“The most commonly used metric for evaluating alertness and vigilance is the Psychomotor Vigilance Test (PVT), previous studies have indicated that alertness and vigilance can be affected by the lack of sleep as a function of sleep loss. This study explores methods to predict median psychomotor vigilance reaction times. The data used in this study comes from a series of tests and surveys conducted on volunteer students. The data set contains many potential predictors of PVT and one aspect of the study was to identify variables that are useful in prediction. The performances of various prediction methods that allow for feature selection were evaluated. Prediction errors were estimated by using ten-fold validation method and root mean squared error was employed to compare the methods.
Results show that the linear model with LASSO feature selection provide the best predictions of psychomotor vigilance test median reaction time in this context. Moreover, we were able to identify subsets of predictors that lead to reduced prediction error and are useful for extracting biological insights. The linear mixed model and canonical correlation analysis provided information on what factors affect vigilance attention, and what cognitive functions are affected by sleep quality”--Abstract, page iii.
Samaranayake, V. A.
Thimgan, Matthew S.
Wen, Xuerong Meggie
Mathematics and Statistics
M.S. in Applied Mathematics
Missouri University of Science and Technology
ix, 36 pages
© 2020 Quang Nghia Le, All rights reserved.
Thesis - Open Access
Le, Quang Nghia, "Quantifying effects of sleep deprivation on cognitive performance" (2020). Masters Theses. 8004.