SCH: EXP: Collaborative Research: A Low-cost and Non-invasive Method for Personalized Cardiovascular Health Assessment

University Of Maryland, College Park, College Park MD

Investigators

Abstract

This project develops a low-cost and non-invasive method that can help in assessing cardiovascular health and disease by deriving personalized cardiovascular risk predictors. Cardiovascular disease remains a major source of morbidity and mortality in the United States and around the world. The developed methods can be widely used to improve cardiovascular risk stratification and thereby reduce the incidence of stroke and heart disease. This project provides new opportunities for technological advances in pervasive and personalized medicine, which can ultimately improve the quality of life of human beings. This project also impacts education by developing new multi-disciplinary course modules and encouraging minority students to participate in this project. This research derives a methodological framework to infer cardiovascular risk predictors from the analysis of blood volume waveform signals measured by low-cost and non-invasive modalities such as oscillometric cuff oscillations. In this framework, model-based adaptive signal processing methods analyze blood volume waveform signals measured at peripheral locations on the body to derive personalized blood pressure waveform signals. Then, cardiovascular risk predictors are derived from a model-based analysis of these blood pressure and volume waveform signals. The research work includes: (1) deriving mathematical models that dictate the relation between blood pressure versus volume waveform signals; (2) deriving adaptive signal processing methods that transform blood volume waveform signals to blood pressure waveform signals; and 3) deriving methods that compute cardiovascular risk predictors from blood pressure and volume waveform signals. This project makes contributions to the derivation of a unified framework for mathematical modeling of the relation between blood pressure and volume waves. It also contributes to the advancement of adaptive signal processing methodologies relevant to physiological system modeling and health monitoring.