1 Automation and Process Control Engineer, Gist Limited, Bristol, United Kingdom.
2 Engineering management. University of south Wales, South Wale, United Kingdom.
3 Process Engineer and Renewable Energy Technologist, University of Applied Sciences Bremerhaven, Germany.
4 Automation Engineer, University of South Wales, United Kingdom.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(01), 1385-1414
Received on 08 June 2024; revised on 15 July 2024; accepted on 18 July 2024
The integration of Artificial Intelligence (AI) in Electrocardiography (ECG) and Photoplethysmography (PPG) signifies AI's profound influence on heart rate monitoring and analysis. ECG traditionally offers critical insights into cardiac health, necessitating expert interpretation. This study introduces an AI framework with Fast Fourier Transformation Analysis for swift, human-like interpretation of complex ECG signals. A multilayer AI Network accurately detects intricate features, enhancing ECG analysis precision. Leveraging comprehensive datasets, AI models proficiently identify heart dysfunctions like atrial fibrillation and hypertrophic cardiomyopathy, and can estimate age, sex, and race. The proliferation of mobile ECG technologies has spurred AI-based ECG phenotyping, impacting clinical and population health. This research explores AI's role in enhancing cardiac health assessment and clinical decision-making using MATLAB, acknowledging its transformative potential and inherent limitations.
Artificial Intelligence; Electrocardiography (ECG); Fast Fourier Transformation; Heart Rate Monitoring; Clinical Decision-Making; MATLAB
Preview Article PDF
Joseph Nnaemeka Chukwunweike, Samakinwa Michael, Martin Ifeanyi Mbamalu MNSE and Chinonso Emeh. Artificial intelligence and electrocardiography: A modern approach to heart rate monitoring. World Journal of Advanced Research and Reviews, 2024, 23(1), 1385-1414. Article DOI: https://doi.org/10.30574/wjarr.2024.23.1.2162