Convolutional Neural Network and Rule-Based Algorithms for Classifying 12-lead ECGs

The objective of this study was to classify 27 cardiac abnormalities based on a data set of 43101 ECG recordings. A hybrid model combining a rule-based algorithm with different deep learning architectures was developed. We compared two different Convolutional Neural Networks, a Fully Convolutional Neural Network and an Encoder Network, a combination of both, and with the addition of another neural network using age and gender as input. Two of these combinations were finally combined with a rule-based model using derived ECG features. The performance of the models was evaluated on validation data during model development using hold-out validation. Finally, the models were deployed to a Docker image, trained on the provided development data, and tested on the Challenge validation set. The model that performed best on the Challenge validation set was then deployed and tested on the full Challenge test set. The performance was evaluated based on a particular Challenge score. Our team, TeamUIO, achieved a Challenge validation score of 0.377, and a full test score of 0.206 for our best model. The score on the full test set placed us at 20th out of 41 teams in the official ranking.