Zhao Lei, Liang Cunman, Huang Yan, Zhou Guodong, Xiao Yiqun, Ji Nan, Zhang Yuan-Ting, Zhao Ni
Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China.
Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China.
NPJ Digit Med. 2023 May 22;6(1):93. doi: 10.1038/s41746-023-00835-6.
Cardiovascular diseases (CVDs) are a leading cause of death worldwide. For early diagnosis, intervention and management of CVDs, it is highly desirable to frequently monitor blood pressure (BP), a vital sign closely related to CVDs, during people's daily life, including sleep time. Towards this end, wearable and cuffless BP extraction methods have been extensively researched in recent years as part of the mobile healthcare initiative. This review focuses on the enabling technologies for wearable and cuffless BP monitoring platforms, covering both the emerging flexible sensor designs and BP extraction algorithms. Based on the signal type, the sensing devices are classified into electrical, optical, and mechanical sensors, and the state-of-the-art material choices, fabrication methods, and performances of each type of sensor are briefly reviewed. In the model part of the review, contemporary algorithmic BP estimation methods for beat-to-beat BP measurements and continuous BP waveform extraction are introduced. Mainstream approaches, such as pulse transit time-based analytical models and machine learning methods, are compared in terms of their input modalities, features, implementation algorithms, and performances. The review sheds light on the interdisciplinary research opportunities to combine the latest innovations in the sensor and signal processing research fields to achieve a new generation of cuffless BP measurement devices with improved wearability, reliability, and accuracy.
心血管疾病（CVDs）是全球主要的死亡原因之一。为了对心血管疾病进行早期诊断、干预和管理，非常希望在人们的日常生活（包括睡眠时间）中频繁监测血压（BP），血压是与心血管疾病密切相关的生命体征。为此，作为移动医疗倡议的一部分，近年来可穿戴式和无袖带血压提取方法得到了广泛研究。本综述重点关注可穿戴式和无袖带血压监测平台的使能技术，涵盖新兴的柔性传感器设计和血压提取算法。基于信号类型，传感设备分为电学、光学和机械传感器，并简要回顾了每种类型传感器的最新材料选择、制造方法和性能。在综述的模型部分，介绍了用于逐搏血压测量和连续血压波形提取的当代算法血压估计方法。对基于脉搏传输时间的分析模型和机器学习方法等主流方法在输入模态、特征、实现算法和性能方面进行了比较。该综述揭示了跨学科研究机会，以结合传感器和信号处理研究领域的最新创新成果，实现新一代具有更高可穿戴性、可靠性和准确性的无袖带血压测量设备。
