Deep Discriminative Domain Generalization with Adversarial Feature Learning for Classifying ECG Signals

Zuogang Shang, Zhibin Zhao, Hui Fang, Samuel D. Relton, Darcy Murphy, Zoe Hancox, Ruqiang Yan, David Wong

Abstract: Introduction: The goal of the 2021 PhysioNet/CinC challenge is to classify cardiac abnormalities from ECGs and evaluate the diagnostic potential of reduced-lead ECGs. Here, we describe the classification model created by the team “AI_Healthcare”. Methods: ECGs were downsampled to 300 Hz and filtered by wavelet. ECGs were randomly clipped or zero-padded to 4,096 samples. We modified an SE-ResNet to perform multi-task classification of both dataset and disease. We used a gradient reversal layer as part of an adversarial feature learning scheme to learn domain-invariant and discriminative representations. Results: We trained our domain-invariant model on 5 datasets, keeping one data set (Ningbo) for local validation. We also trained a baseline SE-ResNet using the same training data. In validation on the held-out data set, the domain-invariant model had a higher Challenge metric than the baseline model. Our entry was not officially ranked in the Challenge, as we did not have a successful entry during the unofficial phase of the Challenge. Conclusion: The domain-invariant model performed better than the baseline model in local held-out datasets, suggesting that this method may help improve generalisation performance.