Evidence map›Paper›PMID 27411338›Full record

ArticleSleep & breathing = Schlaf & Atmung2017

Support vector machines for automated snoring detection: proof-of-concept.

Laura B Samuelsson, Anusha A Rangarajan, Kenji Shimada, Robert T Krafty, Daniel J Buysse, Patrick J Strollo, Howard M Kravitz, Huiyong Zheng, Martica H Hall

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Sleep & breathing = Schlaf & Atmung, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. How to objectively measure snoring: a systematic review.Sleep & breathing = Schlaf & Atmung · 2024
    Pooled it
  2. Article
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

9 authors.

Laura B SamuelssonDepartment of Psychology, University of Pittsburgh, Pittsburgh, PA, USA.
Anusha A RangarajanDepartment of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
Kenji ShimadaDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
Robert T KraftyDepartment of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.
Daniel J BuysseDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
Patrick J StrolloDivision of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Howard M KravitzDepartment of Psychiatry and Department of Preventive Medicine, Rush University, Chicago, IL, USA.
Huiyong ZhengDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Martica H HallDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA. hallmh@upmc.edu.

Funding

WOMENS HEALTH ACROSS THE NATION--COORDINATING CENTERU01AG012553 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BROOKS, MARIA MORI · 1994 to 2019
$37.7M
WOMENS HEALTH ACROSS THE NATION--ENDOCRINE LABU01AG012495 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MCCONNELL, DANIEL S · 1994 to 2018
$18.8M
WOMENS HEALTH ACROSS THE NATION--PITTSBURGHU01AG012546 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI MATTHEWS, KAREN · 1994 to 2019
$16.2M
WOMENS HEALTH ACROSS THE NATION--UCDAVISU01AG012554 · NIA · UNIVERSITY OF CALIFORNIA DAVIS · PI GOLD, ELLEN B. · 1994 to 2019
$14.5M
WOMENS HEALTH ACROSS THE NATION--UCLAU01AG012539 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI GREENDALE, GAIL A · 1994 to 2018
$13.7M
THE STUDY OF WOMEN'S HEALTH ACROSS THE NATION-MICHIGANU01NR004061 · NINR · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HARLOW, SIOBAN D · 1994 to 2018
$12.9M
WOMENS HEALTH ACROSS THE NATION--CHICAGOU01AG012505 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI KRAVITZ, HOWARD M · 1994 to 2019
$11.9M
WOMENS HEALTH ACROSS THE NATION--MASSACHUSETTS GENERAL HU01AG012531 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI FINKELSTEIN, JOEL S · 1994 to 2019
$10.7M
Women's Health Across the Nation-New Jersey SiteU01AG012535 · NIA · UNIV OF MED/DENT OF NJ-NJ MEDICAL SCHOOL · PI DERBY, CAROL A. · 1994 to 2019
$9.2M
SWAN Sleep Study II: PittsburghR01AG019362 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HALL, MARTICA HELON · 2002 to 2008
$3.5M
SWAN Sleep Study II - Michigan CenterR01AG019360 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SOWERS, MARYFRAN R · 2002 to 2008
$2.3M
A Statistical Framework for the Spectral Analysis of High-Dimensional Physiological Time Series SignalsR01GM113243 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI KRAFTY, ROBERT T · 2014 to 2020
$2.0M
NIA NIH HHS R01 AG019360NIA NIH HHS R01 AG019361NIA NIH HHS R01 AG019362NIA NIH HHS R01 AG019363NIA NIH HHS U01 AG012495NIA NIH HHS U01 AG012505NIA NIH HHS U01 AG012531NIA NIH HHS U01 AG012535NIA NIH HHS U01 AG012539NIA NIH HHS U01 AG012546NIA NIH HHS U01 AG012553NIA NIH HHS U01 AG012554NIGMS NIH HHS R01 GM113243NINR NIH HHS U01 NR004061
6 · The paper itself

Abstract

backgroundSnoring has been shown to be associated with adverse physical and mental health, independent of the effects of sleep disordered breathing. Despite increasing evidence for the risks of snoring, few studies on sleep and health include objective measures of snoring. One reason for this methodological limitation is the difficulty of quantifying snoring. Conventional methods may rely on manual scoring of snore events by trained human scorers, but this process is both time- and labor-intensive, making the measurement of objective snoring impractical for large or multi-night studies.

methodsThe current study is a proof-of-concept to validate the use of support vector machines (SVM), a form of machine learning, for the automated scoring of an objective snoring signal. An SVM algorithm was trained and tested on a set of approximately 150,000 snoring and non-snoring data segments, and F-scores for SVM performance compared to visual scoring performance were calculated using the Wilcoxon signed rank test for paired data.

resultsThe ability of the SVM algorithm to discriminate snore from non-snore segments of data did not differ statistically from visual scorer performance (SVM F-score = 82.46 ± 7.93 versus average visual F-score = 88.35 ± 4.61, p = 0.2786), supporting SVM snore classification ability comparable to visual scorers.

conclusionIn this proof-of-concept, we established that the SVM algorithm performs comparably to trained visual scorers, supporting the use of SVM for automated snoring detection in future studies.

Indexed as

Diagnosis, Computer-AssistedProof of Concept StudySupport Vector MachineAdultCohort StudiesFemaleHumansMiddle AgedPolysomnographyRisk FactorsSnoringUnited StatesAutomated snore detectionMachine learningSnoringSupport vector machines

Identifiers

PMID27411338
PMCPMC5903275

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Registered trials

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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.