Estimation of Characteristic Impedance using Multi-Gaussian Modelled Flow Velocity Waveform: A Virtual Subjects Study

Area of Science:

• Biomedical Engineering
• Cardiovascular Physiology
• Medical Imaging Analysis

Background:

• Characteristic impedance (Zc) is vital for understanding blood flow dynamics and wave separation analysis (WSA).
• Conventional WSA requires simultaneous pressure and flow velocity measurements, limiting its application.
• Simplified WSA methods using modeled flow velocity waveforms are needed for broader applicability.

Purpose of the Study:

• To estimate characteristic impedance (Zc) using a multi-Gaussian decomposition (MGD) modeled flow velocity waveform.
• To validate the accuracy of the MGD-modeled Zc estimation against Zc derived from actual flow velocity waveforms in the carotid artery.
• To assess the potential of this simplified method for wave separation analysis.

Main Methods:

• Frequency domain analysis was employed to estimate Zc from an MGD-modeled flow velocity waveform.
• The MGD-modeled Zc was compared with Zc estimated from true flow velocity waveforms in virtual healthy subjects.
• Statistical analyses, including correlation and Bland-Altman analysis, were performed for comparison.

Main Results:

• The MGD-modeled flow velocity waveform estimated Zc values ranging from 4.98 to 34.79, with a group average of 16.43±0.10.
• A small difference of 4.72% was found between the group average Zc values from both methods.
• A strong correlation (r = 0.708, p < 0.0001) and a bias of 0.74 were observed between the two Zc estimation methods.

Conclusions:

• Characteristic impedance (Zc) can be reliably estimated using MGD-modeled flow velocity waveforms.
• This method offers a potential surrogate for flow velocity models in wave separation analysis at the carotid artery.
• The technique allows Zc derivation without direct measurement of actual flow velocity, requiring only a single pulse waveform.