Short term prediction of Atrial Fibrillation from ambulatory monitoring ECG using a deep neural network

Atrial fibrillation (AF) is associated with significant morbidity but remains underdiagnosed. We hypothesize a deep learning model can identify patients at risk of AF in the 2 weeks following a 24-hour ambulatory ECG with no documented AF. We identified a training set of Holter recordings of 7 to 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 dataset not used during algorithm development.