A review on the state of the art in atrial fibrillation detection enabled by machine learning

Rizwan, A., Zoha, A. , Mabrouk, I. B., Sabbour, H., Al-Sumaiti, A. S., Alomaniy, A., Imran, M. A. and 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, pp. 219-239. (doi: 10.1109/RBME.2020.2976507) (PMID:32112683)

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. n 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. It is the first review paper of its kind that comprehensively presents a discussion on all these aspects related to AF auto-diagnosis at one place. It is observed that there is dire need of low energy, low cost but accurate auto diagnosis solutions for the proactive management of AF.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zoha, Dr Ahmed and Rizwan, Ali and Abbasi, Professor Qammer and Imran, Professor Muhammad
• Authors: Rizwan, A., Zoha, A., Mabrouk, I. B., Sabbour, H., Al-Sumaiti, A. S., Alomaniy, A., Imran, M. A., and Abbasi, Q. H.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: IEEE Reviews in Biomedical Engineering
• Publisher: IEEE
• ISSN: 1937-3333
• ISSN (Online): 1941-1189
• Published Online: 27 February 2020
• Copyright Holders: Copyright © 2019 IEEE
• First Published: First published in IEEE Reviews in Biomedical Engineering 14:219 - 239
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher