IEEE J Biomed Health Inform. 2024 May;28(5):2674-2686. doi: 10.1109/JBHI.2024.3377128. Epub 2024 May 6.
Causalityholds profound potentials to dissipate confusion and improve accuracy in cuffless continuous blood pressure (BP) estimation, an area often neglected in current research. In this study, we propose a two-stage framework, CiGNN, that seamlessly integrates causality and graph neural network (GNN) for cuffless continuous BP estimation. The first stage concentrates on the generation of a causal graph between BP and wearable features from the the perspective of causal inference, so as to identify features that are causally related to BP variations. This stage is pivotal for the identification of novel causal features from the causal graph beyond pulse transit time (PTT). We found these causal features empower better tracking in BP changes compared to PTT. For the second stage, a spatio-temporal GNN (STGNN) is utilized to learn from the causal graph obtained from the first stage. The STGNN can exploit both the spatial information within the causal graph and temporal information from beat-by-beat cardiac signals for refined cuffless continuous BP estimation. We evaluated the proposed method with three datasets that include 305 subjects (102 hypertensive patients) with age ranging from 20-90 and BP at different levels, with the continuous Finapres BP as references. The mean absolute difference (MAD) for estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 3.77 mmHg and 2.52 mmHg, respectively, which outperformed comparison methods. In all cases including subjects with different age groups, while doing various maneuvers that induces BP changes at different levels and with or without hypertension, the proposed CiGNN method demonstrates superior performance for cuffless continuous BP estimation. These findings suggest that the proposed CiGNN is a promising approach in elucidating the causal mechanisms of cuffless BP estimation and can substantially enhance the precision of BP measurement.
因果关系具有消除困惑和提高无袖带连续血压 (BP) 估计准确性的巨大潜力,而这一领域在当前研究中经常被忽视。在本研究中,我们提出了一个两阶段框架 CiGNN,它无缝地将因果关系和图神经网络 (GNN) 集成在一起,用于无袖带连续 BP 估计。第一阶段集中于从因果推断的角度生成 BP 和可穿戴特征之间的因果图,以识别与 BP 变化有因果关系的特征。这一阶段对于从因果图中识别除脉搏传输时间 (PTT) 之外的新的因果特征至关重要。我们发现这些因果特征比 PTT 更能更好地跟踪 BP 的变化。对于第二阶段,使用时空图神经网络 (STGNN) 从第一阶段获得的因果图中进行学习。STGNN 可以利用因果图中的空间信息和逐拍心信号中的时间信息,进行更精细的无袖带连续 BP 估计。我们使用包含 305 名受试者(102 名高血压患者)的三个数据集评估了所提出的方法,这些受试者年龄在 20-90 岁之间,BP 水平不同,连续 Finapres BP 作为参考。估计的收缩压 (SBP) 和舒张压 (DBP) 的平均绝对差异 (MAD) 分别为 3.77mmHg 和 2.52mmHg,优于比较方法。在包括不同年龄组的受试者的所有情况下,当进行各种引起不同水平 BP 变化的操作时,以及有或没有高血压时,所提出的 CiGNN 方法在无袖带连续 BP 估计方面表现出优越的性能。这些发现表明,所提出的 CiGNN 是阐明无袖带 BP 估计因果机制的一种很有前途的方法,可以大大提高 BP 测量的精度。