Ultra-Convenient Cardiovascular Risk Predictors Measurement Based on the Ballistocardiogram
Description: The goal of this project is to investigate the feasibility of cardiovascular (CV) risk predictors measurement from the heartbeat-induced body movement called the ballistocardiogram (BCG). State-of-the-art methods to measure CV risk predictors necessitate costly procedures and trained operators, limiting their prevalent use (e.g., in the primary care) and preventing effective detection and treatment of CV disease (CVD). The BCG is receiving an increasing interest as a means for CV health assessment due to its close relationship to heart function and CV hemodynamics as well as its amenity for ultra-convenient measurement using, e.g., bed, scale, and wearables. However, the physical relationship between the BCG and CV hemodynamics has remained mysterious for more than a century. Due to this reason, most prior effort on the use of the BCG for CV health assessment has resorted to brute-force data mining, which ended up with limited success. Our innovative approach to achieve the project goal is to establish a systematic mathematical relationship between the BCG and CV risk predictors, so that CV risk predictors can be measured disciplinedly from the BCG with strong physical rationale. Specific aims are: (1) to develop a physics-based mathematical model that relates the BCG to CV risk predictors and then (2) to investigate the feasibility of CV risk predictors measurement from the BCG based on this model using hemodynamic data collected from animals. This project may resolve the long-standing mystery on the physical relationship between the BCG and CV risk predictors by discovering the physical basis of the BCG based on systematic mathematical modeling. This novelty differentiates this project from virtually all prior effort on BCG-based CV health assessment built upon ad-hoc data mining methods. In this way, this project may transform the way the BCG is utilized for CV risk predictors measurement and CV health assessment. This innovation may in turn accelerate the development of transformative CV health and risk monitoring methodologies to enable ultra-convenient prevention, detection, and treatment of CVD and stroke, supporting the mission of the American Heart Association of building healthier lives free of CVD and stroke.
Funder: American Heart Association
Amount: $200,000
Funding type: research
Funder scheme: Innovative Project Award
Start date: 2018-07-01
End date: 2020-06-30
Investigators: Jin-oh HAHN, Jin-oh HAHN
Institution: University of Maryland-College Park
Funded outputs count: 3
Funder award ID: 18IPA34110466
OpenAlex ID: G1589917125