An intelligent health monitoring and diagnosis system based on the internet of things and fuzzy logic for cardiac arrhythmia COVID-19 patients. (March 2023)

• Record Type: Journal Article
• Authors: Rahman, Muhammad Zia; Akbar, Muhammad Azeem; Leiva, Víctor; Tahir, Abdullah; Riaz, Muhammad Tanveer; Martin-Barreiro, Carlos
• Is Part Of: Computers in biology and medicine. Volume 154(2023)
• Publication Date: 2023-03
• Subjects: 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; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106583
• Languages: English
• ISSNs: 0010-4825

Abstract

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. Graphical abstract: Highlights: We design an intelligent health monitoring and diagnosis system using IoT and fuzzy logic-based artificial intelligence tools. Cardiac arrhythmia critical COVID-19 patients are benefited of our system who are located at remote areas or isolated. Our algorithm is pre-processed with ECG data and then features are calculated. ECG data from the MIT-BIH database are used for training the algorithm. Training of the system achieved about 100% accuracy in real-time validation.