An Improved Methodology for Individualized Performance Prediction of Sleep-Deprived Individuals with the two-process model

http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA518396&Location=U2&doc=GetTRDoc.pdf

An Improved Methodology for Individualized Performance Prediction of Sleep-
Deprived Individuals with the Two-Process Model

Srinivasan Rajaraman, PhD; Andrei V. Gribok, PhD; Nancy J. Wesensten, PhD; Thomas J. Balkin, PhD; Jaques Reifman, PhD


We present a method based on the two-process model of sleep regulation for developing individualized biomathematical models that predict performance impairment for individuals subjected to total sleep loss. This new method advances our previous work in two important ways. First, it enables model customization to start as soon as the first performance
measurement from an individual becomes available. This was achieved by optimally combining the performance information obtained from the individual’s performance measurements with a priori performance information using a Bayesian framework, while retaining the strategy of transforming the nonlinear optimization problem of finding the optimal estimates of the two-process model parameters into a series of linear optimization problems. Second, by taking advantage of the linear representation of the two-process model, this new method enables the analytical computation of statistically based measures of reliability for the model predictions in the form of prediction intervals. Two distinct data sets were used to evaluate the proposed method

Citation: Rajaraman S; Gribok AV; Wesensten NJ; Balkin TJ; Reifman
J. An improved methodology for individualized performance prediction
of sleep-deprived individuals with the two-process model. SLEEP
2009;32(10):1377-1392.