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

Peyman Tavallali,Marianne Razavi,Niema M Pahlevan

doi:10.1038/s41598-018-19457-0

Peyman Tavallali, Marianne Razavi + Show 1 more

Open Access

https://doi.org/10.1038/s41598-018-19457-0

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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.

Highlights

Cardiovascular diseases (CVDs) and stroke are among the major causes of death in the United States and the total cost related to them was more than $316 billion in 2011–20121,2
In (6), Mean Carotid Shape Factor (MCSF) is the mean carotid shape factor, Augmentation Index (AIx) is the augmentation index, SSN is the supra-sternal notch to femoral site length, Reflected Wave Arrival Time (RWAT) is the reflected wave arrival time for a cardiac arterial waveform cycle, Age is the age of the participant at the time of tonometry reading, Ps is the brachial systolic pressure, and Pd is the brachial diastolic pressure
After adjusting for standard risk factors, both Pulse Wave Velocity (PWV) and estimated PWV were significantly associated with an increased risk for a first major CVD

Summary

Introduction

Cardiovascular diseases (CVDs) and stroke are among the major causes of death in the United States and the total cost related to them was more than $316 billion in 2011–20121,2. The original set of variables used for feature extraction included IFs and their variants, carotid waveform shape factors such as reflected wave arrival time and augmentation index, and clinical features and blood pressure and age. In (6), MCSF is the mean carotid shape factor, AIx is the augmentation index, SSN is the supra-sternal notch to femoral site length, RWAT is the reflected wave arrival time for a cardiac arterial waveform cycle, Age is the age of the participant at the time of tonometry reading, Ps is the brachial systolic pressure, and Pd is the brachial diastolic pressure.

Results

Conclusion