Optimization of Biomathematical Model Predictions for Cognitive Performance Impairment in Individuals: Accounting for Unknown Traits and Uncertain States in Homeostatic and Circadian Processes

Abstract

Current biomathematical models of fatigue and performance do not accurately predict cognitive performance for individuals with a priori unknown degrees of trait vulnerability to sleep loss, do not predict performance reliably when initial conditions are uncertain, and do not yield statistically valid estimates of prediction accuracy. These limitations diminish their usefulness for predicting the performance of individuals in operational environments. To overcome these 3 limitations, a novel modeling approach was developed, based on the expansion of a statistical technique called Bayesian forecasting. The expanded Bayesian forecasting procedure was implemented in the two-process model of sleep regulation, which has been used to predict performance on the basis of the combination of a sleep homeostatic process and a circadian process. Employing the two-process model with the Bayesian forecasting procedure to predict performance for individual subjects in the face of unknown traits and uncertain states entailed subject-specific optimization of 3 trait parameters (homeostatic build-up rate, circadian amplitude, and basal performance level) and 2 initial state parameters (initial homeostatic state and circadian phase angle). Prior information about the distribution of the trait parameters in the population at large was extracted from psychomotor vigilance test (PVT) performance measurements in 10 subjects who had participated in a laboratory experiment with 88 h of total sleep deprivation. The PVT performance data of 3 additional subjects in this experiment were set aside beforehand for use in prospective computer simulations. The simulations involved updating the subject-specific model parameters every time the next performance measurement became avail-able, and then predicting performance 24 h ahead. Comparison of the predictions to the subjects' actual data revealed that as more data became available for the individuals at hand, the performance predictions became increasingly more accurate and had progressively smaller 95% confidence intervals, as the model parameters converged efficiently to those that best characterized each individual. Even when more challenging simulations were run (mimicking a change in the initial homeostatic state; simulating the data to be sparse), the predictions were still considerably more accurate than would have been achieved by the two-process model alone. Although the work described here is still limited to periods of consolidated wakefulness with stable circadian rhythms, the results obtained thus far indicate that the Bayesian forecasting procedure can successfully overcome some of the major outstanding challenges for biomathematical prediction of cognitive performance in operational settings.

A new metric for quantifying performance impairment on the psychomotor vigilance test

“…We assessed indirectly the ability of the metrics to capture the homeostatic and circadian processes by using each metric separately as a dependent variable to fit individual data to the two-process model, which is based on electroencephalography data (Borbé ly, 1982), and has been validated extensively on PVT data (Rajaraman et al, 2008(Rajaraman et al, , 2009Van Dongen et al, 2007). Again, we found that, in general, the RTD metric yielded consistently better fits than the other four PVT metrics, both in terms of the coefficient of determination (R 2 ) and the whiteness of the residual errors (Table 2).…”

Current Approaches and Challenges to Development of an Individualized Sleep and Performance Prediction Model~!2009-03-22~!2010-05-11~!2010-07-15~!

“…(Note that Bayesian model individualization and forecasting may be applied regardless of the amount of data available; cf. [17]). …”

“…Such approaches, which invariably involve some aspect of Bayesian statistical modeling, utilize a small amount of performance information about the individual at hand, and relate that to the variability in performance profiles previously obtained from a sample of the population (e.g., in a laboratory or field experiment) -see [17]. Given that performance responses to sleep loss involve a trait [7], the performance of a specific individual observed during a sustained operation (i.e., when little or no sleep is obtained), combined with the prior information estimated from the population, allows for reliable prediction of future performance.…”

A review of fatigue in fishermen: a complicated and underprioritised area of research

“…The awareness of the increased accident risk in the transport sector, has led to development and use of computerised mathematical process models, dual-models and 3-process models for predicting level of fatigue and its effect on cognitive functions, e.g. alertness/performance in daily life, for navy, airline and railway applications [9][10][11] as well as multimodal fatigue measure assessment for measuring early onset of drivers fatigue [12], but so far these have not been implemented in the fishing industry.…”