Cardiovascular disease (CVD) is the most important cause of human death, of which hypertension is the most common chronic disease in people's life and is one of the most important risk factors for CVD. With the socio-economic development and accelerating population aging and urbanization, hypertension is on the rise. According to research, the presymptoms of hypertension are not obvious, and a considerable portion of patients do not have any uncomfortable clinical symptom such as dizziness, headache, and shortness of breath. When blood pressure is elevated for a long time and exceeds the normal range, this may result in serious complications and even threaten life safety. Therefore, accurate blood pressure monitoring is crucial for early diagnosis and intervention treatment. However, compared with the single point in time blood pressure detection of traditional cuff-type electronic blood pressure monitors, continuous dynamic monitoring can more truly reflect the real-time changes in blood pressure and dynamic trends, providing more comprehensive and accurate data. The human pulse signal contains a large amount of physiological and pathological information related to the cardiovascular system, and continuous blood pressure monitoring can be realized by accurately extracting the characteristic parameters and building a blood pressure prediction model. Currently, the main method of pulse signal detection is the PPG method, whose major drawbacks are high power consumption, sensitivity to ambient light and pressure perturbation, and susceptibility of electronic components to electromagnetic wave interference. As a result, it is impossible to measure blood pressure simultaneously in special environments such as MRI and CT. Thus, we propose a fiber-optic blood pressure sensor with continuous accurate measurement and without spatial alignment based on the microstructural setup of a reflective microfiber coupler, which is achieved by combining dual-channel pulse wave acquisition and machine-learning model prediction. This electromagnetic interference-resistant, wearable, and continuous blood pressure monitoring system will play an important role in human CVD prevention in the future.

First, two single-mode fibers twisted around each other are drawn into a microfiber coupler using the flame fusion taper method, and the reflective coupler is formed by cutting flat at the section of the waist region area, which has a diameter of 5 μm and a length of 10 mm. The device is encapsulated between an epoxy resin substrate and two layers of PDMS circular films, where the substrate is a through-hole structure, the upper PDMS layer is a circular film with a diameter of 15 mm and a thickness of 100 μm, and the lower PDMS is a raised spherical structure with a diameter of 10 mm and a height of 1.5 mm. Particularly, this structure can improve the detection sensitivity and reduce the sensitivity of the sensing area to the spatial location. Then, a dual-channel pulse wave detection system is set up to obtain the brachial artery transit time (BPTT), the radial artery transit time (RPTT), and the transit time difference between the radial artery and brachial artery (DBRPTT). Finally, the support vector regression algorithm is utilized to build a blood pressure prediction model to realize continuous and accurate blood pressure detection.

The mechanical simulation results of the packaging structure show that it can sense micro-pressure from multiple directions, reducing its dependence on the detection position (Figs. 2-3). In the static pressure experiment, the detection sensitivity is -0.682 kPa^-1 in the range of 500-1000 Pa. The sensor can respond immediately at the moment of loading and unloading pressure, with the response time of 35 ms and 46 ms respectively. Additionally, the durability and repeatability of the sensor are also tested. After 2500 cycles of the periodic pressure with a frequency of 5 Hz and a size of 1 N, the sensor still shows good response and excellent repeatability. After about 5000 cycles, the response amplitude drops by about 5% from the beginning. Since the time for sensing to measure pulse is short (about five seconds), less impact is exerted on later blood pressure prediction. When the sensor is placed at different positions in the radial artery area, the sensor can effectively detect high-fidelity pulse signals, indicating that there are no strict alignment requirements between the sensor and the artery (Fig. 5). By employing a dual-channel sensing system, the pulse waveforms at the radial artery and brachial artery are collected simultaneously. Three PTT (BPTT, RPTT, and DBRPTT) characteristic parameters (Fig. 6) are extracted from these sample data to build a blood pressure prediction model. The correlation diagram and Bland-Altman diagram reveal that both the true and the predicted values are negatively correlated with the K value. The correlation coefficient R values of SBP and DBP are 0.96 and 0.95 respectively, which indicates that there is a good positive correlation between the reference and predicted values. The mean difference value and SD value of SBP are 0.08 mmHg and 1.13 mmHg respectively, and the mean difference value and SD value of DBP are -0.35 mmHg and 1.25 mmHg respectively (Fig. 11). These indicators are both lower than the AAMI standard [(5±8) mmHg]. The performance comparison results between the sensor and other blood pressure sensors show that the sensor features an extremely compact structure, high sensitivity, sound stability, long service life, and anti-electromagnetic interference. Finally, a volunteer is randomly selected to collect 14 sets of data from 8:00 to 21:00 a day to verify the feasibility of the sensor. The results demonstrate that the normal pattern of“two peaks and one trough”is blood pressure trends. Another volunteer receives continuous monitoring during a mixed exercise of squatting and jogging. As the exercise time increases, both SBP and DBP rise but remain stable after about ten minutes (Fig. 12). This shows that the proposed blood pressure monitoring system can continuously and effectively monitor the health level of blood pressure.

We develop a reflective optical microfiber coupler sensor chip (R-OMCSC) for cardiovascular health assessment of accurate and continuous blood pressure monitoring. The R-OMCSC exhibits performance with high sensitivity and detection pulse wave without spatial alignment, which allows for perceiving weak physiological signals. Embedding the sensor into a sports wristband, we construct a dual-channel pulse wave detection system, obtain the RPTT, DPTT, and DBRPTT values, and build an SVR prediction model. Experimental results show that the system can achieve continuous blood pressure monitoring. In the future, we will keep improving the integration of the photoelectric signal processing system with the proposed dual-channel R-OMCSC pulse wave sensor, and a large amount of data will be collected for more accurate analysis. The proposed non-invasive BP detection system features high accuracy and continuous monitoring and will have the opportunity to be employed for clinical applications and thus help patients with CVD prevention.