Short-term prediction of atrial fibrillation from ambulatory monitoring ECG using a deep neural network

ABSTRACT: Abstract

Aims

Atrial fibrillation (AF) is associated with significant morbidity but remains underdiagnosed. A 24 h ambulatory electrocardiogram (ECG) is largely used as a tool to document AF but yield remains limited. We hypothesize that a deep learning model can identify patients at risk of AF in the 2 weeks following a 24 h ambulatory ECG with no documented AF.

Methods and results

We identified a training set of Holter recordings of 7–15 days duration, in which no AF could be found in the first 24 h. We trained a neural network to predict the presence or absence of AF in the 15 following days, using only the first 24 h of the recording. We evaluated the neural network on a testing set and an external data set not used during algorithm development. In the testing data se

SUBMITTER:  

PROVIDER: S-EPMC9708000 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature