical part of sleep staging, and spindles are believed to play an important role in development, aging, and diseases of the nervous system.
| S-EPMC4426257 | biostudies-literature
Project description:BackgroundLimited-channel EEG research in neonates is hindered by lack of open, accessible analytic tools. To overcome this limitation, we have created the Washington University-Neonatal EEG Analysis Toolbox (WU-NEAT), containing two of the most commonly used tools, provided in an open-source, clinically-validated package running within MATLAB.MethodsThe first algorithm is the amplitude-integrated EEG (aEEG), which is generated by filtering, rectifying and time-compressing the original EEG recording, with subsequent semi-logarithmic display. The second algorithm is the spectral edge frequency (SEF), calculated as the critical frequency below which a user-defined proportion of the EEG spectral power is located. The aEEG algorithm was validated by three experienced reviewers. Reviewers evaluated aEEG recordings of fourteen preterm/term infants, displayed twice in random order, once using a reference algorithm and again using the WU-NEAT aEEG algorithm. Using standard methodology, reviewers assigned a background pattern classification. Inter/intra-rater reliability was assessed. For the SEF, calculations were made using the same fourteen recordings, first with the reference and then with the WU-NEAT algorithm. Results were compared using Pearson's correlation coefficient.ResultsFor the aEEG algorithm, intra- and inter-rater reliability was 100% and 98%, respectively. For the SEF, the mean±SD Pearson correlation coefficient between algorithms was 0.96±0.04.ConclusionWe have demonstrated a clinically-validated toolbox for generating the aEEG as well as calculating the SEF from EEG data. Open-source access will enable widespread use of common analytic algorithms which are device-independent and unlikely to become outdated as technology changes, thereby facilitating future collaborative research in neonatal EEG.
| S-EPMC7606381 | biostudies-literature
Project description:Accurate incidence estimation of HIV infection from cross-sectional biomarker data is crucial for monitoring the epidemic and determining the impact of HIV prevention interventions. A key feature of cross-sectional incidence testing methods is the mean window period, defined as the average duration that infected individuals are classified as recently infected. Two assays available for cross-sectional incidence estimation, the BED capture immunoassay, and the Limiting Antigen (LAg) Avidity assay, measure a general characteristic of antibody response; performance of these assays can be affected and biased by factors such as viral suppression, resulting in sample misclassification and overestimation of HIV incidence. As availability and use of antiretroviral treatment increase worldwide, algorithms that do not include HIV viral load and are not impacted by viral suppression are needed for cross-sectional HIV incidence estimation. Using a phage display system to quantify antibody binding to over 3300 HIV peptides, we present a classifier based on top scoring peptide pairs that identifies recent infections using HIV antibody responses alone. Based on plasma samples from individuals with known dates of seroconversion, we estimated the mean window period for our classifier to be 217 days (95% confidence interval 183 to 257 days), compared to the estimated mean window period for the LAg-Avidity protocol of 106 days (76 to 146 days). Moreover, each of the four peptide pairs correctly classified more of the recent samples than the LAg-Avidity assay alone at the same classification accuracy for non-recent samples.
| S-EPMC8375573 | biostudies-literature
Project description:Objectives:Scoring laboratory polysomnography (PSG) data remains a manual task of visually annotating 3 primary categories: sleep stages, sleep disordered breathing, and limb movements. Attempts to automate this process have been hampered by the complexity of PSG signals and physiological heterogeneity between patients. Deep neural networks, which have recently achieved expert-level performance for other complex medical tasks, are ideally suited to PSG scoring, given sufficient training data. Methods:We used a combination of deep recurrent and convolutional neural networks (RCNN) for supervised learning of clinical labels designating sleep stages, sleep apnea events, and limb movements. The data for testing and training were derived from 10 000 clinical PSGs and 5804 research PSGs. Results:When trained on the clinical dataset, the RCNN reproduces PSG diagnostic scoring for sleep staging, sleep apnea, and limb movements with accuracies of 87.6%, 88.2% and 84.7% on held-out test data, a level of performance comparable to human experts. The RCNN model performs equally well when tested on the independent research PSG database. Only small reductions in accuracy were noted when training on limited channels to mimic at-home monitoring devices: frontal leads only for sleep staging, and thoracic belt signals only for the apnea-hypopnea index. Conclusions:By creating accurate deep learning models for sleep scoring, our work opens the path toward broader and more timely access to sleep diagnostics. Accurate scoring automation can improve the utility and efficiency of in-lab and at-home approaches to sleep diagnostics, potentially extending the reach of sleep expertise beyond specialty clinics.
| S-EPMC6289549 | biostudies-literature