Li Peihao, Laleg-Kirati Taous-Meriem
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2683-2686. doi: 10.1109/EMBC44109.2020.9176849.
In this paper, photoplethysmogram (PPG) features are combined with supervised machine learning algorithms to estimate arterial blood pressure (ABP). Three algorithms for the estimation of cuffless ABP using PPG signals are compared. Since PPG signals are measured non-invasively, this method guarantees an individuals comfort while not omitting important ABP information. The proposed framework predicts the ABP values by processing PPG signals with semi-classical signal analysis (SCSA) method, extracting several categories of features, which reflect the PPG signal morphology variations. Then, regression algorithms are selected for the ABP estimation. The proposed method is evaluated based on a virtual dataset with more than four thousand subjects and MIMIC II database with over eight thousand subjects for model training and testing. Mean average error (MAE) and standard deviation (STD) are evaluated for different machine learning algorithms during the prediction and estimation process. Multiple linear regression (MLR) meets the AAMI standard in terms of estimation accuracy, which proves that the ABP can be accurately estimated in a nonintrusive fashion. Given the easy implementation of the ABP estimation method, we regard that the proposed features and machine learning algorithms for the cuffless estimation of the ABP can potentially provide the means for mobile healthcare equipment to monitor the ABP continuously.
在本文中，光电容积脉搏波描记图（PPG）特征与监督式机器学习算法相结合，以估计动脉血压（ABP）。比较了三种使用PPG信号估计无袖带ABP的算法。由于PPG信号是通过非侵入性方式测量的，这种方法在不遗漏重要ABP信息的同时保证了个体的舒适度。所提出的框架通过使用半经典信号分析（SCSA）方法处理PPG信号、提取几类反映PPG信号形态变化的特征来预测ABP值。然后，选择回归算法进行ABP估计。基于一个包含四千多名受试者的虚拟数据集和一个包含八千多名受试者的MIMIC II数据库对所提出的方法进行评估，用于模型训练和测试。在预测和估计过程中，针对不同的机器学习算法评估平均绝对误差（MAE）和标准差（STD）。多元线性回归（MLR）在估计准确性方面符合AAMI标准，这证明可以以非侵入性方式准确估计ABP。鉴于ABP估计方法易于实现，我们认为所提出的用于无袖带ABP估计的特征和机器学习算法有可能为移动医疗设备持续监测ABP提供手段。
