CiGNN: A Causality-Informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation

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 (GN...

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Veröffentlicht in:IEEE journal of biomedical and health informatics Jg. 28; H. 5; S. 2674 - 2686
Hauptverfasser: Liu, Lei, Lu, Huiqi, Whelan, Maxine, Chen, Yifan, Ding, Xiaorong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.05.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adult
Age groups
Aged
Algorithms
Amplitude alteration
Biomedical monitoring
Blood pressure
Blood Pressure - physiology
Blood Pressure Determination - methods
Causality
cuffless continuous blood pressure
Electrocardiography
Estimation
Feature extraction
Female
Graph neural networks
Humans
Hypertension
Inference algorithms
Male
Middle Aged
Neural networks
Neural Networks, Computer
Physiology
Pressure estimation
pulse transit time
Signal Processing, Computer-Assisted
Spatial data
spatio-temporal graph neural network
Transit time
Young Adult
ISSN:2168-2194, 2168-2208, 2168-2208
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Zusammenfassung: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.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2024.3377128