Forecast of paroxysmal atrial fibrillation using a deep neural network

Atrial fibrillation (AF) is the most common heart arrhythmia. It affects between 1% and 2% of the world population over 35 years old. This disease is linked to an increased risk of stroke and heart failure. AF is a progressive disease and, at first, paroxysmal AF episodes occur, last from seconds up to a week and then stop. The disease evolves to permanent state, where the heart is always in fibrillation and can't be corrected. Forecasting paroxysmal AF episode a few seconds or minutes before its onset remains a hard challenge, but could lead to new treatment methods. For this study, we constructed a new long-term electrocardiogram (ECG) database (24 to 96 hours), composed of 10484 ECG. As a result of a careful analysis by a cardiologist, 250 AF onsets of paroxysmal AF have been detected in 140 ECG. We developed a deep neural network (DNN) model, composed of convolutional neural network (CNN) layers and bidirectional gated recurrent units (GRU) as recurrent neural network (RNN) layers. The model was trained for a supervised binary classification distinguishing between heartbeats series (RR intervals) that precede an AF onset and series distant from any AF. The model achieved an average area under the receiver operating characteristic (ROC) curve of 0.74. We evaluated the impact of heartbeat window size given as input, and the time period between the heartbeats window and the AF onset. We found that an input window of 300 heartbeats gives the best results and, not surprisingly, the closer the window is from the AF onset, the better the results. We concluded that RR intervals series contains information about the incoming AF episode, and that it can be exploited to forecast such episode.