SENSORS: Multi-Channel Wearable Biosensors for Continuous Cardiovascular Monitoring

Massachusetts Institute Of Technology, Cambridge MA

Investigators

Abstract

Presently, there is no adequate wearable biosensor system (WBS) for circulation monitoring. Enabling the continuous beat-to-beat monitoring of hemodynamics for observation periods of weeks, months, or years, could prove revolutionary as a tool for the study and management of chronic cardiovascular (CV) diseases such as hypertension, heart failure, and peripheral vascular disease. This task requires sensors on the body, which ideally can be worn for days at a time, yielding real time data. Existing sensor modalities that are able to make useful hemodynamic and vascular measurements are not actually wearable, whereas existing wearable sensor modalities record biosignals that are problematic for medical applications due to motion artifact and varying physiological conditions. To overcome these problems, we propose a new system identification approach based on multiple sensor fusion: the multi-channel blind system identification (MBSI) technique. MBSI allows the estimation of both an unknown input and the unknown dynamics by exploiting the correlation among sensor outputs observed simultaneously at multiple points. Placing multiple non-invasive biosensors on the surface of a subject yields simultaneous circulatory signals. These measurements can be systematically processed with MBSI algorithms to estimate the vascular channel dynamics and central hemodynamics, such as arterial blood pressure (ABP) and cardiac output (CO). Although the physiological conditions of the subject frequently change during an extended period of monitoring, the MBSI method can identify the changes in real time, and use the identified channel dynamics for correctly converting the observed peripheral sensor signals to the deep-body signals, such as CO, which has been measured only by invasive catheterization. In this project we will establish the theoretical foundation of the MBSI-based CV monitoring, develop a prototype wearable biosensor network having non-invasive, low-power, compact biosensors connected by a wireless ad hoc network, and develop three major clinical applications; 1) long-term cardiac output estimation with non-invasive sensors, 2) continuous arterial blood pressure monitoring with wearable sensors, 3) diagnosis of peripheral arterial pathology such as focal atherosclerosis and dissection. The highly interdisciplinary nature and the international collaboration component of this project provide unique educational opportunities to undergraduate and graduate students.