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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
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
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	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.
AbstractList	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. 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.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.
Author	Lu, Huiqi Whelan, Maxine Ding, Xiaorong Liu, Lei Chen, Yifan
AuthorAffiliation	Centre for Healthcare and Communities Coventry University 2706 CV1 5FB Coventry U.K School of Life Science and Technology University of Electronic Science and Technology of China 12599 Chengdu 611731 China Institute of Biomedical Engineering University of Oxford 6396 OX1 2JD Oxford U.K
AuthorAffiliation_xml	– name: School of Life Science and Technology University of Electronic Science and Technology of China 12599 Chengdu 611731 China – name: Institute of Biomedical Engineering University of Oxford 6396 OX1 2JD Oxford U.K – name: Centre for Healthcare and Communities Coventry University 2706 CV1 5FB Coventry U.K
Author_xml	– sequence: 1 givenname: Lei orcidid: 0000-0001-6362-7722 surname: Liu fullname: Liu, Lei organization: School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China – sequence: 2 givenname: Huiqi orcidid: 0000-0002-6140-3394 surname: Lu fullname: Lu, Huiqi organization: Institute of Biomedical Engineering, University of Oxford, Oxford, U.K – sequence: 3 givenname: Maxine orcidid: 0000-0002-9203-3162 surname: Whelan fullname: Whelan, Maxine organization: Centre for Healthcare and Communities, Coventry University, Coventry, U.K – sequence: 4 givenname: Yifan orcidid: 0000-0001-7645-623X surname: Chen fullname: Chen, Yifan organization: School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China – sequence: 5 givenname: Xiaorong orcidid: 0000-0002-3269-2852 surname: Ding fullname: Ding, Xiaorong email: xiaorong.ding@uestc.edu.cn organization: School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/38478458$$D View this record in MEDLINE/PubMed
BookMark	eNp9UsFu1DAQtVARLaUfgISQJS5cstiOEztcUDdqt4uqhQOcLcdxqEvWXmynqH_PbHcXlR7wZeyZ90bzxu8lOvLBW4ReUzKjlDQfPs-vljNGGJ-VpRCUyWfohNFaFowReXS404Yfo7OUbgkcCammfoGOS8mF5JU8QXetW6xWH_E5bvWU9OjyfbH0Q4hr22Pte7yIenODV3aKeoSQf4f4E891gvJl1Gv78AY8bqdhGG1KuA0-Oz-FKeH5GEKPv0ZIT9Hii5TdWmcX_Cv0fNBjsmf7eIq-X158a6-K6y-LZXt-XRjOm1yYaqirgXSVtYbK2rDG2p7xjljRkaExRA-N7eqqo8TC0abvOyoY41LUdWVEeYo-7fpupg4UGesz6FCbCHPEexW0U_9WvLtRP8KdorBjImsKHd7vO8Twa7Ipq7VLxo6j9hYkKtZUgtacVwyg755Ab8MUPehTJakYlSXnElBvH4_0d5bDnwBA7AAmhpSiHZRx-WFrMKEbFSVqawC1NYDaGkDtDQBM-oR5aP4_zpsdx8ECH-G5YKRk5R_Xbrza
CODEN	IJBHA9
CitedBy_id	crossref_primary_10_1002_widm_70024 crossref_primary_10_3390_s25113254 crossref_primary_10_1109_JSEN_2024_3468316 crossref_primary_10_1007_s00521_024_10285_0
Cites_doi	10.1161/01.cir.84.2.482 10.1109/ieeestd.2019.8859685 10.1016/j.compbiomed.2023.106900 10.1076/brhm.30.2.178.1422 10.1109/JBHI.2016.2620995 10.1038/s41598-017-11507-3 10.1016/j.artmed.2020.101919 10.2307/1912791.1969 10.1038/s41597-022-01411-5 10.1177/089443939100900106 10.23915/distill.00033.ISSN2476-0757 10.1016/j.artint.2008.08.001 10.1126/sciadv.aau4996 10.1001/jama.2013.284427 10.1161/01.CIR.93.8.1527 10.1016/S2666-7568(23)00129-0 10.1007/BF02345755 10.1016/j.bspc.2019.02.028 10.3390/s20195606 10.1016/0895-7061(94)00175-b 10.3389/fgene.2019.00524 10.1109/iembs.2005.1615827 10.1007/978-981-10-4505-9_49 10.1007/s10115-021-01621-0 10.1016/j.eng.2019.08.016 10.1016/j.bspc.2020.102301 10.1109/TBME.2015.2480679 10.1186/s12987-020-00201-8 10.1016/j.ahj.2005.02.014 10.1145/3397269 10.1146/annurev-statistics-031017-100630 10.1007/s13534-019-00096-x 10.1109/JBHI.2017.2691715 10.1016/j.bspc.2020.101870 10.7551/mitpress/7503.003.0069 10.1161/01.CIR.6.4.553 10.1056/nejmc061756
