title: Support vector machines for automated snoring detection: proof-of-concept.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Laura B
authors.initials: LB
authors.lastname: Samuelsson
authors.affiliation: Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
authors.forename: Anusha A
authors.initials: AA
authors.lastname: Rangarajan
authors.affiliation: Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
authors.forename: Kenji
authors.initials: K
authors.lastname: Shimada
authors.affiliation: Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Robert T
authors.initials: RT
authors.lastname: Krafty
authors.affiliation: Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Buysse
authors.affiliation: Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Patrick J
authors.initials: PJ
authors.lastname: Strollo
authors.affiliation: Department of Psychiatry and Department of Preventive Medicine, Rush University, Chicago, IL, USA.
authors.forename: Howard M
authors.initials: HM
authors.lastname: Kravitz
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Huiyong
authors.initials: H
authors.lastname: Zheng
authors.affiliation: Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA. hallmh@upmc.edu.
authors.forename: Martica H
authors.initials: MH
authors.lastname: Hall
mesh_terms: D000328:Adult; D015331:Cohort Studies; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008875:Middle Aged; D017286:Polysomnography; D000075082:Proof of Concept Study; D012307:Risk Factors; D012913:Snoring / Q000175:diagnosis*; D060388:Support Vector Machine; D014481:United States
keywords: Automated snore detection; Machine learning; Snoring; Support vector machines