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

Lei Liu, Huiqi Lu, Maxine Whelan, Yifan Chen, Xiaorong Ding

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
22 Downloads (Pure)

Abstract

Causality holds 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.
Original languageEnglish
Pages (from-to)2674-2686
Number of pages13
JournalIEEE Journal of Biomedical and Health Informatics
Volume28
Issue number5
Early online date13 Mar 2024
DOIs
Publication statusPublished - May 2024

Bibliographical note

This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/CC BY-NC-ND/4.0/),which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited..

Keywords

  • Biomedical monitoring
  • Causality
  • Electrocardiography
  • Estimation
  • Feature extraction
  • Graph neural networks
  • Inference algorithms
  • Physiology
  • amplitude alteration
  • cuffless continuous blood pressure
  • pulse transit time
  • spatio-temporal graph neural network

ASJC Scopus subject areas

  • Health Information Management
  • Health Informatics
  • Electrical and Electronic Engineering
  • Computer Science Applications

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