Compression based entropy estimation of heart rate variability on multiple time scales.

Area of Science:

• Physiology
• Biomedical Engineering
• Data Science

Background:

• Heart rate variability (HRV) exhibits complex beat-to-beat fluctuations.
• Assessing HRV entropy across multiple timescales is crucial for understanding cardiac health.
• Existing methods may not fully capture the multiscale complexity of HRV.

Purpose of the Study:

• To develop a novel framework for multiscale entropy assessment of heart rate fluctuations.
• To utilize data compression techniques for quantifying HRV complexity.
• To compare HRV entropy characteristics between young and older adults.

Main Methods:

• Employed the Lempel-Ziv algorithm for lossless data compression.
• Applied a coarse-graining procedure to analyze RR interval time series on different timescales.
• Compared compressibility and entropy values of original RR interval data with randomized surrogate data.

Main Results:

• Original RR interval time series showed significantly lower compression entropy than randomized data.
• Older subjects exhibited significantly different multiscale entropy characteristics compared to younger subjects.
• The Lempel-Ziv algorithm demonstrated effectiveness in assessing HRV complexity.

Conclusions:

• Data compression offers a viable approach for multiscale entropy assessment of heart rate variability.
• Aging is associated with altered patterns of heart rate complexity across multiple timescales.
• This method provides new insights into the physiological basis of HRV.