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

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

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

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.

Original languageBritish English
Article number9016113
Pages (from-to)219-239
Number of pages21
JournalIEEE Reviews in Biomedical Engineering
Volume14
DOIs
StatePublished - 2021

Keywords

  • AF diagnosis
  • arrhythmia
  • Atrial fibrillation
  • ECG
  • machine learning

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