We developed a convolutional recurrent neural network to classify 12-lead ECG
signals for the challenge of PhysioNet/ Computing in Cardiology 2020 as team
Pink Irish Hat. The model combines convolutional and recurrent layers, takes
sliding windows of ECG signals as input and yields the probability of each
class as output. The convolutional part extracts features from each sliding
window. The bi-directional gated recurrent unit (GRU) layer and an attention
layer aggregate these features from all windows into a single feature vector.
Finally, a dense layer outputs class probabilities. The final decision is made
using test time augmentation (TTA) and an optimized decision threshold. Several
hyperparameters of our architecture were optimized, the most important of which
turned out to be the choice of optimizer and the number of filters per
convolutional layer. Our network achieved a challenge score of 0.511 on the
hidden validation set and 0.167 on the full hidden test set, ranking us 23rd
out of 41 in the official ranking.
Halla Sigurthorsdottir, Jérôme Van Zaen, Ricard Delgado-Gonzalo, Mathieu Lemay