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Heart rate variability can be used to estimate sleepiness-related decrements in psychomotor vigilance during total sleep deprivation.

ABSTRACT: STUDY OBJECTIVES: To assess whether changes in psychomotor vigilance during sleep deprivation can be estimated using heart rate variability (HRV). DESIGN: HRV, ocular, and electroencephalogram (EEG) measures were compared for their ability to predict lapses on the Psychomotor Vigilance Task (PVT). SETTING: Chronobiology and Sleep Laboratory, Duke-NUS Graduate Medical School Singapore. PARTICIPANTS: Twenty-four healthy Chinese men (mean age ± SD = 25.9 ± 2.8 years). INTERVENTIONS: 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. MEASUREMENTS AND 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.

SUBMITTER: Chua EC

PROVIDER: S-EPMC3274333 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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Heart rate variability can be used to estimate sleepiness-related decrements in psychomotor vigilance during total sleep deprivation.

Chua Eric Chern-Pin EC Tan Wen-Qi WQ Yeo Sing-Chen SC Lau Pauline P Lee Ivan I Mien Ivan Ho IH Puvanendran Kathiravelu K Gooley Joshua J JJ

Sleep 20120301 3

<h4>Study objectives</h4>To assess whether changes in psychomotor vigilance during sleep deprivation can be estimated using heart rate variability (HRV).<h4>Design</h4>HRV, ocular, and electroencephalogram (EEG) measures were compared for their ability to predict lapses on the Psychomotor Vigilance Task (PVT).<h4>Setting</h4>Chronobiology and Sleep Laboratory, Duke-NUS Graduate Medical School Singapore.<h4>Participants</h4>Twenty-four healthy Chinese men (mean age ± SD = 25.9 ± 2.8 years).<h4>In ...[more]

PMID: 22379238

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Project description:To identify measures derived from baseline psychomotor vigilance task (PVT) performance that can reliably predict vulnerability to sleep deprivation.Subjects underwent total sleep deprivation and completed a 10-min PVT every 1-2 h in a controlled laboratory setting. Participants were categorized as vulnerable or resistant to sleep deprivation, based on a median split of lapses that occurred following sleep deprivation. Standard reaction time, drift diffusion model (DDM), and wavelet metrics were derived from PVT response times collected at baseline. A support vector machine model that incorporated maximum relevance and minimum redundancy feature selection and wrapper-based heuristics was used to classify subjects as vulnerable or resistant using rested data.Two academic sleep laboratories.Independent samples of 135 (69 women, age 18 to 25 y), and 45 (3 women, age 22 to 32 y) healthy adults.In both datasets, DDM measures, number of consecutive reaction times that differ by more than 250 ms, and two wavelet features were selected by the model as features predictive of vulnerability to sleep deprivation. Using the best set of features selected in each dataset, classification accuracy was 77% and 82% using fivefold stratified cross-validation, respectively.In both datasets, DDM measures, number of consecutive reaction times that differ by more than 250 ms, and two wavelet features were selected by the model as features predictive of vulnerability to sleep deprivation. Using the best set of features selected in each dataset, classification accuracy was 77% and 82% using fivefold stratified cross-validation, respectively.Despite differences in experimental conditions across studies, drift diffusion model parameters associated reliably with individual differences in performance during total sleep deprivation. These results demonstrate the utility of drift diffusion modeling of baseline performance in estimating vulnerability to psychomotor vigilance decline following sleep deprivation.

Dataset Information

Heart rate variability can be used to estimate sleepiness-related decrements in psychomotor vigilance during total sleep deprivation.

Publications

Heart rate variability can be used to estimate sleepiness-related decrements in psychomotor vigilance during total sleep deprivation.

Similar Datasets

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