title: EMG variance during polysomnography as an assessment for REM sleep behavior disorder.
abstract: OBJECTIVE
  In a previous study, we validated a polysomnographic assessment for REM sleep behavior disorder (RBD). The method proved to be reliable but required slow, labor-intensive visual scoring of surface electromyogram (EMG) activity. We therefore developed a computerized metric to assess EMG variance and compared the results to those previously published for visual scoring, bed partner-rated RBD symptom scores, and clinical assessments by sleep medicine specialists.
  
  
  METHODS
  Retrospective validation of new computer algorithm.
  
  
  METHODS
  Sleep research laboratory
  
  
  METHODS
  Twenty-three subjects: 17 with neurodegenerative disorders (9 with probable or possible RBD), and 6 controls.
  
  
  METHODS
  N/A METHODS: We visually scored 2 consecutive nocturnal polysomnograms for each subject. A computer algorithm calculated the variance of the chin EMG during all 3-second mini-epochs, and compared variances during REM sleep to a threshold defined by variances during quiet NREM sleep. The percentage of all REM mini-epochs with variance above this threshold created a metric, which we refer to as the supra-threshold REM EMG activity metric (STREAM) for each subject.
  
  
  RESULTS
  The STREAM correlated highly with the visually-derived score for RBD severity (Spearman rho = 0.87, P < 0.0001). A clinical impression of probable or possible RBD was associated to a similar extent with both STREAM (Wilcoxon rank sum test, P = 0.009) and the visually-derived score (P = 0.018). An optimal STREAM cutoff identified probable or possible RBD with 100% sensitivity and 71% specificity. The RBD symptom score correlated with both STREAM (rho = 0.42, P = 0.046) and the visual score (rho = 0.42, P = 0.048).
  
  
  CONCLUSIONS
  These results suggest that a new, automated assessment for RBD may provide as much utility as a more time-consuming manual approach.
authors.affiliation: Michigan Tech Research Institute, Michigan Technological University, Ann Arbor, MI 48105, USA. joseph.burns@mtu.edu
authors.forename: Joseph W
authors.initials: JW
authors.lastname: Burns
authors.forename: Flavia B
authors.initials: FB
authors.lastname: Consens
authors.forename: Roderick J
authors.initials: RJ
authors.lastname: Little
authors.forename: Karen J
authors.initials: KJ
authors.lastname: Angell
authors.forename: Sid
authors.initials: S
authors.lastname: Gilman
authors.forename: Ronald D
authors.initials: RD
authors.lastname: Chervin
mesh_terms: D000368:Aged; D000465:Algorithms; D003626:Data Display; D004576:Electromyography / Q000295:instrumentation*; D005260:Female; D006801:Humans; D008297:Male; D008432:Mathematical Computing; D008875:Middle Aged; D019636:Neurodegenerative Diseases / Q000150:complications / Q000175:diagnosis; D017286:Polysomnography / Q000295:instrumentation*; D020187:REM Sleep Behavior Disorder / Q000175:diagnosis* / Q000209:etiology; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*