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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:
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.
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
Core Tip

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.