Artificial Intelligence Estimation of Carotid-Femoral Pulse Wave Velocity Using Carotid Waveform

Overview

Journal Sci Rep

Specialty Science

Date 2018 Jan 19

PMID 29343797

Citations 17

Authors

Peyman Tavallali

Marianne Razavi

Niema M Pahlevan

Affiliations

Soon will be listed here.

Abstract

In this article, we offer an artificial intelligence method to estimate the carotid-femoral Pulse Wave Velocity (PWV) non-invasively from one uncalibrated carotid waveform measured by tonometry and few routine clinical variables. Since the signal processing inputs to this machine learning algorithm are sensor agnostic, the presented method can accompany any medical instrument that provides a calibrated or uncalibrated carotid pressure waveform. Our results show that, for an unseen hold back test set population in the age range of 20 to 69, our model can estimate PWV with a Root-Mean-Square Error (RMSE) of 1.12 m/sec compared to the reference method. The results convey the fact that this model is a reliable surrogate of PWV. Our study also showed that estimated PWV was significantly associated with an increased risk of CVDs.

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