School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Department of Biomedical Engineering, City University of Hong Kong, 999077, Hong Kong, China; Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE) at Hong Kong Science and Technology Park, 999077, Hong Kong, China.
Comput Biol Med. 2023 Jun;159:106900. doi: 10.1016/j.compbiomed.2023.106900. Epub 2023 Apr 12.
Enabled by wearable sensing, e.g., photoplethysmography (PPG) and electrocardiography (ECG), and machine learning techniques, study on cuffless blood pressure (BP) measurement with data-driven methods has become popular in recent years. However, causality has been overlooked in most of current studies. In this study, we aim to examine the feasibility of causal inference for cuffless BP estimation. We first attempt to detect wearable features that are causally related, rather than correlated, to BP changes by identifying causal graphs of interested variables with fast causal inference (FCI) algorithm. With identified causal features, we then employ time-lagged link to integrate the mechanism of causal inference into the BP estimated model. The proposed method was validated on 62 subjects with their continuous ECG, PPG and BP signals being collected. We found new causal features that can better track BP changes than pulse transit time (PTT). Further, the developed causal-based estimation model achieved an estimation error of mean absolute difference (MAD) being 5.10 mmHg and 2.85 mmHg for SBP and DBP, respectively, which outperformed traditional model without consideration of causality. To the best of our knowledge, this work is the first to study the causal inference for cuffless BP estimation, which can shed light on the mechanism, method and application of cuffless BP measurement.
得益于可穿戴传感器，例如光电容积脉搏波（PPG）和心电图（ECG），以及机器学习技术，近年来，基于数据驱动方法的无袖带血压（BP）测量研究变得非常热门。然而，在大多数当前研究中，因果关系被忽视了。在这项研究中，我们旨在检验无袖带 BP 估计的因果推理的可行性。我们首先尝试通过使用快速因果推理（FCI）算法识别感兴趣变量的因果图，来检测与血压变化具有因果关系而不是相关关系的可穿戴特征。有了识别出的因果特征，我们然后使用时间滞后链接将因果推理的机制集成到 BP 估计模型中。该方法在 62 名受试者上进行了验证，连续采集了他们的心电图、PPG 和 BP 信号。我们发现了一些新的因果特征，它们比脉搏传输时间（PTT）更能跟踪血压变化。此外，开发的基于因果关系的估计模型实现了平均绝对差（MAD）的估计误差，收缩压和舒张压分别为 5.10mmHg 和 2.85mmHg，优于没有考虑因果关系的传统模型。据我们所知，这是首次研究无袖带 BP 估计的因果推理，这可以为无袖带 BP 测量的机制、方法和应用提供启示。
