A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning

Rizwan, Ali; Zoha, Ahmed; Mabrouk, Ismail Ben; Sabbour, Hani M.; Al-Sumaiti, Ameena Saad; Alomainy, Akram; Imran, Muhammad Ali; Abbasi, Qammer H.

Abstract

Atrial Fibrillation (AF) the most commonly occurring type of cardiac arrhythmia is one of the main causes of morbidity and mortality worldwide. The timely diagnosis of AF is an equally important and challenging task because of its asymptomatic and episodic nature. In this paper, state-of-the-art ECG data-based machine learning models and signal processing techniques applied for auto diagnosis of AF are reviewed. Moreover, key biomarkers of AF on ECG and the common methods and equipment used for the collection of ECG data are discussed. Besides that, the modern wearable and implantable ECG sensing technologies used for gathering AF data are presented briefly. In the end, key challenges associated with the development of auto diagnosis solutions of AF are also highlighted. This is the first review paper of its kind that comprehensively presents a discussion on all these aspects related to AF auto-diagnosis in one place. It is observed that there is a dire need for low energy and low cost but accurate auto diagnosis solutions for the proactive management of AF.

Citation

Rizwan, A., Zoha, A., Mabrouk, I. B., Sabbour, H. M., Al-Sumaiti, A. S., Alomainy, A., Imran, M. A., & Abbasi, Q. H. (2021). A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning. IEEE Reviews in Biomedical Engineering, 14, 219-239. https://doi.org/10.1109/rbme.2020.2976507

• Journal Article Type: Article
• Acceptance Date: Dec 1, 2019
• Online Publication Date: Feb 27, 2020
• Publication Date: 2021
• Deposit Date: May 26, 2023
• Journal: IEEE Reviews in Biomedical Engineering
• Print ISSN: 1937-3333
• Electronic ISSN: 1941-1189
• Publisher: Institute of Electrical and Electronics Engineers
• Volume: 14
• Pages: 219-239
• DOI: https://doi.org/10.1109/rbme.2020.2976507
• Public URL: https://durham-repository.worktribe.com/output/1171311
• Related Public URLs: https://eprints.gla.ac.uk/204507/