The specific entropy rate provides a state-dependent measure of predictive uncertainty for continuous-valued time series, with potential applications in analyzing heart rate variability.
State-specific entropy rate refines time-series unpredictability assessment; extends Approximate/Sample Entropy tools but leaves clinical adoption open.
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 relate the specific entropy rate to popular `complexity' measures such as Approximate and Sample Entropies. 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 specific entropy rate from synthetic and physiological data relevant to the analysis of heart rate variability.
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David Darmon (2016) studied this question.
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