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
Online-Zugang:	Volltext
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