Atrial Fibrillation: A Medical and Technological Review

TL;DR

This review discusses atrial fibrillation's symptoms, diagnosis challenges, and the promising role of machine learning in improving detection and understanding of this common arrhythmia.

Contribution

It offers a comprehensive overview of AF, highlighting recent advances in machine learning for better detection and understanding of the condition.

Findings

Machine learning algorithms effectively identify AF from ECG data.

Early detection of AF can reduce healthcare costs and improve patient outcomes.

Current detection methods rely mainly on single-point ECG recordings.

Abstract

Atrial Fibrillation (AF) is the most common type of arrhythmia (Greek a-, loss + rhythmos, rhythm = loss of rhythm) leading to hospitalization in the United States. Though sometimes AF is asymptomatic, it increases the risk of stroke and heart failure in patients, in addition to lowering the health-related quality of life (HRQOL). AF-related care costs the healthcare system between $6.0 to $26 billion each year. Early detection of AF and clinical attention can help improve symptoms and HRQOL of the patient, as well as bring down the cost of care. However, the prevalent paradigm of AF detection depends on electrocardiogram (ECG) recorded at a single point in time and does not shed light on the relation of the symptoms with heart rhythm or AF. In the recent decade, due to the democratization of health monitors and the advent of high-performing computers, Machine Learning algorithms have been proven effective in identifying AF, from the ECG of patients. This paper provides an overview of the symptoms of AF, its diagnosis, and future prospects for research in the field.