  Atrial Fibrillation (AF) is a heart's arrhythmia which, despite being often
asymptomatic, represents an important risk factor for stroke, therefore being
able to predict AF at the electrocardiogram exam, would be of great impact on
actively targeting patients at high risk. In the present work we use
Convolution Neural Networks to analyze ECG and predict Atrial Fibrillation
starting from realistic datasets, i.e. considering fewer ECG than other studies
and extending the maximal distance between ECG and AF diagnosis. We achieved
75.5% (0.75) AUC firstly increasing our dataset size by a shifting technique
and secondarily using the dilation parameter of the convolution neural network.
In addition we find that, contrarily to what is commonly used by clinicians
reporting AF at the exam, the most informative leads for the task of predicting
AF are D1 and avR. Similarly, we find that the most important frequencies to
check are in the range of 5-20 Hz. Finally, we develop a net able to manage at
the same time the electrocardiographic signal together with the electronic
health record, showing that integration between different sources of data is a
profitable path. In fact, the 2.8% gain of such net brings us to a 78.6% (std
0.77) AUC. In future works we will deepen both the integration of sources and
the reason why we claim avR is the most informative lead.
