Quantifying partition-based Kolmogorov-Sinai Entropy on Heart Rate Variability: a young vs. elderly study

Area of Science:

• Physiology
• Information Theory
• Biomedical Engineering

Background:

• Quantifying complexity in physiological time series, especially heart rate variability (HRV), is crucial.
• Existing methods like Approximate Entropy and Sample Entropy rely on statistical approximations and parameter tuning.

Purpose of the Study:

• To investigate the application of Algorithmic Information Content, estimated via compression algorithms, for quantifying partition-based Kolmogorov-Sinai (K-S) entropy on HRV.
• To assess the ability of this K-S entropy measure to differentiate complexity dynamics between young and elderly individuals.

Main Methods:

• Utilized Algorithmic Information Content, estimated through effective compression algorithms.
• Applied partition-based Kolmogorov-Sinai (K-S) entropy estimation to heart rate variability (HRV) data.
• Analyzed data from the Fantasia database, comparing young and elderly subjects.

Main Results:

• Elderly individuals exhibited lower HRV complexity compared to younger adults.
• A more predictable behavior was observed in the elderly group.
• Significantly lower partition-based K-S entropy values were found in the elderly compared to the young.

Conclusions:

• Partition-based K-S entropy, estimated using Algorithmic Information Content, effectively quantifies complexity in HRV.
• This method can distinguish age-related differences in cardiovascular system dynamics.
• It serves as a valuable complementary tool for assessing cardiovascular health and detecting pathological conditions.