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World J Clin Cases. Sep 16, 2022; 10(26): 9207-9218
Published online Sep 16, 2022. doi: 10.12998/wjcc.v10.i26.9207
Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic
Sina Dami
Sina Dami, Department of Computer Engineering, West Tehran Branch, Islamic Azad University, Tehran 1468763785, Iran
Author contributions: The author contributed to the study conception and design, data analysis, figure collection and processing, the first and final draft of the manuscript; Dami S commented on previous versions of the manuscript; he read and approved the final manuscript.
Conflict-of-interest statement: There is no conflict of interest associated with the author contributed his efforts in this manuscript.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Sina Dami, PhD, Assistant Professor, Department of Computer Engineering, West Tehran Branch, Islamic Azad University, Ashrafi Esfahani Highway-End of Shahid Azari Street, Tehran 1468763785, Iran. dami@wtiau.ac.ir
Received: February 11, 2022
Peer-review started: February 11, 2022
First decision: June 7, 2022
Revised: June 19, 2022
Accepted: July 25, 2022
Article in press: July 25, 2022
Published online: September 16, 2022
Processing time: 202 Days and 15 Hours
Abstract

The coronavirus disease 2019 (COVID-19) has currently caused the mortality of millions of people around the world. Aside from the direct mortality from the COVID-19, the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients. Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality, which did not relate to COVID-19 infection. It has in fact increased the risk of death in cardiovascular disease (CVD) patients. For this purpose, it is dramatically inevitable to monitor CVD patients’ vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death. Internet of things (IoT) and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’ data. The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments. To this end, this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments. Experimental results showed that the proposed method was able to detect cardiovascular events with better performance (95.30% average sensitivity and 95.94% mean prediction values).

Keywords: Health monitoring; Early detection; Cardiovascular events; COVID-19 Pandemic; Internet of things

Core Tip: This paper has focused on presenting a health monitoring system for cardiovascular disease patients during coronavirus disease 2019 pandemic. For this purpose, a new framework for early detection of cardiovascular events was proposed based on a deep learning architecture in internet of things environments. The proposed method has provided a peaceful solution for limited scalability and late detection of cardiovascular events by enabling latency-sensitive surveillance and computing of large amounts of patients’ data.