An intelligent health monitoring and diagnosis system based on the internet of things and fuzzy logic for cardiac arrhythmia COVID-19 patients

Muhammad Zia Rahman; Muhammad Azeem Akbar; Víctor Leiva; Abdullah Tahir; Muhammad Tanveer Riaz; Carlos Martin-Barreiro

doi:10.1016/j.compbiomed.2023.106583

An intelligent health monitoring and diagnosis system based on the internet of things and fuzzy logic for cardiac arrhythmia COVID-19 patients

Comput Biol Med. 2023 Mar:154:106583. doi: 10.1016/j.compbiomed.2023.106583. Epub 2023 Jan 24.

Authors

Muhammad Zia Rahman¹, Muhammad Azeem Akbar², Víctor Leiva³, Abdullah Tahir⁴, Muhammad Tanveer Riaz⁵, Carlos Martin-Barreiro⁶

Affiliations

¹ Department of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad, Pakistan. Electronic address: ziaurrahman@uet.edu.pk.
² Department of Information Technology, Lappeenranta University of Technology, Lappeenranta, Finland. Electronic address: azeem.akbar@lut.fi.
³ School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile. Electronic address: victorleivasanchez@gmail.com.
⁴ Department of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad, Pakistan.
⁵ Department of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad, Pakistan; Department of Electrical and Information Engineering, Politecnico di Bari, Bari, Italy.
⁶ Faculty of Natural Sciences and Mathematics, Escuela Superior Politécnica del Litoral ESPOL, Guayaquil, Ecuador; Faculty of Engineering, Universidad Espíritu Santo, Samborondón, Ecuador.

Abstract

Background: During the COVID-19 pandemic, there is a global demand for intelligent health surveillance and diagnosis systems for patients with critical conditions, particularly those with severe heart diseases. Sophisticated measurement tools are used in hospitals worldwide to identify serious heart conditions. However, these tools need the face-to-face involvement of healthcare experts to identify cardiac problems.

Objective: To design and implement an intelligent health monitoring and diagnosis system for critical cardiac arrhythmia COVID-19 patients.

Methodology: We use artificial intelligence tools divided into two parts: (i) IoT-based health monitoring; and (ii) fuzzy logic-based medical diagnosis. The intelligent diagnosis of heart conditions and IoT-based health surveillance by doctors is offered to critical COVID-19 patients or isolated in remote locations. Sensors, cloud storage, as well as a global system for mobile texts and emails for communication with doctors in case of emergency are employed in our proposal.

Results: Our implemented system favors remote areas and isolated critical patients. This system utilizes an intelligent algorithm that employs an ECG signal pre-processed by moving through six digital filters. Then, based on the processed results, features are computed and assessed. The intelligent fuzzy system can make an autonomous diagnosis and has enough information to avoid human intervention. The algorithm is trained using ECG data from the MIT-BIH database and achieves high accuracy. In real-time validation, the fuzzy algorithm obtained almost 100% accuracy for all experiments.

Conclusion: Our intelligent system can be helpful in many situations, but it is particularly beneficial for isolated COVID-19 patients who have critical heart arrhythmia and must receive intensive care.

Keywords: Artificial intelligence; Defuzzification; Features extraction; Finite impulse response; Fuzzy membership and rules; Global system for mobile communication; Internet of things; MIT-BIH database; QRS peaks; SARS-CoV-2.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Arrhythmias, Cardiac / diagnosis
Artificial Intelligence
COVID-19 Testing
COVID-19* / diagnosis
Fuzzy Logic
Humans
Internet
Internet of Things*
Pandemics