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Fast tuning of observer-based circadian phase estimator using biometric data.

Chukwuemeka O Ike1,2, John T Wen1,2, Meeko M K Oishi3

  • 1Lighting Enabled Systems and Applications (LESA) Engineering Research Center, Troy, NY, United States.

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Summary

This study introduces a new method for estimating circadian rhythms using a linear state observer. The technique provides continuous, real-time phase estimation from biometric data, improving upon traditional delayed methods.

Keywords:
ActigraphyCircadian rhythmMelatoninSleep-wake cycleState observerWearable data

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Area of Science:

  • Chronobiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Circadian rhythms are crucial for well-being, regulated by the suprachiasmatic nuclei (SCN).
  • Current circadian phase estimation methods rely on indirect measurements and suffer from significant delays.
  • The inaccessibility of the SCN hinders direct assessment of circadian state.

Purpose of the Study:

  • To develop a continuous and real-time method for circadian phase estimation.
  • To overcome the limitations of delayed and invasive traditional methods.
  • To utilize biometric signals for accurate circadian state assessment.

Main Methods:

  • Proposed a linear state observer for continuous phase estimation.
  • Designed an observer-based filter to isolate frequency components from biometric signals.
  • Tuned observer gains using an evolutionary optimization algorithm with a fixed 24-hour oscillatory frequency.

Main Results:

  • The developed filter successfully estimated circadian phase from minute-to-minute actigraphy data.
  • Achieved an average absolute error within 1.5 hours across all test subjects.
  • Demonstrated the feasibility of online circadian phase estimation under ambulatory conditions.

Conclusions:

  • The linear state observer offers an elegant and effective solution for continuous circadian phase estimation.
  • This approach overcomes the delay issues associated with conventional methods.
  • Enables real-time monitoring of circadian state using readily available biometric data.