title: Computer-Assisted Automated Scoring of Polysomnograms Using the Somnolyzer System.
abstract: OBJECTIVE
  Manual scoring of polysomnograms is a time-consuming and tedious process. To expedite the scoring of polysomnograms, several computerized algorithms for automated scoring have been developed. The overarching goal of this study was to determine the validity of the Somnolyzer system, an automated system for scoring polysomnograms.
  
  
  METHODS
  The analysis sample comprised of 97 sleep studies. Each polysomnogram was manually scored by certified technologists from four sleep laboratories and concurrently subjected to automated scoring by the Somnolyzer system. Agreement between manual and automated scoring was examined. Sleep staging and scoring of disordered breathing events was conducted using the 2007 American Academy of Sleep Medicine criteria.
  
  
  METHODS
  Clinical sleep laboratories.
  
  
  RESULTS
  A high degree of agreement was noted between manual and automated scoring of the apnea-hypopnea index (AHI). The average correlation between the manually scored AHI across the four clinical sites was 0.92 (95% confidence interval: 0.90-0.93). Similarly, the average correlation between the manual and Somnolyzer-scored AHI values was 0.93 (95% confidence interval: 0.91-0.96). Thus, interscorer correlation between the manually scored results was no different than that derived from manual and automated scoring. Substantial concordance in the arousal index, total sleep time, and sleep efficiency between manual and automated scoring was also observed. In contrast, differences were noted between manually and automated scored percentages of sleep stages N1, N2, and N3.
  
  
  CONCLUSIONS
  Automated analysis of polysomnograms using the Somnolyzer system provides results that are comparable to manual scoring for commonly used metrics in sleep medicine. Although differences exist between manual versus automated scoring for specific sleep stages, the level of agreement between manual and automated scoring is not significantly different than that between any two human scorers. In light of the burden associated with manual scoring, automated scoring platforms provide a viable complement of tools in the diagnostic armamentarium of sleep medicine.
authors.affiliation: Department of Medicine, Johns Hopkins University Baltimore, MD.|Department of Epidemiology, Johns Hopkins University, Baltimore, MD.
authors.forename: Naresh M
authors.initials: NM
authors.lastname: Punjabi
authors.affiliation: Department of Mathematics, DePauw University, Greencastle, IN.
authors.forename: Naima
authors.initials: N
authors.lastname: Shifa
authors.affiliation: Medical University of Vienna, Vienna, Austria.
authors.forename: Georg
authors.initials: G
authors.lastname: Dorffner
authors.affiliation: Department of Medicine, Johns Hopkins University Baltimore, MD.
authors.forename: Susheel
authors.initials: S
authors.lastname: Patil
authors.affiliation: Department of Medicine, Johns Hopkins University Baltimore, MD.
authors.forename: Grace
authors.initials: G
authors.lastname: Pien
authors.affiliation: Department of Medicine, Johns Hopkins University Baltimore, MD.
authors.forename: Rashmi N
authors.initials: RN
authors.lastname: Aurora
mesh_terms: D000328:Adult; D000465:Algorithms; D001143:Arousal; D001331:Automation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D015588:Observer Variation; D017286:Polysomnography / Q000379:methods*; D015203:Reproducibility of Results; D012891:Sleep Apnea Syndromes / Q000175:diagnosis* / Q000378:metabolism / Q000503:physiopathology*; D056508:Sleep Medicine Specialty / Q000379:methods*; D012894:Sleep Stages / Q000502:physiology*
keywords: automated scoring; polysomnography