vicbi / zaoct-pwv-estimation

ZaoCT-PWV-Estimation

Determination of Aortic Characteristic Impedance and Total Arterial Compliance From Regional Pulse Wave Velocities Using Machine Learning: An in-silico Study

This repository contains Python scripts for training and testing machine learning regression methods to estimate total arterial compiance and characteristic impedance using as input non-invasive regional pulse wave velocity measurements.

Abstract

In-vivo assessment of aortic characteristic impedance (Zao) and total arterial compliance (CT) has been hampered by the need for either invasive or inconvenient and expensive methods to access simultaneous recordings of aortic pressure and flow, wall thickness, and cross-sectional area. In contrast, regional pulse wave velocity (PWV) measurements are non-invasive and clinically available. In this study, we present a non-invasive method for estimating Zao and CT using cuff pressure, carotid-femoral PWV (cfPWV), and carotid-radial PWV (crPWV). Regression analysis is employed for both Zao and CT. The regressors are trained and tested using a pool of virtual subjects (n = 3,818) generated from a previously validated in-silico model. Predictions achieved an accuracy of 7.40%, r = 0.90, and 6.26%, r = 0.95, for Zao, and CT, respectively. The proposed approach constitutes a step forward to non-invasive screening of elastic vascular properties in humans by exploiting easily obtained measurements. This study could introduce a valuable tool for assessing arterial stiffness reducing the cost and the complexity of the required measuring techniques. Further clinical studies are required to validate the method in-vivo.

Original Publication

For a comprehensive understanding of the methodology and background, please refer to the original publication: Bikia, V., Rovas, G., Pagoulatou, S., & Stergiopulos, N. (2021). Determination of aortic characteristic impedance and total arterial compliance from regional pulse wave velocities using machine learning: An in-silico study. Frontiers in bioengineering and biotechnology, 9, 649866.

Citation

If you use this code in your research, please cite the original publication:

Bikia, V., Rovas, G., Pagoulatou, S., & Stergiopulos, N. (2021). Determination of aortic characteristic impedance and total arterial compliance from regional pulse wave velocities using machine learning: An in-silico study. Frontiers in bioengineering and biotechnology, 9, 649866. https://doi.org/10.3389/fbioe.2021.649866

License

This project is licensed under the Apache License 2.0 - see the LICENSE.md file for details.

This work was developed as part of a research project undertaken by the Laboratory of Hemodynamics and Cardiovascular Technology at EPFL (https://www.epfl.ch/labs/lhtc/).

Feel free to reach out at [email protected] if you have any questions or need further assistance!