Dataset Information

Classifying attentional vulnerability to total sleep deprivation using baseline features of Psychomotor Vigilance Test performance.

ABSTRACT: There are strong individual differences in performance during sleep deprivation. We assessed whether baseline features of Psychomotor Vigilance Test (PVT) performance can be used for classifying participants' relative attentional vulnerability to total sleep deprivation. In a laboratory, healthy adults (n?=?160, aged 18-30 years) completed a 10-min PVT every 2?h while being kept awake for ?24?hours. Participants were categorized as vulnerable (n?=?40), intermediate (n?=?80), or resilient (n?=?40) based on their number of PVT lapses during one night of sleep deprivation. For each baseline PVT (taken 4-14?h after wake-up time), a linear discriminant model with wrapper-based feature selection was used to classify participants' vulnerability to subsequent sleep deprivation. Across models, classification accuracy was about 70% (range 65-76%) using stratified 5-fold cross validation. The models provided about 78% sensitivity and 86% specificity for classifying resilient participants, and about 70% sensitivity and 89% specificity for classifying vulnerable participants. These results suggest features derived from a single 10-min PVT at baseline can provide substantial, but incomplete information about a person's relative attentional vulnerability to total sleep deprivation. In the long term, modeling approaches that incorporate baseline performance characteristics can potentially improve personalized predictions of attentional performance when sleep deprivation cannot be avoided.

SUBMITTER: Chua EC

PROVIDER: S-EPMC6702200 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Classifying attentional vulnerability to total sleep deprivation using baseline features of Psychomotor Vigilance Test performance.

Chua Eric Chern-Pin EC Sullivan Jason P JP Duffy Jeanne F JF Klerman Elizabeth B EB Lockley Steven W SW Kristal Bruce S BS Czeisler Charles A CA Gooley Joshua J JJ

Scientific reports 20190820 1

There are strong individual differences in performance during sleep deprivation. We assessed whether baseline features of Psychomotor Vigilance Test (PVT) performance can be used for classifying participants' relative attentional vulnerability to total sleep deprivation. In a laboratory, healthy adults (n = 160, aged 18-30 years) completed a 10-min PVT every 2 h while being kept awake for ≥24 hours. Participants were categorized as vulnerable (n = 40), intermediate (n = 80), or resilient (n = 40 ...[more]

PMID: 31431644

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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

Classifying attentional vulnerability to total sleep deprivation using baseline features of Psychomotor Vigilance Test performance.

Publications

Classifying attentional vulnerability to total sleep deprivation using baseline features of Psychomotor Vigilance Test performance.

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