Measuring Coupling of Rhythmical Time Series Using Cross Sample Entropy and Cross Recurrence Quantification Analysis

Area of Science:

• Physiology
• Biomedical Engineering
• Data Analysis

Background:

• Assessing the coupling of rhythmical patterns is crucial in understanding physiological systems.
• Existing methods may have limitations in accurately quantifying complex signal interactions.

Purpose of the Study:

• To compare the efficacy of cross sample entropy (xSE) and cross recurrence quantification analysis (cRQA) for evaluating rhythmical pattern coupling.
• To investigate these measures in simulated data and biological models of locomotor-respiratory coupling.

Main Methods:

• Generated simulated signals with varying fluctuations (regular, chaotic, random) and frequency ratios.
• Recorded breathing and walking data from healthy controls and COPD patients during treadmill exercise.
• Quantified xSE and cRQA measures (determinism, line statistics, entropy) for both simulated and experimental datasets.

Main Results:

• Simulated data showed xSE, determinism, and entropy were sensitive to frequency manipulations, with a 1:1 ratio being distinct.
• COPD patients exhibited a higher prevalence of a 2:3 frequency ratio.
• xSE and cRQA measures successfully differentiated between healthy and COPD groups and between different walking speeds.

Conclusions:

• xSE and cRQA are robust tools for assessing rhythmical pattern coupling in physiological systems.
• These methods can identify abnormal coupling patterns, such as those observed in COPD.
• The findings support the use of xSE and cRQA in analyzing complex biological rhythms.