Evidence map›Paper›PMID 32858282›Full record

ArticleComputer methods and programs in biomedicine2020

WU-NEAT: A clinically validated, open-source MATLAB toolbox for limited-channel neonatal EEG analysis.

Zachary A Vesoulis, Paul G Gamble, Siddharth Jain, Nathalie M El Ters, Steve M Liao, Amit M Mathur

Abstract read
In one paragraph

Article in Computer methods and programs in biomedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

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0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. EEG Spectral Power and Neurovascular Coupling as Early Predictors of Neurodevelopmental Outcome in Neonatal Hypoxic-Ischemic Encephalopathy.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Zachary A VesoulisDepartment of Pediatrics, Division of Newborn Medicine, Washington University School of Medicine, 1 Children's Place, Campus Box 8116, St. Louis, MO 63110, USA. Electronic address: vesoulis_z@wustl.edu.
Paul G GambleDepartment of Pediatrics, Division of Newborn Medicine, Washington University School of Medicine, 1 Children's Place, Campus Box 8116, St. Louis, MO 63110, USA.
Siddharth JainDepartment of Neurology, Division of Pediatric Neurology, Washington University School of Medicine, USA.
Nathalie M El TersDepartment of Pediatrics, Division of Newborn Medicine, Washington University School of Medicine, 1 Children's Place, Campus Box 8116, St. Louis, MO 63110, USA.
Steve M LiaoDepartment of Pediatrics, Division of Newborn Medicine, Washington University School of Medicine, 1 Children's Place, Campus Box 8116, St. Louis, MO 63110, USA.
Amit M MathurDepartment of Pediatrics, Division of Neonatal-Perinatal Medicine, Saint Louis University School of Medicine, USA.

Funding

WU INSTITUTE OF CLINICAL AND TRANSLATIONAL SCIENCESUL1TR002345 · NCATS · WASHINGTON UNIVERSITY · PI William G. Powderly · 2017 to 2026
$97.8M
Washington University Institute of Clinical and Translational SciencesUL1TR000448 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$41.4M
Washington University Institute of Clinical and Translational Sciences (KL2) KL2TR002346 · NCATS · WASHINGTON UNIVERSITY · PI Dominic N Reeds · 2017 to 2026
$14.0M
Washington University Institute of Clinical and Translational SciencesKL2TR000450 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$8.8M
Racial Disparities in Preterm Brain Injury Are Driven by Differences in Cerebral AutoregulationK23NS111086 · NINDS · WASHINGTON UNIVERSITY · PI VESOULIS, ZACHARY ANDREW · 2019 to 2022
$736k
NCATS NIH HHS KL2 TR000450NCATS NIH HHS KL2 TR002346NCATS NIH HHS UL1 TR000448NCATS NIH HHS UL1 TR002345NINDS NIH HHS K23 NS111086
6 · The paper itself

Abstract

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.

Indexed as

ElectroencephalographyUniversitiesHumansInfantInfant, NewbornInfant, PrematureReproducibility of ResultsWashingtonEEGMATLABNeonatesOpen sourceQuantitative methodsSpectral edge frequency

Identifiers

PMID32858282
PMCPMC7606381

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.