Heart rate variability for medical decision support systems: A review
Article
Article Title | Heart rate variability for medical decision support systems: A review |
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ERA Journal ID | 5040 |
Article Category | Article |
Authors | Faust, Oliver, Hong, Wanrong, Loh, Hui Wen, Xu, Shuting, Tan, Ru-San, Chakraborty, Subrata, Barua, Prabal Datta, Molinari, Filippo and Acharya, U. Rajendra |
Journal Title | Computers in Biology and Medicine |
Journal Citation | 145 |
Article Number | 105407 |
Number of Pages | 17 |
Year | 2022 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0010-4825 |
1879-0534 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2022.105407 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0010482522001998 |
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. |
Keywords | Artificial intelligence; Computer-aided diagnosis; Heart rate variability; Patient remote monitoring |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Sheffield Hallam University, United Kingdom |
Cogninet Australia, Australia | |
Singapore University of Social Sciences (SUSS), Singapore | |
National Heart Centre, Singapore | |
Duke-NUS Medical School, Singapore | |
University of New England | |
University of Technology Sydney | |
School of Business | |
Polytechnic University of Turin, Italy | |
Ngee Ann Polytechnic, Singapore | |
Asia University, Taiwan |
https://research.usq.edu.au/item/yywq9/heart-rate-variability-for-medical-decision-support-systems-a-review
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