Heart rate variability for medical decision support systems: A review.

Faust, O; Hong, W; Loh, HW; Xu, S; Tan, R-S; Chakraborty, S; Barua, PD; Molinari, F; Acharya, UR

Heart rate variability for medical decision support systems: A review.

Faust, O Hong, W Loh, HW Xu, S Tan, R-S Chakraborty, S

Barua, PD Molinari, F Acharya, UR

Permalink

Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Comput Biol Med, 2022, 145, pp. 105407
Issue Date:: 2022-06

Closed Access

	Filename	Description	Size
	1-s2.0-S0010482522001998-main.pdf		5.01 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Faust, O
dc.contributor.author	Hong, W
dc.contributor.author	Loh, HW
dc.contributor.author	Xu, S
dc.contributor.author	Tan, R-S
dc.contributor.author	Chakraborty, S https://orcid.org/0000-0002-0102-5424
dc.contributor.author	Barua, PD
dc.contributor.author	Molinari, F
dc.contributor.author	Acharya, UR
dc.date.accessioned	2023-03-27T02:38:58Z
dc.date.available	2022-03-12
dc.date.available	2023-03-27T02:38:58Z
dc.date.issued	2022-06
dc.identifier.citation	Comput Biol Med, 2022, 145, pp. 105407
dc.identifier.issn	0010-4825
dc.identifier.issn	1879-0534
dc.identifier.uri	http://hdl.handle.net/10453/168543
dc.description.abstract	Heart Rate Variability (HRV) is a good predictor of human health because the heart rhythm is modulated by a wide range of physiological processes. This statement embodies both challenges to and opportunities for HRV analysis. Opportunities arise from the wide-ranging applicability of HRV analysis for disease detection. The availability of modern high-quality sensors and the low data rate of heart rate signals make HRV easy to measure, communicate, store, and process. However, there are also significant obstacles that prevent a wider use of this technology. HRV signals are both nonstationary and nonlinear and, to the human eye, they appear noise-like. This makes them difficult to analyze and indeed the analysis findings are difficult to explain. Moreover, it is difficult to discriminate between the influences of different complex physiological processes on the HRV. These difficulties are compounded by the effects of aging and the presence of comorbidities. In this review, we have looked at scientific studies that have addressed these challenges with advanced signal processing and Artificial Intelligence (AI) methods.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Comput Biol Med
dc.relation.isbasedon	10.1016/j.compbiomed.2022.105407
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 11 Medical and Health Sciences
dc.subject.classification	Biomedical Engineering
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Electrocardiography
dc.subject.mesh	Heart Rate
dc.subject.mesh	Humans
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.subject.mesh	Humans
dc.subject.mesh	Electrocardiography
dc.subject.mesh	Heart Rate
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Electrocardiography
dc.subject.mesh	Heart Rate
dc.subject.mesh	Humans
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.title	Heart rate variability for medical decision support systems: A review.
dc.type	Journal Article
utslib.citation.volume	145
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 Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-27T02:38:56Z
pubs.publication-status	Published
pubs.volume	145

Abstract:

Heart Rate Variability (HRV) is a good predictor of human health because the heart rhythm is modulated by a wide range of physiological processes. This statement embodies both challenges to and opportunities for HRV analysis. Opportunities arise from the wide-ranging applicability of HRV analysis for disease detection. The availability of modern high-quality sensors and the low data rate of heart rate signals make HRV easy to measure, communicate, store, and process. However, there are also significant obstacles that prevent a wider use of this technology. HRV signals are both nonstationary and nonlinear and, to the human eye, they appear noise-like. This makes them difficult to analyze and indeed the analysis findings are difficult to explain. Moreover, it is difficult to discriminate between the influences of different complex physiological processes on the HRV. These difficulties are compounded by the effects of aging and the presence of comorbidities. In this review, we have looked at scientific studies that have addressed these challenges with advanced signal processing and Artificial Intelligence (AI) methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/168543