Systolic blood pressure estimation using PPG and ECG during physical exercise
Systolic blood pressure estimation using PPG and ECG during physical exercise
复制标题
DOI：
10.1088/0967-3334/37/12/2154
复制
发表时间：
2016-12-01
影响因子：
3.2
通讯作者：
Aarts, R. M.
中科院分区：
文献类型：
--
作者：
Sun, S.;Bezemer, R.;Aarts, R. M.
在这项工作中，提出了一种使用光电体积描记法（PPG）和心电图（ECG）来估计收缩压（SBP）的模型。对 19 名受试者进行 40 分钟锻炼的数据进行了分析。根据容积钳原理在手指处测量参考收缩压。 PPG 信号在手指和前额处测量。在对每位受试者进行休息时的初始化过程后，该模型在整个运动期间每 30 秒估计一次 SBP。为了构建该模型，通过波形、一阶导数、二阶导数和频谱从 PPG 信号中提取了 18 个特征。此外，脉搏到达时间（PAT）是 PPG 和 ECG 相结合的一个特征。在评估了四种回归模型后，我们选择多元线性回归（MLR）来结合所有导出的特征来估计 SBP。每个特征的贡献均使用其在 MLR 中的归一化权重进行量化。为了评估模型的性能，我们使用了留一主题交叉验证。为了探索该模型的潜力，我们研究了 PAT、回归模型、测量部位（手指和额头）和姿势变化（躺着、坐着和站立）的影响。结果表明，加入 PAT 将差异的标准偏差 (SD) 从 14.07 降低到 13.52 mmHg。使用手指和额头 PPG 信号的模型之间的估计性能没有显着差异。不同的姿势需要单独的模型。在体育锻炼期间使用手指衍生的 PPG 信号的优化模型的性能平均差为 0.43 mmHg，差值 SD 为 13.52 mmHg，中位相关系数为 0.86。此外，我们还确定了与其他特征相比对 SBP 估计贡献更大的两组特征。一组由我们提出的描述节拍形态的特征组成。另一个包括描述重搏切迹的现有特征。目前的工作证明了体育锻炼期间 SBP 估计模型的良好结果。
In this work, a model to estimate systolic blood pressure (SBP) using photoplethysmography (PPG) and electrocardiography (ECG) is proposed. Data from 19 subjects doing a 40 min exercise was analyzed. Reference SBP was measured at the finger based on the volume-clamp principle. PPG signals were measured at the finger and forehead. After an initialization process for each subject at rest, the model estimated SBP every 30 s for the whole period of exercise. In order to build this model, 18 features were extracted from PPG signals by means of its waveform, first derivative, second derivative, and frequency spectrum. In addition, pulse arrival time (PAT) was derived as a feature from the combination of PPG and ECG. After evaluating four regression models, we chose multiple linear regression (MLR) to combine all derived features to estimate SBP. The contribution of each feature was quantified using its normalized weight in the MLR. To evaluate the performance of the model, we used a leave-one-subject-out cross validation. With the aim of exploring the potential of the model, we investigated the influences of the inclusion of PAT, regression models, measurement sites (finger and forehead), and posture change (lying, sitting, and standing). The results show that the inclusion of PAT reduced the standard deviation (SD) of the difference from 14.07 to 13.52 mmHg. There was no significant difference in the estimation performance between the model using finger-and foreheadderived PPG signals. Separate models are necessary for different postures. The optimized model using finger-derived PPG signals during physical exercise had a performance with a mean difference of 0.43 mmHg, an SD of difference of 13.52 mmHg, and median correlation coefficients of 0.86. Furthermore, we identified two groups of features that contributed more to SBP estimation compared to other features. One group consists of our proposed features depicting beat morphology. The other comprises existing features depicting the dicrotic notch. The present work demonstrates promising results of the SBP estimation model during physical exercise.