title: Heart rate variability can be used to estimate sleepiness-related decrements in psychomotor vigilance during total sleep deprivation.
abstract: OBJECTIVE
  To assess whether changes in psychomotor vigilance during sleep deprivation can be estimated using heart rate variability (HRV).
  
  
  METHODS
  HRV, ocular, and electroencephalogram (EEG) measures were compared for their ability to predict lapses on the Psychomotor Vigilance Task (PVT).
  
  
  METHODS
  Chronobiology and Sleep Laboratory, Duke-NUS Graduate Medical School Singapore.
  
  
  METHODS
  Twenty-four healthy Chinese men (mean age ± SD = 25.9 ± 2.8 years).
  
  
  METHODS
  Subjects were kept awake continuously for 40 hours under constant environmental conditions. Every 2 hours, subjects completed a 10-minute PVT to assess their ability to sustain visual attention.
  
  
  RESULTS
  During each PVT, we examined the electrocardiogram (ECG), EEG, and percentage of time that the eyes were closed (PERCLOS). Similar to EEG power density and PERCLOS measures, the time course of ECG RR-interval power density in the 0.02-0.08-Hz range correlated with the 40-hour profile of PVT lapses. Based on receiver operating characteristic curves, RR-interval power density performed as well as EEG power density at identifying a sleepiness-related increase in PVT lapses above threshold. RR-interval power density (0.02-0.08 Hz) also classified subject performance with sensitivity and specificity similar to that of PERCLOS.
  
  
  CONCLUSIONS
  The ECG carries information about a person's vigilance state. Hence, HRV measures could potentially be used to predict when an individual is at increased risk of attentional failure. Our results suggest that HRV monitoring, either alone or in combination with other physiologic measures, could be incorporated into safety devices to warn drowsy operators when their performance is impaired.
authors.affiliation: Program in Neuroscience and Behavioral Disorders, Duke-NUS Graduate Medical School, Singapore.
authors.forename: Eric Chern-Pin
authors.initials: EC
authors.lastname: Chua
authors.forename: Wen-Qi
authors.initials: WQ
authors.lastname: Tan
authors.forename: Sing-Chen
authors.initials: SC
authors.lastname: Yeo
authors.forename: Pauline
authors.initials: P
authors.lastname: Lau
authors.forename: Ivan
authors.initials: I
authors.lastname: Lee
authors.forename: Ivan Ho
authors.initials: IH
authors.lastname: Mien
authors.forename: Kathiravelu
authors.initials: K
authors.lastname: Puvanendran
authors.forename: Joshua J
authors.initials: JJ
authors.lastname: Gooley
mesh_terms: D000328:Adult; D001143:Arousal / Q000502:physiology*; D004569:Electroencephalography; D005221:Fatigue / Q000175:diagnosis* / Q000209:etiology / Q000503:physiopathology; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008297:Male; D011237:Predictive Value of Tests; D011597:Psychomotor Performance / Q000502:physiology*; D012372:ROC Curve; D011930:Reaction Time; D012892:Sleep Deprivation / Q000150:complications / Q000503:physiopathology* / Q000523:psychology*; D055815:Young Adult