This study investigates the use of multiscale entropy (MSE) analysis to distinguish the effects of aging and heart failure on heart rate variability (HRV). MSE is a method that calculates entropy over a wide range of scales, allowing for the assessment of complex physiological dynamics. The researchers analyzed RR intervals from young (n=26), elderly (n=46), and heart failure (n=43) subjects. The MSE measures revealed characteristic curves that indicated fundamental changes in HRV 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 heart failure subjects with 92% accuracy. Older healthy subjects could be separated from heart failure subjects with 94% accuracy. The method also achieved 76% positive predictivity and 83% specificity in distinguishing heart failure and elderly subjects. The study found that MSE features capture differences in complexity due to aging and heart failure. These differences have implications for modeling neuroautonomic perturbations in health and disease. The MSE method is based on the Approximate Entropy (ApEn) family of parameters and has been applied to HRV data from healthy subjects and those with heart failure. The results suggest that MSE can be used in automatic classification algorithms to separate different groups. However, larger databases are needed to confirm the reliability of these results. The study concludes that MSE is a promising tool for analyzing complex physiological time series and distinguishing the effects of aging and heart failure on HRV.This study investigates the use of multiscale entropy (MSE) analysis to distinguish the effects of aging and heart failure on heart rate variability (HRV). MSE is a method that calculates entropy over a wide range of scales, allowing for the assessment of complex physiological dynamics. The researchers analyzed RR intervals from young (n=26), elderly (n=46), and heart failure (n=43) subjects. The MSE measures revealed characteristic curves that indicated fundamental changes in HRV 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 heart failure subjects with 92% accuracy. Older healthy subjects could be separated from heart failure subjects with 94% accuracy. The method also achieved 76% positive predictivity and 83% specificity in distinguishing heart failure and elderly subjects. The study found that MSE features capture differences in complexity due to aging and heart failure. These differences have implications for modeling neuroautonomic perturbations in health and disease. The MSE method is based on the Approximate Entropy (ApEn) family of parameters and has been applied to HRV data from healthy subjects and those with heart failure. The results suggest that MSE can be used in automatic classification algorithms to separate different groups. However, larger databases are needed to confirm the reliability of these results. The study concludes that MSE is a promising tool for analyzing complex physiological time series and distinguishing the effects of aging and heart failure on HRV.