A Wearable Ultrasonic System for Automatic, Continuous, and Noninvasive Monitoring of Central Blood Pressure

用于自动、连续、无创监测中心血压的可穿戴超声波系统

• 批准号: 10631219
• 负责人: Sheng Xu
• 金额: $ 49.17万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10631219
• 关键词: Acoustics; Address; Algorithms; Anterior; Antihypertensive Agents; Arteries; Automatic Data Processing; Awareness; Benchmarking; Blood Pressure; Blood Pressure Monitors; Blood Vessels; Bluetooth; Cardiology; Cardiovascular Diseases; Cardiovascular system; Catheterization; Catheters; Cellular Phone; Clinical; Collaborations; Consumption; Data; Data Set; Dependence; Development; Devices; Diagnosis; Diameter; Electronics; Elements; Evaluation; Event; Fiber Optics; Fourier Analysis; Frequencies; Future; General Population; Grant; Health; Health Care Costs; Hour; Human; Hypertension; Image; Immobilization; Intervention; Label; Location; Machine Learning; Manuals; Maps; Measurement; Measures; Methods; Modeling; Monitor; Noise; Outcome; Participant; Patients; Performance; Peripheral; Phase; Photoplethysmography; Physics; Physiologic pulse; Polymers; Procedures; Process; Research; Research Project Grants; Resolution; Risk Assessment; Risk Management; Running; Safety; Series; Shapes; Side; Signal Transduction; Skin; Statistical Data Interpretation; Supervision; Surface; Symptoms; System; Systems Integration; Techniques; Technology; Time; Training; Transducers; Translating; Ultrasonic Transducer; Ultrasonics; United States National Institutes of Health; Validation; Work; arm; automated algorithm; blood pressure control; blood pressure reduction; cardiovascular disorder prevention; cardiovascular risk factor; clinical practice; clinically relevant; computerized data processing; data exchange; deep learning; deep learning algorithm; design; disorder risk; high throughput screening; human error; human subject; hypertension control; hypertension treatment; improved; invention; machine learning algorithm; miniaturize; monitoring device; mortality; multidisciplinary; non-invasive monitor; operation; optical fiber; peripheral blood; power consumption; prehypertension; printed circuit board; screening; skills; standard of care; tonometry; ultrasound; usability; wearable platform; wireless; wireless transmission

PROJECT SUMMARY This proposed project aims to develop a wearable ultrasonic patch for automatic, continuous, and noninvasive central blood pressure monitoring. Hypertension is a “silent killer” since it gradually induces a series of cardiovascular diseases, usually without any obvious symptoms. Compared with brachial blood pressure acquired by the cuff, blood pressure at central locations has been proven to be more valuable for predicting future cardiovascular events and disease risks. Thus, it is essential to measure central blood pressure regularly to maximize the blood pressure management outcome. This research is distinct from all other existing methods for central blood pressure measurements that have a series of different technical challenges, such as the use of invasive procedures, insufficient accuracy, and significant dependence on the operator’s skills. In 2018, the PI’s group invented the first wearable ultrasonic device for central blood pressure recording, supported by an R21 grant from NIH. However, this device still needs cables for power and data transfer, manual processing of the data, and a low signal-to-noise ratio. In this proposed project, we will demonstrate a wearable ultrasonic transducer array integrated with miniaturized wireless control electronics and automatic data processing algorithms. The integrated control electronics will eliminate the bulky setup and connecting wires. The phased-array beamforming will focus and steer the ultrasound beam to search the blood vessels and improve the signal-to-noise ratio and the spatial resolution. All data acquired by the wearable ultrasonic device will be wirelessly transmitted to a terminal receiver (e.g., a smartphone), where a deep learning based algorithm is running for further data processing. The customized algorithm will automatically localize the targeted artery and track both the anterior and posterior walls of the vessel without the supervision of human operators. Thereby, the operator dependence in conventional ultrasound systems will be eliminated, and device usability can be much improved. Finally, the proposed entire system will be validated on patients against arterial catheterization, the current gold standard for central blood pressure monitoring in clinical settings. If successful, this proposed study will provide patients with a device that can access their central blood pressure automatically, continuously, and noninvasively. Using a stretchable platform that matches the softness of the human skin will make a key difference in patient acceptance and monitoring outcomes. The ease of measurements enabled by the automatic algorithm can significantly help high-throughput screening of central blood pressure in the general population and guide the development of antihypertensive drugs, which can eventually translate to a significant reduction in blood pressure associated mortality and healthcare costs.

项目摘要 这个拟议的项目旨在开发可穿戴的超声贴片，以自动，连续和无创 中央血压监测。高血压是“沉默杀手”，因为它逐渐引起了一系列 心血管疾病通常没有明显的症状。与臂血压相比 被袖口收购，中央位置的血压已被证明对预测更有价值 未来的心血管事件和疾病风险。那是必不可少的 最大化血压管理结果。这项研究与所有其他现有方法不同 对于具有一系列不同技术挑战的中央血压测量值，例如使用 侵入性程序，准确性不足以及对操作员技能的显着依赖。在2018年，PI 小组发明了第一个用于中央血压记录的可穿戴超声设备，由R21支持 NIH的赠款。但是，该设备仍需要电缆以进行电源和数据传输，手动处理 数据和低信噪比。在这个拟议的项目中，我们将展示可穿戴的超声波 与微型无线控制电子和自动数据处理集成的传感器阵列 算法。集成的控制电子将消除笨重的设置和连接电线。这 阶梯式阵列横梁的形成将聚焦并阻止超声梁以搜索血管并改善 信噪比和空间分辨率。可穿戴超声设备获得的所有数据将是 无线传输到终端接收器（例如智能手机），其中深度学习算法为 运行以进行进一步的数据处理。自定义算法将自动定位目标动脉 在没有人类操作员的监督的情况下，跟踪容器的前壁和后壁。从而， 将消除常规超声系统中的操作员依赖性，设备可用性可以是 改进了很多。最后，拟议的整个系统将对患者进行针对伪影导管插入的患者的验证， 当前用于临床环境中中央血压监测的金标准。如果成功，这提出了 研究将为患者提供可以自动进入中心血压的设备，继续，继续 而且无创。使用与人类皮肤柔软度相匹配的可拉伸平台将成为钥匙 患者接受和监测结果的差异。由 自动算法可以显着帮助一般中枢性血压的高通量筛查 人口和指导降压药的发展，有时可以转化为重要 血压降低相关的死亡率和医疗保健成本。