PDF(Pubmed)
Abstract：
Accurately measuring blood pressure (BP) is essential for maintaining physiological health, which is commonly achieved using cuff-based sphygmomanometers. Several attempts have been made to develop cuffless sphygmomanometers. To increase their accuracy and long-term variability, machine learning methods can be applied for analyzing photoplethysmogram (PPG) signals. Here, we propose a method to estimate the BP during exercise using a cuffless device. The BP estimation process involved preprocessing signals, feature extraction, and machine learning techniques. To ensure the reliability of the signals extracted from the PPG, we employed the skewness signal quality index and the RReliefF algorithm for signal selection. Thereafter, the BP was estimated using the long short-term memory (LSTM)-based neural network. Seventeen young adult males participated in the experiments, undergoing a structured protocol composed of rest, exercise, and recovery for 20 min. Compared to the BP measured using a non-invasive voltage clamp-type continuous sphygmomanometer, that estimated by the proposed method exhibited a mean error of 0.32 ± 7.76 mmHg, which is equivalent to the accuracy of a cuff-based sphygmomanometer per regulatory standards. By enhancing patient comfort and improving healthcare outcomes, the proposed approach can revolutionize BP monitoring in various settings, including clinical, home, and sports environments.
摘要：
准确测量血压（BP）对于维持生理健康至关重要，这通常使用袖带式血压计来实现。已经进行了一些尝试来开发无袖带血压计。为了提高它们的准确性和长期可变性，机器学习方法可以应用于分析光电血管容积图(PPG)信号。这里,我们提出了一种使用无袖口装置估计运动过程中BP的方法。BP估计过程涉及预处理信号，特征提取,和机器学习技术。为了确保从PPG中提取的信号的可靠性，我们采用偏度信号质量指数和RReliefF算法进行信号选择。此后,BP是使用基于长期短期记忆（LSTM）的神经网络进行估计的。17名年轻的成年男性参加了实验，经历一个由休息组成的结构化协议，锻炼,和恢复20分钟。与使用无创电压钳型连续血压计测量的BP相比，所提出的方法估计的平均误差为0.32±7.76mmHg，这相当于每个监管标准的基于袖带的血压计的准确性。通过增强患者舒适度并改善医疗保健结果，所提出的方法可以彻底改变各种环境中的BP监测，包括临床，home,和运动环境。
