Advanced Wearable Cardiovascular Monitoring Platform

本研究的目标是开发一种可穿戴心脏健康监测系统，该系统对运动伪影具有鲁棒性。目前，大多数可穿戴心脏监测器检测心脏的电活动，即心电图信号。然而，心脏的机械活动也可以揭示有关个人健康状况的宝贵信息。商业运动传感器，即加速度计和陀螺仪，可以通过记录胸壁上的心跳引起的振动来检测这些活动。然而，所记录的振动经常被来自心脏以外的源（诸如对象的运动）的运动噪声污染。这项研究提出了一种新的硬件设计和算法开发的组合，以解决这个问题，以及提供更先进的心脏健康状态指标相比，目前最先进的可穿戴传感器。一系列运动传感器将嵌入可穿戴的带子中，并放置在胸壁周围。该算法利用传感器阵列提供的额外信息来消除记录中的运动噪声。其他类型的传感器（如电子和光学传感器）也将嵌入系统中，以增强心脏健康的评估并提供心力衰竭的检测。这样一个系统将是非常宝贵的家庭为基础的检测和管理的心脏病。拟议的研究将与各种教育和外联工作相结合。具体而言，PI将通过自由科学中心的“科学伙伴”计划吸引高中生，并通过史蒂文斯的夏季学者研究计划继续招募本科生，特别是女性和少数民族学生。PI还将参与向Physiobank提供数据，Physiobank是最大的在线生物物理数据库，可供生物医学研究界免费使用。本研究的目标是开发一种可穿戴的，多模态的心血管监测系统具有高鲁棒性。最近在开发非侵入式可穿戴系统，特别是监测心脏电生理学的设备方面做出了重大努力。然而，除了电气方面，还需要获得对心脏和血管的机械活动的观点，以全面评估心血管健康。可穿戴心脏机械感测领域的进展目前受到克服运动伪影的挑战的阻碍。这项研究提出了一个整体的硬件/软件解决方案，这个问题，通过实施一个阵列的惯性测量单元，放置在胸壁和记录心脏引起的胸部振动的线性和旋转分量。基于模型的信号处理算法，利用传感器阵列提供的冗余信息的优势，然后将被应用于去除运动噪声分量从心脏机械记录在嵌入式平台。本研究的第二个重点是用ECG和光电体积描记术（PPG）的更标准模态来增强所提出的心脏机械感测系统。传感器融合算法将用于多模态信号，以提取进一步的心血管特征，并提高监测过程的准确性。最后，异常检测和分类算法将分析导出的特征，以评估心血管系统的健康状况并检测心力衰竭。这项研究在鲁棒性和运动容限方面推进了心脏机械感测的状态。此外，所提出的线性和旋转心跳引起的胸壁运动的组合分析将提供对心脏机械感测定理的更深入的了解。最后，建议的噪声消除方法提供了一个嵌入式框架，从强背景噪声中提取弱生物物理信号，并可以应用于其他生物物理传感模式在类似的应用场景scenaries.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

The goal of this research is to develop a wearable heart health monitoring system that is robust to motion artifacts. Currently, most wearable heart monitors detect the electrical activity of the heart, namely the electrocardiogram signal. However, the mechanical activities of the heart can also reveal valuable information about the health status of an individual. Commercial motion sensors, i.e. accelerometers and gyroscopes, can detect these activities by recording the vibrations induced by the heartbeat on the chest wall. However, the recorded vibrations often become contaminated by motion noise arising from sources other than the heart, such as the movement of the subjects. This research proposes a novel combination of hardware design and algorithm development to solve this problem as well as to provide more advanced heart health status metrics compared to current state-of-the-art wearable sensors. An array of motion sensors will be embedded in a wearable strap and placed around the chest wall. The algorithm takes advantage of the extra information provided by the sensor array to eliminate the motion noise in the recordings. Other types of sensors (such as electrical and optical sensors) will also be embedded in the system to augment the evaluation of heart wellness and provide detection of heart failure. Such a system will be invaluable for the home-based detection and management of cardiac diseases. The proposed research will be combined with various educational and outreach efforts. Specifically, the PI will engage high school students through the Liberty Science Center's "Partners in Science" program and continue to recruit undergraduate students, especially from female and minority groups, through the Summer Scholars Research Program at Stevens. The PI will also participate in providing data to Physiobank, which is the largest biophysical database online, available for the free use of the biomedical research community. The goal of this research is to develop a wearable, multi-modal cardiovascular monitoring system with high robustness. There has been significant effort recently on the development of non-invasive wearable systems, in particular devices that monitor cardiac electrophysiology. However, in addition to the electrical aspects, a perspective on the mechanical activities of the heart and blood vessels also needs to be gained for a comprehensive evaluation of cardiovascular health. Progress in the area of wearable cardio-mechanical sensing is currently hindered by the challenge of overcoming motion artifacts. This research proposes a holistic hardware/software solution to this problem by implementing an array of inertial measurement units which are placed around the chest wall and record both the linear and rotational components of heart-induced chest vibrations. A model-based signal processing algorithm which takes advantage of the redundant information provided by the sensor array will then be applied to remove motion noise components from cardio-mechanical recordings in an embedded platform. The second thrust of this research is to augment the proposed cardio-mechanical sensing system with the more standard modalities of ECG and photoplethysmography (PPG). Sensor fusion algorithms will be used on the multi-modal signals to extract further cardiovascular features as well as increase the accuracy of the monitoring process. Finally, abnormality detection and classification algorithms will analyze the derived features to evaluate the wellness of the cardiovascular system and to detect heart failure. This research advances the state of cardio-mechanical sensing in terms of robustness and motion tolerance. Additionally, the proposed combined analysis of linear and rotational heartbeat-induced chest wall movements will provide a deeper insight into the theorem of cardio-mechanical sensing. Finally, the proposed noise cancellation approach provides an embedded framework for the extraction of weak biophysical signals from strong background noises and can be applied to other biophysical sensing modalities in similar application scenarios.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.