Towards accurate estimation of cuffless and continuous blood pressure using multi-order derivative and multivariate photoplethysmogram features
Towards accurate estimation of cuffless and continuous blood pressure using multi-order derivative and multivariate photoplethysmogram features
复制标题
使用多阶导数和多元光电体积描记图特征准确估计无袖血压和连续血压
DOI：
10.1016/j.bspc.2020.102198
复制
发表时间：
2021-01-01
影响因子：
5.1
通讯作者：
Li, Guanglin
中科院分区：
文献类型：
--
作者：
Lin, Wan-Hua;Chen, Fei;Li, Guanglin
目的：无创估计无袖带连续血压（CC - BP）对心血管疾病的预防和诊断具有重要意义。在估计CC - BP方面已经做出了很多努力，但由于当前算法的准确性和可用性有限，仍然无法满足实际应用。虽然之前的大多数研究使用混合脉搏波传导时间（PAT）和光电容积脉搏波（PPG）的特征来估计CC - BP，但本研究探讨了仅使用PPG特征是否能够有效且准确地估计CC - BP。
方法：使用来自重症监护病房109名患者的PPG信号提取65个特征用于CC - BP估计。为了进行比较，还从PPG和心电图中提取了两个先前报道的包含PAT和PPG指标的混合特征集。采用一种常用的多元线性回归算法进行CC - BP估计。为了提高CC - BP估计的可用性，开发了一种特征选择方法，根据65个PPG特征在CC - BP估计中的重要性和稳定性，从其中选择最关键且具有代表性的子集。
结果：我们的结果表明，与混合PAT和PPG指标相比，基于65个PPG特征的CC - BP估计准确性显著较高（P < 0.05）。当使用所选的关键的13个PPG特征子集时，也可以获得与混合PAT和PPG特征集相当的估计准确性。
结论：仅基于PPG特征的CC - BP估计算法将为估计CC - BP提供一种便捷的方法，且具有相当的准确性，但应优化个性化校准。
Objective: Noninvasive estimation of cuffless and continuous blood pressure (CC-BP) is important for prevention and diagnosis of cardiovascular diseases. Many efforts have been made to estimate CC-BP, but current algorithms still dissatisfy the practical applications due to their limited accuracy and usability. While most previous studies used the features of hybrid pulse arrival time (PAT) and photoplethysmogram (PPG) for CC-BP estimation, this study investigated whether only using PPG features can estimate CC-BP efficiently and accurately.Methods: The PPG signals from 109 patients of the intensive care units were used to extract 65 features for CC-BP estimation. For comparison purpose, two previously reported hybrid feature sets with PAT and PPG indicators were also extracted from the PPG and electrocardiogram. A commonly used multiple linear regression algorithm was adopted for CC-BP estimation. To increase the usability of CC-BP estimation, a feature selection method was developed to choose the most critical and representative subset from the 65 PPG features based on their importance and stability in CC-BP estimation.Results: Our results demonstrated that the accuracy of the CC-BP estimation from 65 PPG features was significantly high in comparison to that of the hybrid PAT and PPG indicators (P < 0.05). When using the subset of the selected critical 13 PPG features, a comparable estimation accuracy could also be achieved as the hybrid PAT and PPG feature sets.Conclusion: The CC-BP estimation algorithm only based on PPG features would provide a convenient way to estimate CC-BP with a comparable accuracy, but personalized calibration should be optimized.