# Pulse Wave Velocity Estimation in a Controlled In Vitro Vascular Model: Benchmarking Machine Learning Approaches

## TL;DR

This paper tests machine learning methods to estimate pulse wave velocity in artificial blood vessels, comparing their accuracy against known values in a controlled setup.

## Contribution

The study introduces and benchmarks a Sugeno-type ANFIS model for predicting vascular hardness and pulse wave velocity in an in vitro setting.

## Key findings

- The proposed pipeline shows strong agreement with reference PWV measurements in controlled in vitro experiments.
- ANFIS outperforms linear regression and other machine learning baselines in hardness-level prediction.
- PWV estimates using Moens–Korteweg formulation align well with reference values under controlled conditions.

## Abstract

This study evaluates the feasibility of estimating stiffness-related parameters and pulse wave velocity (PWV) in a controlled in vitro circulatory setup using artificial silicone vessels with systematically varied Shore A hardness and wall thickness. From synchronized pressure and capacitive waveforms, fiducial points and engineered features are extracted, together with pump settings (stroke volume and heart rate). A Sugeno-type adaptive neuro-fuzzy inference system (ANFIS) is used for hardness-level prediction and benchmarked against linear regression and contemporary machine-learning/deep-learning baselines using stratified cross-validation. PWV estimates derived via hardness-to-elasticity conversion models and the Moens–Korteweg formulation are evaluated against a reference PWV obtained within the same experimental configuration. Under these controlled conditions, the proposed pipeline shows strong agreement with reference labels and measurements. The results should be interpreted as an in vitro validation step; translation to biological tissues or in vivo data will require external validation, calibration of material-property mapping, and robustness testing under physiological variability and measurement noise.