Deep discriminative domain generalization with adversarial feature learning for classifying ECG signals

Introduction: The goal of the 2021 PhysioNet/CinC challenge is diagnosing cardiac abnormalities from electrocardiograms (ECGs) and evaluating the diagnostic potential of reduced-lead ECGs. We describe the whole model created by the team “AI Healthcare” for this goal.
Methods: ECGs were downsampled to 300 Hz and filtered by wavelet. Then ECGs we randomly clipped or zeropadded to 4,096 samples. To have a better representative learning ability, a modified ResNet with larger kernel sizes was used. Multi-source adversarial feature learning was used to learn domain-invariant and discriminative representations with a special gradient reversal layer (GRL). The performance with and without the domain generation methods was compared.
Results: We achieved a challenge score of 0.66, 0.64, 0.65, 0.65, 0.62 on the validation data. We ranked 8th, 7th, 6th, 6th, and 12th for 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead ECGs, respectively. Testing showed that domain generation improved metric scores on the unseen domain.
Conclusion: Generalized representations perform well for “unseen” data. It is a general method for other models to improve generalization performance by learning a domain-invariant feature representation.