Classifying Vulnerability to Sleep Deprivation Using Baseline Measures of Psychomotor Vigilance

Patanaik, Amiya; Kwoh, Chee Keong; Chua, Eric Chern Pin; Gooley, Joshua J.; Chee, Michael W.L.

doi:10.5665/sleep.4664

Cited by 33 publications

(35 citation statements)

References 49 publications

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“…They approach the problem by assessing a person’s responses to sleep deprivation periodically and updating the model to predict his/her responses to subsequent sleep deprivation, rendering it an impractical approach for many situations. More recently, drift diffusion model 53 parameters derived from baseline PVT data have been used to classify participants in resilient and vulnerable groups with 77% accuracy using a support vector machine (SVM) modeling approach 18 . In its current form, however, that model requires data to be combined across consecutive PVTs for reliable estimates of drift diffusion parameters, whereas our classifier relies on data derived from a single 10-min PVT.…”

Section: Discussionmentioning

confidence: 99%

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

Chua

Sullivan

Duffy

et al. 2019

Sci Rep

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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.

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Section: Discussionmentioning

confidence: 99%

“…These findings suggest that sleep deprivation can amplify individual differences in PVT performance that were already present at baseline. Moreover, these studies suggest that baseline measures of PVT performance can potentially be used to improve predictions of individual differences in attentional responses to sleep deprivation 18 .…”

Section: Introductionmentioning

confidence: 99%

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

Chua

Sullivan

Duffy

et al. 2019

Sci Rep

Self Cite

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“… 12 Even so, it has been reported that baseline diffusion model drift rate can be used to predict PVT performance during total sleep deprivation (Patanaik et al, 2015). …”

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confidence: 99%

Computational cognitive modeling of the temporal dynamics of fatigue from sleep loss

2017

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Computational models have become common tools in psychology. They provide quantitative instantiations of theories that seek to explain the functioning of the human mind. In this paper, we focus on identifying deep theoretical similarities between two very different models. Both models are concerned with how fatigue from sleep loss impacts cognitive processing. The first is based on the diffusion model and posits that fatigue decreases the drift rate of the diffusion process. The second is based on the ACT-R cognitive architecture and posits that fatigue decreases the utility of candidate actions leading to microlapses in cognitive processing. A biomathematical model of fatigue is used to control drift rate in the first account and utility in the second. We investigated the predicted response time distributions of these two integrated computational cognitive models for performance on a psychomotor vigilance test under conditions of total sleep deprivation, simulated shift work, and sustained sleep restriction. The models generated equivalent predictions of response time distributions with excellent goodness-of-fit to the human data. More importantly, although the accounts involve different modeling approaches and levels of abstraction, they represent the effects of fatigue in a functionally equivalent way: in both, fatigue decreases the signal-to-noise ratio in decision processes and decreases response inhibition. This convergence suggests that sleep loss impairs psychomotor vigilance performance through degradation of the quality of cognitive processing, which provides a foundation for systematic investigation of the effects of sleep loss on other aspects of cognition. Our findings illustrate the value of treating different modeling formalisms as vehicles for discovery.

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“…Repeated measures ANOVA, using multivariate models, with resistant/vulnerable cognitive performance as a between-participants factor was used to assess ESB and ISB measures across the five time points. Cognitively resistant (n = 8) and cognitively vulnerable (n = 8) groups were defined by a median split on TSD 10-min PVT performance [66], defined by total lapses (>500 ms response time) and errors performance (range: 1.33-33.33 PVT lapses and errors; mean ± SD, resistant: 3.63 ± 2.15 PVT lapses and errors; vulnerable: 12.00 ± 8.71 PVT lapses and errors); these groups were not significantly different in terms of sex or age (p > .05). After ANOVAs, post hoc comparisons corrected for multiple testing were used to assess differences in ESB and ISB measures for the cognitively resistant versus cognitively vulnerable groups at each time point.…”

Section: Statistical Analysesmentioning

confidence: 99%

The degree of radiation-induced DNA strand breaks is altered by acute sleep deprivation and psychological stress and is associated with cognitive performance in humans

Moreno‐Villanueva

Scheven

Feiveson

et al. 2018

Sleep

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Classifying Vulnerability to Sleep Deprivation Using Baseline Measures of Psychomotor Vigilance

Cited by 33 publications

References 49 publications

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

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

Computational cognitive modeling of the temporal dynamics of fatigue from sleep loss

The degree of radiation-induced DNA strand breaks is altered by acute sleep deprivation and psychological stress and is associated with cognitive performance in humans

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