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Zhao L, Liang C, Huang Y, Zhou G, Xiao Y, Ji N, Zhang YT, Zhao N. Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring. NPJ Digit Med 2023; 6:93. [PMID: 37217650 DOI: 10.1038/s41746-023-00835-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 05/05/2023] [Indexed: 05/24/2023] Open
Abstract
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.
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Affiliation(s)
- Lei Zhao
- 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
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Cunman Liang
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Yan Huang
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Guodong Zhou
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Yiqun Xiao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Nan Ji
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Yuan-Ting Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Ni Zhao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China.
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China.
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