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Cuff-less Calibration-free Blood Pressure Estimation under Ambulatory Environment using Pulse Wave Velocity and Photoplethysmogram Signals

机译:使用脉冲波速度和光电肉测量信号在动态环境下无级耐脉冲压力估计

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This paper presents a blood pressure estimation method based on pulse wave velocity (PWV). Although there are a variety of methods based on PWV to estimate blood pressure, most of them require calibration per patient, and the patient has to remain still. The goal of our research is to develop a calibration-free blood pressure estimation method that is applicable not only during rest but also during exercise. To accomplish our goal, we extracted properties of blood vessels from photoplethysmogram (PPG) signals, and compared several regression models, such as the deductive model based on blood vessel physics equation, and the inductive model based on machine learning. Twenty-four participants performed exercise, measuring blood pressure, electrocardiogram (ECG) and PPG. The best result showed that the mean error for the estimated systolic blood pressure (SBP) against cuff-based blood pressure was 0.18 ± 8.68 mmHg. Although there was not a big difference between the regression models, PWV and Augmentation Index are effective features to estimate SBP. In addition to this, Heart Rate was effective only for the young men, and height ratio of c-wave to a-wave of acceleration pulse wave might be effective for elderly men. These results suggest that our proposed method has the potential for cuff-less calibration-free blood pressure estimation which include measurements during rest and exercise.
机译:本文介绍了一种基于脉搏波速度(PWV)的血压估计方法。虽然存在基于PWV的各种方法来估计血压,但大多数人需要每位患者校准,并且患者必须保持静止。我们的研究目的是开发一种无抗型血压估计方法,不仅适用于休息期间,而且在运动期间也适用。为了实现我们的目标,我们从光增性肌谱(PPG)信号中提取了血管的性质,并比较了几种回归模型,例如基于血管物理方程的演绎模型,以及基于机器学习的电感模型。二十四名参与者进行运动,测量血压,心电图(ECG)和PPG。最好的结果表明,估计的收缩压(SBP)抵抗基于袖带的血压的平均误差为0.18±8.68mmHg。虽然回归模型与增强指数之间没有大的差异是估计SBP的有效功能。除此之外,心率仅针对年轻人有效,而C波的高度比对于加速脉冲波的高度比对于老年人可能是有效的。这些结果表明,我们所提出的方法具有较小的无抗型血压估计的可能性,包括在休息和运动期间的测量。

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