Artificial Intelligence Based Blood Pressure Estimation From Auscultatory and Oscillometric Waveforms: A Methodological Review.

Argha, A; Celler, BG; Lovell, NH

Artificial Intelligence Based Blood Pressure Estimation From Auscultatory and Oscillometric Waveforms: A Methodological Review.

Argha, A

Celler, BG Lovell, NH

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Rev Biomed Eng, 2022, 15, pp. 152-168
Issue Date:: 2022

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Field	Value	Language
dc.contributor.author	Argha, A https://orcid.org/0000-0002-8276-9774
dc.contributor.author	Celler, BG
dc.contributor.author	Lovell, NH
dc.date.accessioned	2023-04-12T22:20:04Z
dc.date.available	2023-04-12T22:20:04Z
dc.date.issued	2022
dc.identifier.citation	IEEE Rev Biomed Eng, 2022, 15, pp. 152-168
dc.identifier.issn	1937-3333
dc.identifier.issn	1941-1189
dc.identifier.uri	http://hdl.handle.net/10453/169659
dc.description.abstract	Cardiovascular disease is known as the number one cause of death globally, with elevated blood pressure (BP) being the single largest risk factor. Hence, BP is an important physiological parameter used as an indicator of cardiovascular health. The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as they can be used without expertise and BP measurement can be performed by patients at home. Non-invasive cuff-based monitoring is the dominant method for BP measurement. While the oscillometric technique is most common, some automated NIBP measurement methods have been developed based on the auscultatory technique. By utilizing (relatively) large BP data annotated by experts, models can be trained using machine learning and statistical concepts to develop novel NIBP estimation algorithms. Amongst artificial intelligence (AI) techniques, deep learning has received increasing attention in different fields due to its strength in data classification and feature extraction problems. This paper reviews AI-based BP estimation methods with a focus on recent advances in deep learning-based approaches within the field. Various architectures and methodologies proposed todate are discussed to clarify their strengths and weaknesses. Based on the literature reviewed, deep learning brings plausible benefits to the field of BP estimation. We also discuss some limitations which can hinder the widespread adoption of deep learning in the field and suggest frameworks to overcome these challenges.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Rev Biomed Eng
dc.relation.isbasedon	10.1109/RBME.2020.3040715
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0903 Biomedical Engineering
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Auscultation
dc.subject.mesh	Blood Pressure
dc.subject.mesh	Blood Pressure Determination
dc.subject.mesh	Humans
dc.subject.mesh	Oscillometry
dc.subject.mesh	Humans
dc.subject.mesh	Blood Pressure Determination
dc.subject.mesh	Auscultation
dc.subject.mesh	Oscillometry
dc.subject.mesh	Blood Pressure
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Auscultation
dc.subject.mesh	Blood Pressure
dc.subject.mesh	Blood Pressure Determination
dc.subject.mesh	Humans
dc.subject.mesh	Oscillometry
dc.title	Artificial Intelligence Based Blood Pressure Estimation From Auscultatory and Oscillometric Waveforms: A Methodological Review.
dc.type	Journal Article
utslib.citation.volume	15
utslib.location.activity	United States
utslib.for	0903 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-12T22:20:02Z
pubs.publication-status	Published
pubs.volume	15

Abstract:

Cardiovascular disease is known as the number one cause of death globally, with elevated blood pressure (BP) being the single largest risk factor. Hence, BP is an important physiological parameter used as an indicator of cardiovascular health. The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as they can be used without expertise and BP measurement can be performed by patients at home. Non-invasive cuff-based monitoring is the dominant method for BP measurement. While the oscillometric technique is most common, some automated NIBP measurement methods have been developed based on the auscultatory technique. By utilizing (relatively) large BP data annotated by experts, models can be trained using machine learning and statistical concepts to develop novel NIBP estimation algorithms. Amongst artificial intelligence (AI) techniques, deep learning has received increasing attention in different fields due to its strength in data classification and feature extraction problems. This paper reviews AI-based BP estimation methods with a focus on recent advances in deep learning-based approaches within the field. Various architectures and methodologies proposed todate are discussed to clarify their strengths and weaknesses. Based on the literature reviewed, deep learning brings plausible benefits to the field of BP estimation. We also discuss some limitations which can hinder the widespread adoption of deep learning in the field and suggest frameworks to overcome these challenges.

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http://hdl.handle.net/10453/169659