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Sleep and Breathing

, Volume 21, Issue 1, pp 119–133 | Cite as

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
Sleep Breathing Physiology and Disorders • Original Article
  • 259 Downloads

Abstract

Background

Snoring 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.

Methods

The 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.

Results

The 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.

Conclusion

In 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.

Keywords

Snoring Machine learning Support vector machines Automated snore detection 

Notes

Compliance with ethical standards

Funding

The Study of Women’s Health Across the Nation (SWAN) has grant support from the National Institutes of Health (NIH), DHHS, through the National Institute on Aging (NIA), the National Institute of Nursing Research (NINR), and the NIH Office of Research on Women’s Health (ORWH) (Grants U01NR004061, U01AG012505, U01AG012535, U01AG012531, U01AG012539, U01AG012546, U01AG012553, U01AG012554, U01AG012495). Funding for the SWAN Sleep Study is from the National Institute on Aging (Grants AG019360, AG019361, AG019362, AG019363). The NIH provided additional financial support in the form of funding to Ms. Samuelsson and Dr. Krafty (R01GM113243). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the NIA, NINR, ORWH, or the NIH.

Conflict of interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Laura B. Samuelsson
    • 1
  • Anusha A. Rangarajan
    • 2
  • Kenji Shimada
    • 3
  • Robert T. Krafty
    • 4
  • Daniel J. Buysse
    • 5
  • Patrick J. Strollo
    • 6
  • Howard M. Kravitz
    • 7
  • Huiyong Zheng
    • 8
  • Martica H. Hall
    • 5
  1. 1.Department of PsychologyUniversity of PittsburghPittsburghUSA
  2. 2.Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghUSA
  3. 3.Department of Mechanical EngineeringCarnegie Mellon UniversityPittsburghUSA
  4. 4.Department of BiostatisticsUniversity of PittsburghPittsburghUSA
  5. 5.Department of PsychiatryUniversity of PittsburghPittsburghUSA
  6. 6.Division of Pulmonary, Allergy, and Critical Care MedicineUniversity of PittsburghPittsburghUSA
  7. 7.Department of Psychiatry and Department of Preventive MedicineRush UniversityChicagoUSA
  8. 8.Department of BiostatisticsUniversity of MichiganAnn ArborUSA

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