dc.contributor.authorPatanaik, Amiya
dc.contributor.authorKwoh, Chee Keong
dc.contributor.authorChua, Eric C. P.
dc.contributor.authorGooley, Joshua J.
dc.contributor.authorChee, Michael W. L.
dc.date.accessioned2015-07-06T06:18:53Z
dc.date.available2015-07-06T06:18:53Z
dc.date.copyright2015en_US
dc.date.issued2015
dc.identifier.citationPatanaik, A., Kwoh, C. K., Chua, E. C. P., Gooley, J. J., & Chee, M. W. L. (2015). Classifying vulnerability to sleep deprivation using baseline measures of psychomotor vigilance. SLEEP, 38(5), 723-734.en_US
dc.identifier.urihttp://hdl.handle.net/10220/26277
dc.description.abstractObjective: 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 data. Setting: Two academic sleep laboratories. Participants: Independent samples of 135 (69 women, age 18 to 25 y), and 45 (3 women, age 22 to 32 y) healthy adults. Measurements and Results: 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. Conclusions: 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. Citation: Patanaik A, Kwoh CK, Chua EC, Gooley JJ, Chee MW. Classifying vulnerability to sleep deprivation using baseline measures of psychomotor vigilance. SLEEP 2015;38(5):723–734.en_US
dc.description.sponsorshipASTAR (Agency for Sci., Tech. and Research, S’pore)
dc.language.isoenen_US
dc.relation.ispartofseriesSLEEPen_US
dc.rights© 2015 Associated Professional Sleep Societies (APSS). This paper was published in Sleep and is made available as an electronic reprint (preprint) with permission of Associated Professional Sleep Societies (APSS). The published version is available at: [http://dx.doi.org/10.5665/sleep.4664]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.en_US
dc.subjectDRNTU::Science::Medicine
dc.titleClassifying vulnerability to sleep deprivation using baseline measures of psychomotor vigilanceen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.5665/sleep.4664
dc.description.versionPublished versionen_US


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