Heart rate variability for medical decision support systems: A review

Oliver Faust; Wanrong Hong; Hui Wen Loh; Shuting Xu; Ru-San Tan; Subrata Chakraborty; Prabal Datta Barua; Filippo Molinari; U Rajendra Acharya

doi:10.1016/j.compbiomed.2022.105407

Heart rate variability for medical decision support systems: A review

Comput Biol Med. 2022 Jun:145:105407. doi: 10.1016/j.compbiomed.2022.105407. Epub 2022 Mar 23.

Authors

Oliver Faust¹, Wanrong Hong², Hui Wen Loh³, Shuting Xu², Ru-San Tan⁴, Subrata Chakraborty⁵, Prabal Datta Barua⁶, Filippo Molinari⁷, U Rajendra Acharya⁸

Affiliations

¹ Sheffield Hallam University, Howard St, Sheffield, S1 1WB, UK.
² Cogninet Australia, Sydney, NSW, 2010, Australia.
³ School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Road, 599494, Singapore.
⁴ National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore.
⁵ School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW, 2351, Australia; Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineer and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia.
⁶ Cogninet Australia, Sydney, NSW, 2010, Australia; School of Management & Enterprise, University of Southern Queensland, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia.
⁷ Department of Electronics and Telecommunications, Politecnico di Torino, Italy.
⁸ School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Road, 599494, Singapore; Department of Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan. Electronic address: aru@np.edu.sg.

PMID: 35349801
DOI: 10.1016/j.compbiomed.2022.105407

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.

Publication types

Review

MeSH terms

Artificial Intelligence*
Electrocardiography* / methods
Heart Rate / physiology
Humans
Signal Processing, Computer-Assisted