Key result
The concepts of differential and conditional Renyi entropy rate were introduced to develop a measure of the Gaussianity of a linear random process for heart rate dynamics.
The study introduces Renyi entropy as a measure of heart rate Gaussianity, providing robust algorithms for its estimation to assess physiological complexity.
HR Gaussianity via Renyi entropy complements sample/approximate entropy for complexity; hypothesis-generating for diagnostics, needs validation.
Sample entropy and approximate entropy are measures that have been successfully utilized to study the deterministic dynamics of heart rate (HR). A complementary stochastic point of view and a heuristic argument using the Central Limit Theorem suggests that the Gaussianity of HR is a complementary measure of the physiological complexity of the underlying signal transduction processes. Renyi entropy (or q-entropy) is a widely used measure of Gaussianity in many applications. Particularly important members of this family are differential (or Shannon) entropy (q = 1) and quadratic entropy (q = 2). We introduce the concepts of differential and conditional Renyi entropy rate and, in conjunction with Burg's theorem, develop a measure of the Gaussianity of a linear random process. Robust algorithms for estimating these quantities are presented along with estimates of their standard errors.
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Douglas E. Lake (2005) studied Heart rate dynamics. Renyi entropy measures was evaluated on Measure of Gaussianity of a linear random process. The concepts of differential and conditional Renyi entropy rate were introduced to develop a measure of the Gaussianity of a linear random process for heart rate dynamics.
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