This study investigates the use of multiscale entropy (MSE) analysis to distinguish between the effects of aging and heart failure on heart rate dynamics. The authors analyzed RR time series from young, elderly, and congestive heart failure (CHF) subjects. MSE measures revealed characteristic curves for each group, indicating fundamental changes in heart rate dynamics due to age and disease. Fisher's linear discriminant was used to evaluate the classification accuracy of MSE features. The results showed that MSE features could separate elderly, young, and CHF subjects with 92% accuracy and healthy elderly subjects from CHF subjects with 94% accuracy. Additionally, the MSE features achieved a positive predictivity of 76% and a specificity of 83% for discriminating CHF subjects from healthy elderly subjects. The study concludes that MSE features effectively capture differences in complexity due to aging and heart failure, which have implications for modeling neuroautonomic perturbations in health and disease.This study investigates the use of multiscale entropy (MSE) analysis to distinguish between the effects of aging and heart failure on heart rate dynamics. The authors analyzed RR time series from young, elderly, and congestive heart failure (CHF) subjects. MSE measures revealed characteristic curves for each group, indicating fundamental changes in heart rate dynamics due to age and disease. Fisher's linear discriminant was used to evaluate the classification accuracy of MSE features. The results showed that MSE features could separate elderly, young, and CHF subjects with 92% accuracy and healthy elderly subjects from CHF subjects with 94% accuracy. Additionally, the MSE features achieved a positive predictivity of 76% and a specificity of 83% for discriminating CHF subjects from healthy elderly subjects. The study concludes that MSE features effectively capture differences in complexity due to aging and heart failure, which have implications for modeling neuroautonomic perturbations in health and disease.