title: Nonparametric entropy estimation using kernel densities.
abstract: The entropy of experimental data from the biological and medical sciences provides additional information over summary statistics. Calculating entropy involves estimates of probability density functions, which can be effectively accomplished using kernel density methods. Kernel density estimation has been widely studied and a univariate implementation is readily available in MATLAB. The traditional definition of Shannon entropy is part of a larger family of statistics, called Renyi entropy, which are useful in applications that require a measure of the Gaussianity of data. Of particular note is the quadratic entropy which is related to the Friedman-Tukey (FT) index, a widely used measure in the statistical community. One application where quadratic entropy is very useful is the detection of abnormal cardiac rhythms, such as atrial fibrillation (AF). Asymptotic and exact small-sample results for optimal bandwidth and kernel selection to estimate the FT index are presented and lead to improved methods for entropy estimation.
authors.affiliation: Departments of Internal Medicine (Cardiovascular Division) and Statistics, University of Virginia, Charlottesville, Virginia, USA.
authors.forename: Douglas E
authors.initials: DE
authors.lastname: Lake
mesh_terms: D001145:Arrhythmias, Cardiac; D003198:Computer Simulation; D019277:Entropy; D008433:Mathematics; D008955:Models, Cardiovascular; D009200:Myocardial Contraction / Q000502:physiology; D010507:Periodicity; D012984:Software