Physicians' Academy for Cardiovascular Education

Artificial intelligence helps pick up AF in ECG acquired during normal sinus rhythm

An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction

Literature - Attia Z, Noseworthy PA, Lopez-Jimenez F et al., - The Lancet 2019. DOI:

Introduction and methods

Screening for atrial fibrillation (AF) is challenging because of the low yield of a single electrocardiograph (ECG) and the cumbersome nature of prolonged monitoring. AF is underdiagnosed, which is a problem because of its association with a higher risk of stroke, heart failure and mortality. A low-cost, widely available and non-invasive test that facilitates identification of patients who are likely to have AF can therefore have important diagnostic and therapeutic implications.

Strokes with no known cause, referred to as embolic stroke of undetermined source (ESUS) are often related to AF, which can remain undetected due to its paroxysmal and often asymptomatic nature [1]. The risk of recurrent stroke is high in ESUS, but treatment with anticoagulation after ESUS to prevent recurrent stroke has only been found to beneficial when AF is indeed documented [2-5]. Thus accurate determination of AF is important to guide therapy.

Evidence is emerging that patients who develop AF have structural changes in the atria, which predispose towards atrial arrhythmias [6]. These changes might be implicated in the pathogenesis of ischemic or embolic stroke. The authors have previously used machine learning in the form of deep neural networks to identify subtle patterns in the standard 12-lead ECG to identify the presence of asymptomatic ventricular dysfunction [7].

Now, the authors set out to train a neural network to identify the subtle findings present in a standard 12-lead ECG acquired during normal sinus rhythm that are the consequence of structural changes associated with a history of (or impending) AF, with the aim of developing a diagnostic test. The deep neural network was trained, validated and tested using a large cohort of patients from the Mayo Clinic Digital Data Vault.

All patients of at least 18 years old with at least one digital, normal sinus rhythm, standard 10-second 12-lead ECG acquired in the supine position at the Mayo Clinic ECG laboratory between Dec 31, 1993 and July 21, 2017 were included. In patients with ≥1 AF rhythm detected, the first recorded AF ECG was defined as index ECG, and the 31 days preceding the index ECG were considered the window of interest to detect potential structural changes. In patients without AF recorded on ECG, the first available ECG was considered the index ECG. All patients with their ECGs were randomized (7:1:2) to one of three groups: training, internal validation and testing dataset. Performance of the artificial intelligence (AI)-enhanced ECG to identify patients with AF was mathematically assessed by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.

Main results


This study showed that the AI-enhanced ECG recorded during normal sinus rhythm performed well in identifying the presence of AF (AUC: 0.87 for a single ECG and 0.90 for multiple ECGs). This creates the ability to identify undetected AF with an inexpensive, widely available, point-of-care test, which can have important practical implications, particularly for the management of patients with ESUS.

It should be noted that this network has been trained for retrospective classification of clinically indicated ECGs, rather than for AF detection in unselected patients. The network needs further prospective calibration before widespread application to screen in an ostensibly healthy population is justified.


Show references

Find this article online at The Lancet

Share this page with your colleagues and friends: