Model-based human circadian phase estimation using a particle filter.
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.
Department of Computer and Electrical Engineering, The University of British Columbia Vancouver, BC V6T 1Z4, Canada. chris@pulsarinformatics.com
Christopher
C
Mott
Guy
G
Dumont
Diane B
DB
Boivin
Daniel
D
Mollicone
D056044:Actigraphy; D000465:Algorithms; D001499:Bayes Theorem; D001831:Body Temperature; D002940:Circadian Rhythm / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics