Speaker
Description
Physiological monitoring often generates data characterized by strong cyclostationarity (circadian rhythm) and sensor artifacts – irregular noise. Conventional models (e.g. ARIMA) often fail to capture the time-varying dependencies or conflate behavioral rhythms with noise. We propose a signal processing framework adapted for digital health data: the Fraction-of-Time (FOT) probability approach, alongside robust periodic ARMA (PARMA) modeling.
In contrast with ensemble-based methods that average across populations, the FOT models shift toward temporal occupancy, i.e. how much time a variable spent in a given range. This treats physiological variables as cyclostationary processes, defining probability based on mentioned temporal occupancy to the diurnal cycle. To capture the underlying dynamics, we utilize Robust PARMA modeling. The method accounts for the periodic correlation structure of biological rhythms while mitigating the impact of impulsive sensor outliers.
We demonstrate the feasibility of the FOT–robust PARMA approach through use cases related to digital health applications (e.g. circadian glucose patterns in diabetes management, heart rate variability monitoring with intermittent disruptions). The framework provides more stable representations of cyclical dynamics than standard models and retains clinically relevant observations that may be overlooked through heavy smoothing. Combining time-occupancy probabilities with robust periodic dynamics offers an alternative direction for extracting longitudinal information from digital health monitoring. This approach complements the existing biomedical modeling techniques where data irregularity and periodicity are dominant features.
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