Time-variant partial directed coherence in analysis of the cardiovascular system. A methodological study

Area of Science:

• Cardiovascular Physiology
• Biomedical Signal Processing
• Time-Series Analysis

Background:

• Heart rate variability (HRV) analysis is crucial for assessing autonomic nervous system function.
• Respiratory influences on HRV, particularly respiratory sinus arrhythmia (RSA), are well-established.
• Existing methods may struggle to differentiate true RSA from confounding artifacts.

Purpose of the Study:

• To introduce and validate time-variant partial directed coherence (tvPDC) for multivariate analysis of HRV, respiratory movements (RMs), and arterial blood pressure.
• To identify and characterize respiration-related HRV components beyond the primary RM frequency.
• To assess the utility of tvPDC in distinguishing true RSA from cardiac aliasing (CA) artifacts.

Main Methods:

• Application of tvPDC for the first time in a multivariate analysis of HRV, RMs, and arterial blood pressure.
• Analysis of simulated data to model and understand CA effects.
• Validation using clinical data from full-term neonates and sedated patients.

Main Results:

• tvPDC successfully identified respiration-related HRV components at frequencies other than the RM frequency, indicative of CA.
• The study demonstrated that CA components can contaminate the entire HRV frequency spectrum, potentially leading to misinterpretations.
• tvPDC effectively separated CA components from the RSA component and the Traube-Hering-Mayer wave in both simulated and clinical data.

Conclusions:

• tvPDC is a valuable tool for identifying and mitigating cardiac aliasing in HRV analysis.
• This method enhances the accuracy of RSA assessment by distinguishing it from confounding artifacts.
• tvPDC enables precise quantification of partial correlative interactions between respiratory movements and RSA, improving physiological insights.