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024 2024 The Authors 2024 Authors
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024 – notice: 2024 The Authors 2024 Authors
DBID	97E ESBDL RIA RIE AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 K9. KR7 L7M L~C L~D NAPCQ P64 7X8 5PM
DOI	10.1109/JBHI.2024.3377128
DatabaseName	IEEE Xplore (IEEE) IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Materials Research Database ProQuest Computer Science Collection ProQuest Health & Medical Complete (Alumni) Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Nursing & Allied Health Premium Biotechnology and BioEngineering Abstracts MEDLINE - Academic PubMed Central (Full Participant titles)
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Materials Research Database Civil Engineering Abstracts Aluminium Industry Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Health & Medical Complete (Alumni) Ceramic Abstracts Materials Business File METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Aerospace Database Nursing & Allied Health Premium Engineered Materials Abstracts Biotechnology Research Abstracts Solid State and Superconductivity Abstracts Engineering Research Database Corrosion Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic Materials Research Database MEDLINE
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine
EISSN	2168-2208
EndPage	2686
ExternalDocumentID	PMC11100861 38478458 10_1109_JBHI_2024_3377128 10472032
Genre	orig-research Research Support, Non-U.S. Gov't Journal Article
GrantInformation_xml	– fundername: Royal Academy of Engineering Daphne Jackson Trust Fellowship grant – fundername: National Natural Science Foundation of China grantid: 82102178 funderid: 10.13039/501100001809 – fundername: Sichuan Science and Technology Program grantid: 2021YFH0179 – fundername: EPSRC Healthcare Technologies Challenge Award grantid: EP/N020774/1 – fundername: Wellcome Trust grantid: 217650/Z/19/Z funderid: 10.13039/100010269 – fundername: Wellcome Trust – fundername: ; – fundername: ; grantid: 217650/Z/19/Z – fundername: ; grantid: 82102178 – fundername: ; grantid: 2021YFH0179 – fundername: ; grantid: EP/N020774/1
GroupedDBID	0R~ 4.4 6IF 6IH 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK ACPRK AENEX AFRAH AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD ESBDL HZ~ IFIPE IPLJI JAVBF M43 O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION CGR CUY CVF ECM EIF NPM RIG 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 K9. KR7 L7M L~C L~D NAPCQ P64 7X8 5PM
ID	FETCH-LOGICAL-c449t-c5f65f0b5eec186c29eed24b0e7b0f9c0af9eb65b10eeeeacddb1722487665c73
IEDL.DBID	RIE
ISICitedReferencesCount	4
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001221547700039&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2168-2194 2168-2208
IngestDate	Tue Sep 30 17:08:59 EDT 2025 Sun Sep 28 01:15:46 EDT 2025 Sun Jun 29 15:33:08 EDT 2025 Thu Apr 03 07:02:26 EDT 2025 Sat Nov 29 04:18:36 EST 2025 Tue Nov 18 22:23:50 EST 2025 Wed Aug 27 02:05:27 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	5
Language	English
License	https://creativecommons.org/licenses/by-nc-nd/4.0 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c449t-c5f65f0b5eec186c29eed24b0e7b0f9c0af9eb65b10eeeeacddb1722487665c73
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0002-9203-3162 0000-0001-6362-7722 0000-0002-6140-3394 0000-0001-7645-623X 0000-0002-3269-2852
OpenAccessLink	https://ieeexplore.ieee.org/document/10472032
PMID	38478458
PQID	3052183448
PQPubID	85417
PageCount	13
