Classifying vulnerability to sleep deprivation using baseline measures of psychomotor vigilance.

ABSTRACT:

Objectives

To identify measures derived from baseline psychomotor vigilance task (PVT) performance that can reliably predict vulnerability to sleep deprivation.

Design

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 dat

SUBMITTER: Patanaik A 

PROVIDER: S-EPMC4402656 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature