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

该项目开发了一种低成本和非侵入性的方法，可以通过得出个性化的心血管风险预测因子来帮助评估心血管健康和疾病。心血管疾病仍然是美国和世界各地发病率和死亡率的主要来源。所开发的方法可广泛用于改善心血管风险分层，从而降低中风和心脏病的发生率。该项目为普及和个性化医学的技术进步提供了新的机遇，最终可以提高人类的生活质量。该项目还通过开发新的多学科课程模块和鼓励少数族裔学生参与该项目来影响教育。本研究得出了一个方法框架，通过分析低成本和非侵入性的血液容量波形信号来推断心血管风险预测因素，这些信号通过低成本和非侵入性的方式测量，如振荡袖带振荡。在该框架中，基于模型的自适应信号处理方法分析在身体周围位置测量的血量波形信号，以获得个性化的血压波形信号。然后，从这些血压和容量波形信号的基于模型的分析中得出心血管风险预测因子。研究工作包括：(1)推导出血压与容量波形信号之间的关系的数学模型；(2)推导出将血容量波形信号转换为血压波形信号的自适应信号处理方法；以及(3)推导出根据血压和容量波形信号计算心血管风险预测因子的方法。该项目为建立血压和体积波之间关系的数学模型的统一框架做出了贡献。它还有助于推进与生理系统建模和健康监测相关的自适应信号处理方法。

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.