SENSORS: Multi-Channel Wearable Biosensors for Continuous Cardiovascular Monitoring

目前，还没有足够的可穿戴生物传感器系统（WBS）用于循环监测。在数周、数月或数年的观察期内实现血流动力学的连续逐搏监测，作为研究和管理慢性心血管（CV）疾病（如高血压、心力衰竭和外周血管疾病）的工具，可能是革命性的。 这项任务需要身体上的传感器，理想情况下可以一次佩戴数天，产生真实的时间数据。 能够进行有用的血液动力学和血管测量的现有传感器模态实际上不是可穿戴的，而现有的可穿戴传感器模态记录由于运动伪影和变化的生理条件而对于医疗应用有问题的生物信号。 为了克服这些问题，我们提出了一种新的系统辨识方法的基础上多传感器融合：多通道盲系统辨识（MBSI）技术。 MBSI允许通过利用在多个点同时观察到的传感器输出之间的相关性来估计未知输入和未知动态。将多个非侵入性生物传感器放置在受试者的表面上产生同时的循环信号。 这些测量可以用MBSI算法系统地处理以估计血管通道动力学和中心血液动力学，诸如动脉血压（ABP）和心输出量（CO）。尽管对象的生理状况在延长的监测时段期间频繁地改变，但是MBSI方法可以识别真实的时间中的改变，并且使用所识别的通道动力学来将所观察到的外围传感器信号正确地转换为仅通过侵入性导管插入术测量的深部体信号，诸如CO。在本计画中，我们将建立以MBSI为基础的心血管监测的理论基础，发展一个可穿戴式生物感测器网路的原型，其具有非侵入性、低功率、小型化的生物感测器，并借由一个无线的ad hoc网路来连结，以及发展三个主要的临床应用; 1）使用非侵入式传感器的长期心输出量估计，2）使用可穿戴传感器的连续动脉血压监测，3）外周动脉病变的诊断，如局灶性动脉粥样硬化和夹层。该项目的高度跨学科性质和国际合作部分为本科生和研究生提供了独特的教育机会。

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.