Deepmedi Research Institute of Technology, Deepmedi Inc., Seoul 06232, Korea.
Human Convergence Technology Group, Korea Institute of Industrial Technology (KITECH), 143 Hanggaulro, Ansan 15588, Korea.
Sensors (Basel). 2019 Jan 31;19(3):595. doi: 10.3390/s19030595.
Hypertension is a well-known chronic disease that causes complications such as cardiovascular diseases or stroke, and thus needs to be continuously managed by using a simple system for measuring blood pressure. The existing method for measuring blood pressure uses a wrapping cuff, which makes measuring difficult for patients. To address this problem, cuffless blood pressure measurement methods that detect the peak pressure via signals measured using photoplethysmogram (PPG) and electrocardiogram (ECG) sensors and use it to calculate the pulse transit time (PTT) or pulse wave velocity (PWV) have been studied. However, a drawback of these methods is that a user must be able to recognize and establish contact with the sensor. Furthermore, the peak of the PPG or ECG cannot be detected if the signal quality drops, leading to a decrease in accuracy. In this study, a chair-type system that can monitor blood pressure using polyvinylidene fluoride (PVDF) films in a nonintrusive manner to users was developed. The proposed method also uses instantaneous phase difference (IPD) instead of PTT as the feature value for estimating blood pressure. Experiments were conducted using a blood pressure estimation model created via an artificial neural network (ANN), which showed that IPD could estimate more accurate readings of blood pressure compared to PTT, thus demonstrating the possibility of a nonintrusive blood pressure monitoring system.
高血压是一种众所周知的慢性病，会引起心血管疾病或中风等并发症，因此需要通过简单的血压测量系统进行持续管理。现有的血压测量方法使用包裹式袖带，这使得患者的测量变得困难。为了解决这个问题，已经研究了通过光电体积描记图（PPG）和心电图（ECG）传感器测量的信号检测峰值压力，并利用它来计算脉搏传输时间（PTT）或脉搏波速度（PWV）的无袖带血压测量方法。然而，这些方法的一个缺点是用户必须能够识别和与传感器建立联系。此外，如果信号质量下降，PPG 或 ECG 的峰值就无法检测到，从而导致准确性降低。在这项研究中，开发了一种椅子式系统，该系统可以使用聚偏二氟乙烯（PVDF）薄膜以非侵入性的方式监测用户的血压。该方法还使用瞬时相位差（IPD）代替 PTT 作为估计血压的特征值。通过人工神经网络（ANN）创建的血压估计模型进行了实验，结果表明 IPD 可以比 PTT 更准确地估计血压读数，从而证明了非侵入式血压监测系统的可能性。
