Heartbeat Dynamics Analysis under Cold-Pressure Test using Wavelet p-Leader Non-Gaussian Multiscale Expansions

Area of Science:

• Cardiovascular Physiology
• Nonlinear Dynamics
• Biomedical Signal Processing

Background:

• Multiscale and multifractal (MF) analyses are vital for characterizing heartbeat dynamics.
• Unevenly sampled heartbeat interval data requires robust pre-processing for accurate MF property estimation.
• Autonomic nervous system activity influences cardiovascular control, particularly during stress.

Purpose of the Study:

• To evaluate a novel wavelet p-leaders MF spectra method for analyzing cardiovascular variability.
• To assess the impact of inhomogeneous point-process modeling on MF property estimation.
• To investigate changes in heartbeat dynamics during sympathetic nervous system stimulation via the cold-pressor test.

Main Methods:

• Application of wavelet p-leaders MF spectra for MF analysis.
• Inhomogeneous point-process modeling for pre-processing unevenly sampled heartbeat series.
• Non-Gaussian multiscale expansion to analyze cardiovascular variability changes.
• Comparison of MF estimates from raw and modeled heartbeat data during resting and stress conditions.

Main Results:

• The proposed point-process modeling and MF analysis provide statistically significant features differentiating stress from resting states.
• The method effectively captures changes in heartbeat dynamics across various time scales.
• Wavelet p-leaders MF spectra successfully characterize cardiovascular variability under sympathetic stimulation.

Conclusions:

• The integrated approach of point-process modeling and wavelet p-leaders MF analysis offers a robust method for assessing cardiovascular control.
• This technique enhances the characterization of autonomic nervous system activity during physiological stress.
• Findings contribute to a deeper understanding of heartbeat dynamics in response to sympathetic challenges.