Impact of Neural Architecture Design on Cardiac Abnormality Classification Using 12-lead ECG Signals

Abstract

Cardiac rhythm abnormality, as associated with irregular heart activity, presents as changes in an electrocardiogram (ECG). In this paper, as part of the PhysioNet Challenge 2020, we propose two cardiac abnormality detection and classification neural models, using 12-lead ECG signals. Our ECU team proposes a hand-designed Recurrent Convolutional Neural Network (RCNN), consisting of 49 one-dimensional convolutional layers, 16 skip connections, and one Bi-Directional LSTM layer. This model, without relying on any pre-processing or manual feature engineering, achieved a Challenge validation score of 62.3% and a full test score of 38.2%, ranking us 9th out of 41 teams in the official ranking. Our second neural model, designed through neural architecture search, did not score on the full test dataset nor on the validation dataset; however, we optimistically expect performance improvement compared to our hand-designed RCNN model. This model scored 64.4% using 10-fold cross-validation on the training dataset, which is 2.5% higher than the training score of our RCNN model, using 10-fold cross-validation.