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

SCH：EXP：协作研究：一种低成本、无创的个性化心血管健康评估方法

• 批准号: 1404436
• 负责人: Jin-Oh Hahn
• 金额: $ 21.82万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1404436&HistoricalAwards=false
• 关键词: SCH; EXP; Collaborative; Research; Low

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.

该项目开发了一种低成本和非侵入性方法，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。开发的方法可广泛用于改善心血管风险分层，从而减少中风和心脏病的发生率。 该项目为普遍性和个性化医学的技术进步提供了新的机会，这最终可以改善人类的生活质量。该项目还通过开发新的多学科课程模块来影响教育，并鼓励少数族裔学生参与该项目。这项研究得出了一个方法学框架，从分析通过低比例和非侵入性模态（例如振发式摇篮振荡）来推断心血管风险预测指标。 在此框架中，基于模型的自适应信号处理方法分析了在人体外围位置测得的血量波形信号，以得出个性化的血压波浪信号。 然后，心血管风险预测因素来自对这些血压和体积波形信号的基于模型的分析。 研究工作包括：（1）得出数学模型，以决定血压与体积波形信号之间的关系； （2）推导自适应信号处理方法将血量波形信号转化为血压波形信号； 3）得出从血压和体积波形信号中计算心血管风险预测因子的方法。 该项目为统一框架的推导提供了贡献，用于血压和体积波之间关系的数学建模。 它还有助于与生理系统建模和健康监测有关的自适应信号处理方法的发展。