Title :
Model-Based Human Circadian Phase Estimation Using a Particle Filter
Author :
Mott, Christopher ; Dumont, Guy ; Boivin, Diane B. ; Mollicone, Daniel
Author_Institution :
Dept. of Comput. & Electr. Eng., Univ. of British Columbia, Vancouver, BC, Canada
fDate :
5/1/2011 12:00:00 AM
Abstract :
We present a method for tracking an individual´s circadian phase that integrates dynamic models of circadian physiology with physiological measurements in a Bayesian statistical framework. A model of the circadian pacemaker´s response to light exposure is transformed into a nonlinear state-space model with a circadian phase state. The probability distribution of the circadian phase is estimated by a particle filter that predicts changes over time based on the model, and performs updates with information gained from physiological measurements. Simulations demonstrate how probability distributions allow flexible initialization of model states and enable statistical quantification of entrainment and divergence properties of the circadian pacemaker. The combined use of sleep-wake scheduling data and physiological measurements is demonstrated in a case study highlighting advantages for addressing the challenge of noninvasive ambulatory monitoring of circadian physiology.
Keywords :
Bayes methods; circadian rhythms; pacemakers; particle filtering (numerical methods); phase estimation; physiological models; probability; scheduling; sleep; state-space methods; Bayesian statistics; circadian pacemaker; circadian physiology; human circadian phase estimation; noninvasive ambulatory monitoring; nonlinear state-space model; particle filter; probability distribution; sleep-wake scheduling; Bayesian methods; Mathematical model; Oscillators; Pacemakers; Phase measurement; Probability distribution; Temperature measurement; Bayesian statistics; Monte Carlo methods; circadian physiology; nonlinear estimation; particle filter; Actigraphy; Algorithms; Bayes Theorem; Body Temperature; Circadian Rhythm; Computer Simulation; Humans; Models, Biological; Monte Carlo Method; Nonlinear Dynamics;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2011.2107321