An accurate and efficient method to train classifiers for atrial fibrillation detection in ECGs: Learning by asking better questions

Background: An increasing number of wearables are capable of measuring electrocardiograms (ECGs), which may help in early detection of atrial fibrillation (AF). Therefore, many studies focus on automated detection of AF in ECGs. A major obstacle is the required amount of manually labelled data. This study aimed to provide an efficient and...

Digital biomarkers and algorithms for detection of atrial fibrillation using surface electrocardiograms: A systematic review: Digital Biomarkers for AF in Surface ECGs

Aims: Automated detection of atrial fibrillation (AF) in continuous rhythm registrations is essential in order to prevent complications and optimize treatment of AF. Many algorithms have been developed to detect AF in surface electrocardiograms (ECGs) during the past few years. The aim of this systematic review is to gain more insight into...

Unraveling the Electro-Pathophysiology of Atrial Fibrillation: Automated Atrial Fibrillation Analysis in Post-Operative Electrocardiograms

Introduction Atrial fibrillation (AF) is the most common age-related, progressive tachyarrhythmia in the USA and in European countries. AF is associated with an increased risk of stroke, heart failure, impaired cognitive function, and increased mortality. An obstacle for optimal diagnosis and treatment is the relatively unknown (electro-...