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Actigraphy-based parameter tuning process for adaptive notch filter and circadian phase shift estimation.

Jiawei Yin1, Agung Julius1, John T Wen1

  • 1Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute , Troy, New York, USA.

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|September 2, 2020
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Summary
This summary is machine-generated.

This study introduces an adaptive notch filter (ANF) algorithm for analyzing actigraphy data to accurately estimate circadian phase. This method provides a reliable alternative to salivary melatonin measurements for tracking circadian rhythms.

Keywords:
Adaptive notch filter (ANF)actigraphycircadian phase estimationcircadian rhythmsmelatonin

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Area of Science:

  • Chronobiology
  • Biomedical Engineering
  • Sleep Science

Background:

  • Circadian phase estimation is crucial for understanding sleep-wake cycles and overall health.
  • Traditional methods like Dim Light Melatonin Onset (DLMO) are invasive and resource-intensive.
  • Actigraphy offers a non-invasive alternative but requires robust algorithms for accurate phase estimation.

Purpose of the Study:

  • To apply and optimize an adaptive notch filter (ANF) algorithm for continuous circadian phase estimation using actigraphy data.
  • To validate the ANF algorithm's accuracy against the gold standard DLMO method.
  • To establish a non-invasive method for assessing circadian phase shifts.

Main Methods:

  • Minute-by-minute actigraphy data were collected from eight healthy adults.
  • An adaptive notch filter (ANF) algorithm was applied to the actigraphy data.
  • Parameters of the ANF algorithm (adaptation rates, damping factor) were optimized.
  • Circadian phase was estimated using the optimized ANF and compared with DLMO measurements derived from salivary melatonin assays.

Main Results:

  • The optimized ANF algorithm accurately estimated the continuous circadian phase from actigraphy data.
  • The ANF-derived circadian phase estimations correlated well with DLMO measurements.
  • The study successfully demonstrated the ANF algorithm's capability in detecting circadian phase shifts.

Conclusions:

  • The proposed ANF algorithm, with a defined parameter tuning process, provides accurate circadian phase and phase shift measurements from actigraphy data.
  • This method offers a non-invasive and efficient alternative to salivary melatonin collections for circadian rhythm assessment.
  • Optimized ANF application to actigraphy data holds significant potential for clinical and research applications in chronobiology.