NDLI: Individualized performance prediction during total sleep deprivation: Accounting for trait vulnerability to sleep loss

Content Provider	IEEE Xplore Digital Library
Author	Ramakrishnan, S. Laxminarayan, S. Thorsley, D. Wesensten, N.J. Balkin, T.J. Reifman, J.
Copyright Year	2012
Description	Author affiliation: Dept. of Behavioral Biol., WRAIR, Silver Spring, MD, USA (Balkin, T.J.) \|\| Dept. of Behavioral Biol., Walter Reed Army Inst. of Res. (WRAIR), Silver Spring, MD, USA (Wesensten, N.J.) \|\| Telemedicine & Adv. Med. Technol. Res. Center (TATRC), USAMRMC, Frederick, MD, USA (Ramakrishnan, S.; Laxminarayan, S.; Thorsley, D.; Reifman, J.)
Abstract	Individual differences in vulnerability to sleep loss can be considerable, and thus, recent efforts have focused on developing individualized models for predicting the effects of sleep loss on performance. Individualized models constructed using a Bayesian formulation, which combines an individual's available performance data with a priori performance predictions from a group-average model, typically need at least 40 h of individual data before showing significant improvement over the group-average model predictions. Here, we improve upon the basic Bayesian formulation for developing individualized models by observing that individuals may be classified into three sleep-loss phenotypes: resilient, average, and vulnerable. For each phenotype, we developed a phenotype-specific group-average model and used these models to identify each individual's phenotype. We then used the phenotype-specific models within the Bayesian formulation to make individualized predictions. Results on psychomotor vigilance test data from 48 individuals indicated that, on average, ~85% of individual phenotypes were accurately identified within 30 h of wakefulness. The percentage improvement of the proposed approach in 10-h-ahead predictions was 16% for resilient subjects and 6% for vulnerable subjects. The trade-off for these improvements was a slight decrease in prediction accuracy for average subjects.
Sponsorship	IEEE Eng. Medicine Biol. Soc.
Starting Page	5574
Ending Page	5577
File Size	663526
Page Count	4
File Format	PDF
ISBN	9781424441198
ISSN	1557170X
e-ISBN	9781457717871
DOI	10.1109/EMBC.2012.6347257
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Atmospheric measurements Particle measurements Computational modeling
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Individualized performance prediction during total sleep deprivation: accounting for trait vulnerability to sleep loss (2012)

Individualized Performance Prediction During Total Sleep Deprivation: Accounting for Trait Vulnerability to Sleep Loss (2012)

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