  While most heart arrhythmias are not immediately harmful, they can lead to
severe complications. In particular, atrial fibrillation, the most common
arrhythmia, is characterized by fast and irregular heart beats and increases
the risk of suffering a stroke. To detect such abnormal heart conditions, we
propose a system composed of two main parts: a smart vest with two cooperative
sensors to collect ECG data and a neural network architecture to classify heart
rhythms. The smart vest uses two dry bi-electrodes to record a single lead ECG
signal. The biopotential signal is then streamed via a gateway to the cloud
where a neural network detects and classifies the heart arrhythmias. We
selected an architecture that combines convolutional and recurrent layers. The
convolutional layers extract relevant features from sliding windows of ECG and
the recurrent layer aggregates them for a final softmax layer that performs the
classification. Our neural network achieves an accuracy of 87.50% on the
dataset of the challenge of Computing in Cardiology 2017.
