COMPLEXITY-BASED ANALYSIS OF HEART RATE VARIABILITY DURING AGING
Abstract
One of the important areas of heart research is investigating how heart activity changes during aging. In this research, we employed complexity-based techniques to analyze how heart activity varies based on the age of subjects. For this purpose, the heart rate variability (HRV) of 54 healthy subjects (30 M, 24 F, 28.5–76 years old) in three different age groups was analyzed using fractal theory, sample entropy, and approximate entropy. We showed that the fractal dimension, sample entropy, and approximate entropy of the RR interval time series (as HRV) are related to the age of the subjects. In other words, as subjects get older, the complexity of their RR interval time series decreases. Therefore, we decoded the variations in HRV during aging. The method of analysis that was employed in this research can be used to analyze the variations of other physiological signals (e.g. Electroencephalogram (EEG) signals) during aging.
