Key result
Coarse-grained entropy rates and specific histogram estimation methods for static entropies successfully discriminated between patients at high risk of cardiac arrest and matched controls.
Why the study?
Does nonlinear dynamical analysis of heart rate variability using entropies and coarse-grained entropy rates discriminate between patients at high risk of cardiac arrest and healthy controls?
Case-Control (n=6)
Does nonlinear dynamical analysis of heart rate variability using entropies and coarse-grained entropy rates discriminate between patients at high risk of cardiac arrest and healthy controls?
Answers are grounded in this paper and cite their sources.
Coarse-grained entropy rates of heart rate variability may provide a novel non-linear method to identify patients at high risk for cardiac arrest.
Several definitions of static entropies and coarse-grained entropy rates (CER’s) were used to analyze 24-hour RR interval sequences of three sex, age and disease matched control pairs. The matching was done with the assessment of the risk of cardiac arrest in mind. Details of estimating probability densities (histograms), used to compute the static entropies, have been found crucial to obtain the proper discrimination between the members of control pairs. The effect of signal variance on the entropy estimation is discussed. In a preliminary result, CER’s have been found to yield additional information which may be helpful in discriminating health and pathology.
No takes yet. Share an insight, caveat, or question.
Paluš et al. (2009) conducted a case-control in Risk of cardiac arrest (n=6). High risk of cardiac arrest vs. Low risk matched controls was evaluated on Discrimination between high risk of cardiac arrest and controls using entropy measures. Coarse-grained entropy rates and specific histogram estimation methods for static entropies successfully discriminated between patients at high risk of cardiac arrest and matched controls.
Synapse has enriched 10 related papers. Consider them for comparative context: