Advancements in Hemodynamic Measurement: Arterial Resonance, Ultrasound, and Machine Learning

Citation

Yurk, Dominic Jeffrey (2023) Advancements in Hemodynamic Measurement: Arterial Resonance, Ultrasound, and Machine Learning. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/q7j4-vj19. https://resolver.caltech.edu/CaltechTHESIS:06022023-215651797

Abstract

This thesis covers two separate projects which both use ultrasound to measure a form of blood pressure in very different ways. The first project focuses on the noninvasive measurement of continuous arterial blood pressure via the previously unstudied phenomenon of arterial resonance. While prior research efforts have attempted many methods of noninvasive blood pressure measurement, none has been able to generate continuous, calibration-free measurements based on a first-principles physical model. This work describes the derivation of this resonance-based model, its in vitro validation, and its in vivo testing on 60 subjects. This testing resulted in robust resonance detection and accurate calculation of BP in the large majority of evaluated subjects, representing very promising performance for the first test of a new biomedical technology. The second study changes focus to the measurement of blood pressure in the right atrium of the heart, an important clinical indicator in heart disease patients. Rather than developing a new physical approach, this project used machine learning to model the existing assessments made by cardiologists. Comparison to gold standard invasive catheter measurements showed that model predictions were statistically indistinguishable from cardiologist measurements. Both of these projects represent significant advances in expanding precise blood pressure measurements beyond critical care units and expanding access to a much broader population.

Thesis Files