ParticipantIDs	pubmed_primary_38478458 crossref_citationtrail_10_1109_JBHI_2024_3377128 crossref_primary_10_1109_JBHI_2024_3377128 proquest_miscellaneous_2957164452 pubmedcentral_primary_oai_pubmedcentral_nih_gov_11100861 proquest_journals_3052183448 ieee_primary_10472032
PublicationCentury	2000
PublicationDate	2024-05-01
PublicationDateYYYYMMDD	2024-05-01
PublicationDate_xml	– month: 05 year: 2024 text: 2024-05-01 day: 01
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: Piscataway
PublicationTitle	IEEE journal of biomedical and health informatics
PublicationTitleAbbrev	JBHI
PublicationTitleAlternate	IEEE J Biomed Health Inform
PublicationYear	2024
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref35 ref12 ref37 ref14 ref36 ref31 ref30 ref11 ref10 ref32 ref2 ref17 Ding (ref29) 2019 ref39 ref38 ref19 ref18 Chung (ref33) 2014 Zhao (ref34) 2019 Goudet (ref25) 2017 ref24 ref23 ref45 Pearl (ref16) 2000; 19 ref26 ref20 ref42 ref41 ref22 ref44 ref43 (ref1) 2018 ref28 ref27 ref8 ref7 ref9 ref4 Shimizu (ref21) 2006; 7 ref3 ref6 Pearl (ref15) 2018 ref5 ref40
References_xml	– ident: ref45 doi: 10.1161/01.cir.84.2.482 – year: 2014 ident: ref33 article-title: Empirical evaluation of gated recurrent neural networks on sequence modeling – ident: ref40 doi: 10.1109/ieeestd.2019.8859685 – ident: ref14 doi: 10.1016/j.compbiomed.2023.106900 – ident: ref38 doi: 10.1076/brhm.30.2.178.1422 – ident: ref6 doi: 10.1109/JBHI.2016.2620995 – ident: ref28 doi: 10.1038/s41598-017-11507-3 – ident: ref11 doi: 10.1016/j.artmed.2020.101919 – ident: ref37 doi: 10.2307/1912791.1969 – ident: ref27 doi: 10.1038/s41597-022-01411-5 – ident: ref24 doi: 10.1177/089443939100900106 – ident: ref23 doi: 10.23915/distill.00033.ISSN2476-0757 – ident: ref30 doi: 10.1016/j.artint.2008.08.001 – ident: ref22 doi: 10.1126/sciadv.aau4996 – ident: ref2 doi: 10.1001/jama.2013.284427 – volume-title: World Health Statistics 2018 year: 2018 ident: ref1 – ident: ref44 doi: 10.1161/01.CIR.93.8.1527 – ident: ref3 doi: 10.1016/S2666-7568(23)00129-0 – volume: 19 volume-title: Causality: Models, Reasoning and Inference year: 2000 ident: ref16 – ident: ref7 doi: 10.1007/BF02345755 – ident: ref12 doi: 10.1016/j.bspc.2019.02.028 – ident: ref13 doi: 10.3390/s20195606 – year: 2019 ident: ref29 article-title: Feature exploration for knowledge-guided and data-driven approach based cuffless blood pressure measurement – ident: ref39 doi: 10.1016/0895-7061(94)00175-b – ident: ref19 doi: 10.3389/fgene.2019.00524 – ident: ref35 doi: 10.1109/iembs.2005.1615827 – ident: ref41 doi: 10.1007/978-981-10-4505-9_49 – ident: ref20 doi: 10.1007/s10115-021-01621-0 – ident: ref17 doi: 10.1016/j.eng.2019.08.016 – ident: ref36 doi: 10.1016/j.bspc.2020.102301 – ident: ref8 doi: 10.1109/TBME.2015.2480679 – year: 2017 ident: ref25 article-title: Causal generative neural networks – year: 2019 ident: ref34 article-title: PairNorm: Tackling oversmoothing in GNNs – ident: ref9 doi: 10.1186/s12987-020-00201-8 – ident: ref4 doi: 10.1016/j.ahj.2005.02.014 – ident: ref18 doi: 10.1145/3397269 – ident: ref31 doi: 10.1146/annurev-statistics-031017-100630 – ident: ref42 doi: 10.1007/s13534-019-00096-x – ident: ref26 doi: 10.1109/JBHI.2017.2691715 – ident: ref10 doi: 10.1016/j.bspc.2020.101870 – ident: ref32 doi: 10.7551/mitpress/7503.003.0069 – ident: ref43 doi: 10.1161/01.CIR.6.4.553 – ident: ref5 doi: 10.1056/nejmc061756 – volume: 7 start-page: 2003 issue: 10 year: 2006 ident: ref21 article-title: A linear non-Gaussian acyclic model for causal discovery publication-title: J. Mach. Learn. Res. – volume-title: The Book of Why: The New Science of Cause and Effect year: 2018 ident: ref15
SSID	ssj0000816896
Score	2.4597824
Snippet	Causalityholds profound potentials to dissipate confusion and improve accuracy in cuffless continuous blood pressure (BP) estimation, an area often neglected...
