Amplitude Permutation Conditional Entropy Detects the Decrease of Complexity of Heart Period Variability During Vagal...

Area of Science:

• Physiology
• Biomedical Engineering
• Cardiovascular Research

Background:

• Assessing heart period (HP) dynamics complexity is crucial for understanding cardiac control.
• Traditional methods like permutation conditional entropy (PCE) may struggle with low signal-to-noise data.

Purpose of the Study:

• To compare the efficacy of amplitude permutation conditional entropy (APCE) against permutation conditional entropy (PCE) in detecting changes in HP dynamics complexity.
• To evaluate APCE's reliability under conditions of reduced HP variability, such as vagal blockade.

Main Methods:

• HP variability was analyzed in healthy subjects under baseline and atropine-induced vagal blockade.
• HP data was also collected during head-up tilt to simulate autonomic challenges.
• Coarse-graining-free methods (PCE, APCE) and a coarse-graining-based method (k-nearest-neighbor conditional entropy, KNNCE) were computed over 256 consecutive HP values.

Main Results:

• PCE failed to detect decreased HP variability complexity during atropine administration, while APCE and KNNCE succeeded.
• All methods indicated reduced HP complexity during head-up tilt.
• APCE demonstrated strong correlation with KNNCE across both experimental protocols.

Conclusions:

• APCE is a more reliable measure than PCE for assessing cardiac control complexity, particularly when HP dynamics exhibit low signal-to-noise ratios.
• APCE's robustness makes it suitable for analyzing HP variability under various physiological conditions, including pharmacological interventions.