Evidence map›Paper›PMID 29974042›Full record

ArticleFrontiers in pediatrics2018

Pediatric Videosomnography: Can Signal/Video Processing Distinguish Sleep and Wake States?

A J Schwichtenberg, Jeehyun Choe, Ashleigh Kellerman, Emily A Abel, Edward J Delp

Open access · goldAbstract read
In one paragraph

Article in Frontiers in pediatrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.6field-weighted citation impact, top 11% 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

10 citing papers in PubMed, 34 citations in OpenAlex.

  1. Article
  2. Practical Application of Polysomnography in Infants.Journal of clinical medicine · 2025
    Review
  3. Early identification of sleep phenotypes in infants by videosomnography: a cross-sectional study.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025
    Article
  4. A cross-sectional study on the relationship between infant sleep, temperament, and bedtime practices.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024
    Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Review
  10. Review
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

5 authors at 1 institution in 1 country.

A J SchwichtenbergDepartment of Human Development and Family Studies, College of Health and Human Sciences, Purdue University, West Lafayette, IN, United States.
Jeehyun ChoeSchool of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States.
Ashleigh KellermanDepartment of Human Development and Family Studies, College of Health and Human Sciences, Purdue University, West Lafayette, IN, United States.
Emily A AbelDepartment of Human Development and Family Studies, College of Health and Human Sciences, Purdue University, West Lafayette, IN, United States.
Edward J DelpSchool of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States.
Purdue University West Lafayette · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The term videosomnography captures a range of video-based methods used to record and subsequently score sleep behaviors (most commonly sleep vs. wake states). Until recently, the time consuming nature of behavioral videosomnography coding has limited its clinical and research applications. However, with recent technological advancements, the use of auto-videosomnography techniques may be a practical and valuable extension of behavioral videosomnography coding. To test an auto-videosomnography system within a pediatric sample, we processed 30 videos of infant/toddler sleep using a series of signal/video-processing techniques. The resulting auto-videosomnography system provided minute-by-minute sleep vs. wake estimates, which were then compared to behaviorally coded videosomnography and actigraphy. Minute-by-minute estimates demonstrated moderate agreement across compared methods (auto-videosomnography with behavioral videosomnography, Cohen's kappa = 0.46; with actigraphy = 0.41). Additionally, auto-videosomnography agreements exhibited high sensitivity for sleep but only about half of the wake minutes were correctly identified. For sleep timing (sleep onset and morning rise time), behavioral videosomnography and auto-videosomnography demonstrated strong agreement. However, nighttime waking agreements were poor across both behavioral videosomnography and actigraphy comparisons. Overall, this study provides preliminary support for the use of an auto-videosomnography system to index sleep onset and morning rise time only, which may have potential telemedicine implications. With replication, auto-videosomnography may be useful for researchers and clinicians as a minimally invasive sleep timing assessment method.

Indexed as

actigraphinfantpediatricsignal processingsleeptoddlerwaking

Identifiers

PMID29974042
PMCPMC6020776
OpenAlexW2800499788

What OpenQuestion holds

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LicenceCC BY
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.