Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China.
CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China.
Biosensors (Basel). 2022 Apr 11;12(4):234. doi: 10.3390/bios12040234.
Hypertensive patients account for about 16% to 37% of the global population, and about 9.4 million people die each year from hypertension and its complications. Blood pressure is an important indicator for diagnosing hypertension. Currently, blood pressure measurement methods are mainly based on mercury sphygmomanometers in hospitals or electronic sphygmomanometers at home. However, people's blood pressure changes with time, and using only the blood pressure value at the current moment to judge hypertension may cause misdiagnosis. Continuous blood pressure measurement can monitor sudden increases in blood pressure, and can also provide physicians with long-term continuous blood pressure changes as a diagnostic reference. In this article, we design an artificial intelligence-enhanced blood pressure monitoring wristband. The wristband's sensors are based on piezoelectric nanogenerators, with a high signal-to-noise ratio of 29.7 dB. Through the transformer deep learning model, the wristband can predict blood pressure readings, and the loss value is lower than 4 mmHg. By wearing this blood pressure monitoring wristband, we realized three days of continuous blood pressure monitoring of the subjects. The blood pressure monitoring wristband is lightweight, has profound significance for the prevention and treatment of hypertension, and has wide application prospects in medical, military, aerospace and other fields.
高血压患者约占全球人口的 16%至 37%,每年约有 940 万人死于高血压及其并发症。血压是诊断高血压的重要指标。目前,血压测量方法主要是医院的水银血压计或家庭的电子血压计。然而,人的血压随时间而变化,仅用当前时刻的血压值来判断高血压可能会导致误诊。连续血压测量可以监测血压的突然升高,还可以为医生提供长期连续的血压变化作为诊断参考。本文设计了一种人工智能增强型血压监测腕带。腕带的传感器基于压电纳米发电机,具有 29.7dB 的高信噪比。通过变压器深度学习模型,腕带可以预测血压读数,损失值低于 4mmHg。通过佩戴这种血压监测腕带,我们实现了对受试者三天的连续血压监测。这种血压监测腕带重量轻,对高血压的防治具有重要意义,在医疗、军事、航空航天等领域具有广泛的应用前景。