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.

项目总结