Key result
A novel data-driven method for estimating the specific differential entropy rate was developed to quantify predictive uncertainty in continuous-valued time series such as heart rate variability.
The study introduces a novel mathematical method for estimating the specific entropy rate of continuous-valued time series, which can be applied to quantify predictive uncertainty in physiological signals like heart rate variability.
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May enhance uncertainty quantification in HRV time series; leaves open clinical utility pending validation.
We introduce a method for quantifying the inherent unpredictability of a continuous-valued time series via an extension of the differential Shannon entropy rate. Our extension, the specific entropy rate, quantifies the amount of predictive uncertainty associated with a specific state, rather than averaged over all states. We provide a data-driven approach for estimating the specific entropy rate of an observed time series. Finally, we consider three case studies of estimating the specific entropy rate from synthetic and physiological data relevant to the analysis of heart rate variability.
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David Darmon (2016) studied Heart rate variability. Specific entropy rate estimation method was evaluated. A novel data-driven method for estimating the specific differential entropy rate was developed to quantify predictive uncertainty in continuous-valued time series such as heart rate variability.
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