Rhythm Classification Of 12-Lead Ecgs Using Deep Neural Networks And Class-Activation Maps For Improved Explainability

As part of the PhysioNet/Computing in Cardiology Challenge 2020, we developed a model for multilabel classification of 12-lead electrocardiogram (ECG) data according to specified cardiac abnormalities. Our team, LaussenLabs, developed a novel classifier pipeline with 6 core features (1) the addition of r-peak, p-wave, and t-wave features that were input into the model along with the 12-lead data, (2) data augmentation, (3) competition metric hacking, (4) modified WaveNet architecture, (5) Sigmoid threshold tuning, and (6) model stacking. Our approach received a score of 0.63 using 6-fold cross-validation on the full training data. Unfortunately, our model was unable to run on the test dataset due to time constraints, therefore, our model's final test score is undetermined.