SourceID	pubmedcentral proquest pubmed crossref ieee
SourceType	Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	2674
SubjectTerms	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
Title	CiGNN: A Causality-Informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation
URI	https://ieeexplore.ieee.org/document/10472032 https://www.ncbi.nlm.nih.gov/pubmed/38478458 https://www.proquest.com/docview/3052183448 https://www.proquest.com/docview/2957164452 https://pubmed.ncbi.nlm.nih.gov/PMC11100861
Volume	28
WOSCitedRecordID	wos001221547700039&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2168-2208 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816896 issn: 2168-2194 databaseCode: RIE dateStart: 20130101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3da9swEBdrGaUvW7t1W7q2aLCngVt_SJa1tyY07cZm9rBB3owsn2igOCOJ-_f3TnJMOrrB3gQ6Ccun-5B09zvGPmYiawRAEiWUISOMTaJC1Fmkc6sKpyToxoO4flNlWcxm-kefrO5zYQDAB5_BOTX9W36zsB1dlV0QrABV_N5hO0qpkKw1XKj4ChK-HleKjQglUfSvmEmsL76Ob77gaTAV51mmFCrlfbaXoWYuBBV73zJJvsbKU-7mn1GTW2Zo-vI_F3DAXvT-Jr8MG-SQPYP2Fdv73r-ov2b3k_l1WX7ml3xiupV3yqOQogQNN23DrwnRmhOGB05ThqBxPkbb1_DpJrCLIz2fdM7dod7kBHg1b7tFt-JjCovnIQVxCfwK9UlIlTxiv6ZXPyc3UV-LIbJC6HVkpculi2sJYJMit6lG45qKOgZVx07b2DgNdS7rJMZVozZvmhp9oxTPQ3kurcresN120cI7xo2TtQKVNyDRfbDGGPI7tXDgsrg2asTiDTsq2wOVU72Mu8ofWGJdETMrYmbVM3PEPg1DfgeUjn8RHxFntggDU0bsZMP0qhfkVZVRcjPVIsFhH4ZuFEF6VzEt4L-sUi3p1CkkTvE27JFh8s0eG7Hi0e4ZCAje-3FPO7_1MN8JofkVeXL8l-99z_ZpWSH68oTtrpcdnLLn9n49Xy3PUERmxZkXkQeIFwy3
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9MwELdgoLEXPgcrDDAST0jZ8mHHMW9rta6DLuJhSHuLHOesVZpS1Db7-3dnp1WHAIk3S_5QnPvw2Xf3O8Y-ZyJrBEASJZQhI4xNokLUWaRzqwqnJOjGg7hOVVkWV1f6R5-s7nNhAMAHn8ERNb0vv5nbjp7KjglWgCp-P2SPpBBpEtK1Nk8qvoaEr8iVYiNCWRS9HzOJ9fG34eQc74OpOMoypVAt77HdDHVzIajc-9ah5Kus_Mng_D1ucusgGj_7zy08Z097i5OfBBZ5wR5A-5LtXvQ-9VfsdjQ7K8uv_ISPTLf0ZnkUkpSg4aZt-BlhWnNC8cBlyhA2zod4-jV8vA7t4jiejzrnblBzcoK8mrXdvFvyIQXG85CEuAB-iholJEvus5_j08vRJOqrMURWCL2KrHS5dHEtAWxS5DbVeLymoo5B1bHTNjZOQ53LOolx16jPm6ZG6yjFG1GeS6uy12ynnbdwwLhxslag8gYkGhDWGEOWpxYOXBbXRg1YvCZHZXuocqqYcVP5K0usKyJmRcSsemIO2JfNlF8Bp-Nfg_eJMlsDA1EG7HBN9KoX5WWVUXozVSPBaZ823SiE5FkxLeC_rFIt6d4pJC7xJvDIZvE1jw1YcY97NgMI4Pt-Tzu79kDfCeH5FXny9i_f-5E9mVxeTKvpefn9HdujLYZYzEO2s1p08J49trer2XLxwQvKHUizDxY
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=CiGNN%3A+A+Causality-Informed+and+Graph+Neural+Network+Based+Framework+for+Cuffless+Continuous+Blood+Pressure+Estimation&rft.jtitle=IEEE+journal+of+biomedical+and+health+informatics&rft.au=Liu%2C+Lei&rft.au=Lu%2C+Huiqi&rft.au=Whelan%2C+Maxine&rft.au=Chen%2C+Yifan&rft.date=2024-05-01&rft.pub=IEEE&rft.issn=2168-2194&rft.volume=28&rft.issue=5&rft.spage=2674&rft.epage=2686&rft_id=info:doi/10.1109%2FJBHI.2024.3377128&rft_id=info%3Apmid%2F38478458&rft.externalDocID=10472032
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2168-2194&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2168-2194&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2168-2194&client=summon