Browsing by Author "Gribok, Andrei V"
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- ItemIndividualized performance prediction of sleep-deprived individuals with the two-process model.(2008-02-11) Rajaraman, Srinivasan; Gribok, Andrei V; Wesensten, Nancy J; Balkin, Thomas J; Reifman, JaquesWe present a new method for developing individualized biomathematical models that predict performance impairment for individuals restricted to total sleep loss The underlying formulation is based on the two process model of sleep regulation which has been extensively used to develop group average models However in the proposed method the parameters of the two process model are systematically adjusted to account for an individual s uncertain initial state and unknown trait characteristics resulting in individual specific performance prediction models The method establishes the initial estimates of the model parameters using a set of past performance observations after which the parameters are adjusted as each new observation becomes available Moreover by transforming the nonlinear optimization problem of finding the best estimates of the two process model parameters into a set of linear optimization problems the proposed method yields unique parameter estimates Two distinct data sets are used to evaluate the proposed method Results of simulated data with superimposed noise show that the model parameters asymptotically converge to their true values and the model prediction accuracy improves as the number of performance observations increases and the amount of noise in the data decreases Results of a laboratory study 82 h of total sleep loss for three sleep loss phenotypes suggest that individualized models are consistently more accurate than group average models yielding as much as a threefold reduction in prediction errors In addition we show that the two process model of sleep regulation is capable of representing performance data only when the proposed individualized model is used
- ItemIndividualized short-term core temperature prediction in humans using biomathematical models.(2008-04-28) Gribok, Andrei V; Buller, Mark J; Reifman, JaquesThis study compares and contrasts the ability of three different mathematical modeling techniques to predict individual specific body core temperature variations during physical activity The techniques include a first principles physiology based SCENARIO model a purely data driven model and a hybrid model that combines first principles and data driven components to provide an early short term 20 30 min ahead warning of an impending heat injury Their performance is investigated using two distinct datasets a Field study and a Laboratory study The results indicate that for up to a 30 min prediction horizon the purely data driven model is the most accurate technique followed by the hybrid For this prediction horizon the first principles SCENARIO model produces root mean square prediction errors that are twice as large as those obtained with the other two techniques Another important finding is that if properly regularized and developed with representative data data driven and hybrid models can be made portable from individual to individual and across studies thus significantly reducing the need for collecting developmental data and constructing and tuning individual specific models
- ItemMoving towards individualized performance models.(2007-10-03) Reifman, Jaques; Rajaraman, Srinivasan; Gribok, Andrei V
- ItemProviding statistical measures of reliability for body core temperature predictions.(2007-11-16) Gribok, Andrei V; Buller, Mark J; Hoyt, Reed W; Reifman, JaquesThis paper describes the use of a data driven autoregressive integrated moving average model to predict body core temperature in humans during physical activity We also propose a bootstrap technique to provide a measure of reliability of such predictions in the form of prediction intervals We investigate the model s predictive capabilities and associated reliability using two distinct datasets both obtained in the field under different environmental conditions One dataset is used to develop the model and the other one containing an example of heat illness is used to test the model We demonstrate that accurate and reliable predictions of an extreme core temperature value of 39 5 degrees C can be made 20 minutes ahead of time even when the predictive model is developed on a different individual having core temperatures within healthy physiological limits This result suggests that data driven models can be made portable across different core temperature levels and across different individuals Also we show that the bootstrap prediction intervals cover the actual core temperature and that they exhibit intuitively expected behavior as a function of the prediction horizon and the core temperature variability
- ItemA robust method to estimate instantaneous heart rate from noisy electrocardiogram waveforms.(2011-02-04) Gribok, Andrei V; Chen, Xiaoxiao; Reifman, JaquesWe propose a new algorithm for real time estimation of instantaneous heart rate HR from noise laden electrocardiogram ECG waveforms typical of unstructured ambulatory field environments The estimation of HR from ECG waveforms is an indirect measurement problem that requires differencing which invariably amplifies high frequency noise We circumvented noise amplification by considering the estimation of HR as the solution of a weighted regularized least squares problem which in addition directly provided analytically based confidence intervals CIs for the estimated HRs To evaluate the performance of the proposed algorithm we applied it to simulated data and to noise laden ECG records that were collected during helicopter transport of trauma injured patients to a trauma center We compared the proposed algorithm with HR estimates produced by a widely used vital sign travel monitor and a standard HR estimation technique followed by postprocessing with Kalman filtering or spline smoothing The simulation results indicated that our algorithm consistently produced more accurate HR estimates with estimation errors as much as 67 smaller than those attained by the postprocessing methods while the results with the field collected data showed that the proposed algorithm produced much smoother and reliable HR estimates than those obtained by the vital sign monitor Moreover the obtained CIs reflected the amount of noise in the ECG recording and could be used to statistically quantify uncertainties in the HR estimates We conclude that the proposed method is robust to different types of noise and is particularly suitable for use in ambulatory environments where data quality is notoriously poor