A Novel Automated Blood Pressure Estimation Algorithm Using Sequences of Korotkoff Sounds.

Argha, A; Celler, BG; Lovell, NH

A Novel Automated Blood Pressure Estimation Algorithm Using Sequences of Korotkoff Sounds.

Argha, A

Celler, BG Lovell, NH

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE J Biomed Health Inform, 2021, 25, (4), pp. 1257-1264
Issue Date:: 2021-04

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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	2022-02-25T02:51:52Z
dc.date.available	2022-02-25T02:51:52Z
dc.date.issued	2021-04
dc.identifier.citation	IEEE J Biomed Health Inform, 2021, 25, (4), pp. 1257-1264
dc.identifier.issn	2168-2194
dc.identifier.issn	2168-2208
dc.identifier.uri	http://hdl.handle.net/10453/154844
dc.description.abstract	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, a few automated NIBP measurement methods have been developed based on the auscultatory technique. 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 proposes a novel automated AI-based technique for NIBP estimation from auscultatory waveforms (AWs) based on converting the NIBP estimation problem to a sequence-to-sequence classification problem. To do this, a sequence of segments was first formed by segmenting the AWs, and their corresponding decomposed detail, and approximation parts obtained by wavelet packet decomposition method, and extracting features from each segment. Then, a label was assigned to each segment, i.e. (i) between systolic, and diastolic segments, and (ii) otherwise, and a bidirectional long short term memory recurrent neural network (BiLSTM-RNN) was devised to solve the resulting sequence-to-sequence classification problem. Adopting a 5-fold cross-validation scheme, and using a data base of 350 NIBP recordings gave an average mean absolute error of 1.7±3.7 mmHg for systolic BP (SBP), and 3.4 ±5.0 mmHg for diastolic BP (DBP) relative to reference values. Based on the results achieved, and comparisons made with the existing literature, it is concluded that the proposed automated BP estimation algorithm based on deep learning methods, and auscultatory waveform brings plausible benefits to the field of BP estimation.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE J Biomed Health Inform
dc.relation.isbasedon	10.1109/JBHI.2020.3012567
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 11 Medical and Health Sciences
dc.subject.classification	Medical Informatics
dc.subject.mesh	Algorithms
dc.subject.mesh	Artificial Intelligence
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	Oscillometry
dc.subject.mesh	Blood Pressure
dc.subject.mesh	Algorithms
dc.subject.mesh	Artificial Intelligence
dc.title	A Novel Automated Blood Pressure Estimation Algorithm Using Sequences of Korotkoff Sounds.
dc.type	Journal Article
utslib.citation.volume	25
utslib.location.activity	United States
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	11 Medical and Health Sciences
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	2022-02-25T02:51:51Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	25
utslib.citation.issue	4

Abstract:

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, a few automated NIBP measurement methods have been developed based on the auscultatory technique. 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 proposes a novel automated AI-based technique for NIBP estimation from auscultatory waveforms (AWs) based on converting the NIBP estimation problem to a sequence-to-sequence classification problem. To do this, a sequence of segments was first formed by segmenting the AWs, and their corresponding decomposed detail, and approximation parts obtained by wavelet packet decomposition method, and extracting features from each segment. Then, a label was assigned to each segment, i.e. (i) between systolic, and diastolic segments, and (ii) otherwise, and a bidirectional long short term memory recurrent neural network (BiLSTM-RNN) was devised to solve the resulting sequence-to-sequence classification problem. Adopting a 5-fold cross-validation scheme, and using a data base of 350 NIBP recordings gave an average mean absolute error of 1.7±3.7 mmHg for systolic BP (SBP), and 3.4 ±5.0 mmHg for diastolic BP (DBP) relative to reference values. Based on the results achieved, and comparisons made with the existing literature, it is concluded that the proposed automated BP estimation algorithm based on deep learning methods, and auscultatory waveform brings plausible benefits to the field of BP estimation.

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http://hdl.handle.net/10453/154844