Using Laguerre expansion within point-process models of heartbeat dynamics: a comparative study

Area of Science:

• Cardiovascular Physiology
• Biomedical Signal Processing
• Nonlinear Dynamics

Background:

• Point-process models are used for heartbeat dynamics analysis.
• Nonlinear models offer more accurate cardiovascular control quantification.
• ECG-derived heartbeat series contain complex nonlinear and non-Gaussian information.

Purpose of the Study:

• To propose a Laguerre expansion for linear and nonlinear Wiener-Volterra kernels.
• To account for nonlinear and non-Gaussian information in ECG-derived heartbeat series.
• To continuously estimate dynamic spectrum and bispectrum using reduced parameters.

Main Methods:

• Laguerre expansion of Wiener-Volterra kernels.
• Inverse-Gaussian probability model for up to quadratic nonlinearities.
• Continuous estimation of dynamic spectrum and bispectrum.
• Application to ECG-derived heartbeat series from 10 subjects during a stand-up protocol.

Main Results:

• The novel methodology improves algorithmic performance.
• Accurate characterization of sympatho-vagal changes to posture.
• Effective analysis of nonlinear and non-Gaussian heartbeat dynamics.

Conclusions:

• Laguerre expansion provides an efficient method for analyzing complex heartbeat dynamics.
• This approach enhances the understanding of cardiovascular autonomic regulation.
• The method accurately reflects sympatho-vagal responses to physiological challenges like postural changes.