Technol Health Care. 2020;28(S1):217-227. doi: 10.3233/THC-209022.
Blood pressure (BP) is currently diagnosed by cuff-based devices, which are inconvenient and provide discontinuous measurements. Photoplethysmography (PPG)-based cuffless techniques have recently been used to accurately estimate both systolic BP (SBP) and diastolic BP (DBP). However, it is difficult to use these SBP and DBP estimations to reflect the personalized traits in the peripheral vascular condition; thus, their accuracy is limited.
The purpose of this study is to describe a technique that can be distinguished simply among three BP categories (normotensive, prehypertensive, and hypertensive) and reflect individual traits using PPG only.
We measured BP over 120 s using the fingers of 105 subjects. The PPG waveforms varied in size and amplitude over time. Therefore, normalization for uniform features for individual traits was done after the extracted waveforms were divided into multiple windows. The feature is determined by the lowest amplitude in the waveform within each divided window. The features have been applied to distinguish three BP categories using the first-eigenvector (1-EV) and second-eigenvector (2-EV) in linear discriminant analysis.
The best decision boundary for each BP category was estimated using 1-EV (-0.02 to +0.02) and 2-EV (>+0.02) in the hypertensive category, 1-EV (< 0) and 2-EV (⩽+0.02) in the prehypertensive category, and 1-EV (⩾-0.02) and 2-EV (⩽+0.02) in the normotensive category. The overlap range with 1-EV (-0.02 to 0) and 2-EV (⩽+0.02) in particular accurately reflected individual traits.
Discrimination among the three BP categories reflecting individual traits was successfully achieved using PPG. This method could improve limitations of cuff-based techniques.
目前,血压(BP)是通过基于袖带的设备来诊断的,这些设备既不方便,又提供不连续的测量。基于光电容积脉搏波(PPG)的无袖带技术最近已被用于准确估计收缩压(SBP)和舒张压(DBP)。然而,使用这些 SBP 和 DBP 估计值来反映外周血管状况的个性化特征是困难的;因此,它们的准确性是有限的。
本研究的目的是描述一种技术,该技术仅使用 PPG 即可简单地区分三种 BP 类别(正常血压、高血压前期和高血压),并反映个体特征。
我们使用 105 名受试者的手指测量了 120 秒的 BP。PPG 波形随时间变化而变化。因此,在将提取的波形分为多个窗口之后,对每个个体特征的归一化进行了统一的特征。该特征由每个分割窗口内的波形中的最低幅度确定。使用线性判别分析中的第一特征向量(1-EV)和第二特征向量(2-EV),将这些特征应用于区分三种 BP 类别。
使用 1-EV(-0.02 到 +0.02)和 2-EV(> +0.02)在高血压组,1-EV(<0)和 2-EV(⩽+0.02)在高血压前期组,以及 1-EV(⩾-0.02)和 2-EV(⩽+0.02)在正常血压组,分别估计了每个 BP 类别最佳的决策边界。特别地,1-EV(-0.02 到 0)和 2-EV(⩽+0.02)的重叠范围准确地反映了个体特征。
使用 PPG 成功实现了对反映个体特征的三种 BP 类别的区分。这种方法可以改善基于袖带技术的局限性。