Parameter Estimation in a Model of the Human Circadian Pacemaker Using a Particle Filter

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Objective: In the near future, real-time estimation of people's unique, precise circadian clock state has the potential to improve the efficacy of medical treatments and improve human performance on a broad scale. Human-centric lighting can bring circadian-rhythm support using biodynamic lighting solutions that sync light with the time of day. We investigate a method to improve the tracking of individual's circadian processes.

Methods: In literature, the human circadian physiology has been mathematically modeled using ordinary differential equations, the state of which can be tracked via the signal processing concept of a Particle Filter. We show that substantial improvements can be made if the estimator not only tracks state variables, such as the phase and amplitude of the circadian pacemaker, but also fits specific model parameters to the individual. In particular, we optimize model parameter τ x , which reflects the intrinsic period of the circadian pacemaker (τ). We show that both state and model parameters can be estimated based on minimally-invasive light exposure measurements and sleep-wake state observations. We also quantify the effect of inaccuracies in sensing.

Results: We demonstrate improved performance by estimating τ x for every individual, both with artificially created and human subject data. Prediction accuracy improves with every newly available observation. The estimated τ x -s correlate well with the subjects' chronotypes, in a similar way as τ correlates.

Conclusion: Our results show that individualizing the estimation of model parameters can improve circadian state estimation accuracy.

Significance: These findings underscore the potential improvements in personalized models over "one-size fits all" approaches.
Original languageEnglish
JournalIEEE Transactions on Biomedical Engineering
VolumeXX
Issue numberXX
DOIs
Publication statusE-pub ahead of print - 24 Sep 2020

Keywords

  • Biological system modeling
  • circadian rhythm
  • Mathematical model
  • Oscillators
  • Pacemakers
  • parameter estimation
  • particle filter
  • Physiology
  • Sensors
  • Sleep

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