Ibrahim Bassem, Jafari Roozbeh
Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA.
Department of Biomedical Engineering, Texas A&M University, College Station, Texas, USA.
IEEE Biomed Circuits Syst Conf. 2018 Oct;2018. doi: 10.1109/BIOCAS.2018.8584783. Epub 2018 Dec 24.
Continuous blood pressure (BP) monitoring is essential for diagnosis and management of cardiovascular disorders. Currently, BP is measured using cuff-based methods, which are obtrusive and not suitable for continuous monitoring. Estimation of BP using pulse transit time (PTT) is a prominent method that eliminates the need for a cuff. In this paper, we present a new method to estimate BP based on PTT measurements from an array of 2×2 bio-impedance sensors placed on the wrist, which can be integrated into a small wearable device such as a smart watch for continuous BP monitoring. Diastolic and systolic BP were estimated using AdaBoost regression model based on PTT features extracted from the wrist bio-impedance signals. Data was collected from three participants using our custom bio-impedance sensors. Our method can estimate BP accurately with correlation coefficient, mean absolute error (MAE) and standard deviation (STD) of 0.92, 1.71 and 2.46 mmHg for the diastolic BP and 0.94, 2.57 and 4.35 mmHg for the systolic BP.
连续血压监测对于心血管疾病的诊断和管理至关重要。目前，血压测量采用基于袖带的方法，这种方法具有侵入性，不适用于连续监测。利用脉搏传输时间（PTT）估计血压是一种无需袖带的重要方法。在本文中，我们提出了一种基于放置在手腕上的2×2生物阻抗传感器阵列的PTT测量来估计血压的新方法，该方法可集成到如智能手表这样的小型可穿戴设备中以进行连续血压监测。基于从手腕生物阻抗信号中提取的PTT特征，使用AdaBoost回归模型估计舒张压和收缩压。使用我们定制的生物阻抗传感器从三名参与者收集数据。对于舒张压，我们的方法能够以相关系数0.92、平均绝对误差（MAE）1.71和标准差（STD）2.46 mmHg准确估计血压；对于收缩压，相关系数为0.94、平均绝对误差为2.57和标准差为4.35 mmHg。
