Evidence map›Paper›PMID 37851153›Full record

ArticleJournal of clinical monitoring and computing2024

Quantitative electroencephalogram in term neonates under different sleep states.

Ian Yuan, Georgia Georgostathi, Bingqing Zhang, Ashley Hodges, C Dean Kurth, Matthew P Kirschen, Jimmy W Huh, Alexis A Topjian, Shih-Shan Lang, Adam Richter and 2 more

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of clinical monitoring and computing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 4 citations in OpenAlex.

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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

12 authors at 2 institutions in 1 country.

Ian YuanDepartment of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia. Perelman School of Medicine, University of Pennsylvania, 3401 Civic Center Blvd., Philadelphia, PA, 19104, USA. yuani@chop.edu.
Georgia GeorgostathiVagelos Integrated Program in Energy Research, University of Pennsylvania, Philadelphia, PA, USA.
Bingqing ZhangDepartment of Biomedical and Health Informatics, Data Science and Biostatistics Unit, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Ashley HodgesDepartment of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia. Perelman School of Medicine, University of Pennsylvania, 3401 Civic Center Blvd., Philadelphia, PA, 19104, USA.
C Dean KurthDepartment of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia. Perelman School of Medicine, University of Pennsylvania, 3401 Civic Center Blvd., Philadelphia, PA, 19104, USA.
Matthew P KirschenDepartment of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia. Perelman School of Medicine, University of Pennsylvania, 3401 Civic Center Blvd., Philadelphia, PA, 19104, USA.
Jimmy W HuhDepartment of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia. Perelman School of Medicine, University of Pennsylvania, 3401 Civic Center Blvd., Philadelphia, PA, 19104, USA.
Alexis A TopjianDepartment of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia. Perelman School of Medicine, University of Pennsylvania, 3401 Civic Center Blvd., Philadelphia, PA, 19104, USA.
Shih-Shan LangDivision of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Adam RichterVagelos Integrated Program in Energy Research, University of Pennsylvania, Philadelphia, PA, USA.
Nicholas S AbendDepartment of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia. Perelman School of Medicine, University of Pennsylvania, 3401 Civic Center Blvd., Philadelphia, PA, 19104, USA.
Shavonne L MasseyDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Children's Hospital of Philadelphia · USUniversity of Pennsylvania · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electroencephalogram (EEG) can be used to assess depth of consciousness, but interpreting EEG can be challenging, especially in neonates whose EEG undergo rapid changes during the perinatal course. EEG can be processed into quantitative EEG (QEEG), but limited data exist on the range of QEEG for normal term neonates during wakefulness and sleep, baseline information that would be useful to determine changes during sedation or anesthesia. We aimed to determine the range of QEEG in neonates during awake, active sleep and quiet sleep states, and identified the ones best at discriminating between the three states. Normal neonatal EEG from 37 to 46 weeks were analyzed and classified as awake, quiet sleep, or active sleep. After processing and artifact removal, total power, power ratio, coherence, entropy, and spectral edge frequency (SEF) 50 and 90 were calculated. Descriptive statistics were used to summarize the QEEG in each of the three states. Receiver operating characteristic (ROC) curves were used to assess discriminatory ability of QEEG. 30 neonates were analyzed. QEEG were different between awake vs asleep states, but similar between active vs quiet sleep states. Entropy beta, delta2 power %, coherence delta2, and SEF50 were best at discriminating awake vs active sleep. Entropy beta had the highest AUC-ROC ≥ 0.84. Entropy beta, entropy delta1, theta power %, and SEF50 were best at discriminating awake vs quiet sleep. All had AUC-ROC ≥ 0.78. In active sleep vs quiet sleep, theta power % had highest AUC-ROC > 0.69, lower than the other comparisons. We determined the QEEG range in healthy neonates in different states of consciousness. Entropy beta and SEF50 were best at discriminating between awake and sleep states. QEEG were not as good at discriminating between quiet and active sleep. In the future, QEEG with high discriminatory power can be combined to further improve ability to differentiate between states of consciousness.

Indexed as

ElectroencephalographyROC CurveSleepWakefulnessArtifactsBrainConsciousnessEntropyFemaleHumansInfant, NewbornMaleSignal Processing, Computer-AssistedEEGNeonatal EEGNeonatal sleepQuantitative EEG

Identifiers

PMID37851153
OpenAlexW4387729269

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.