Deep_Discriminative_Domain_Generalization_with_Adversarial_Feature_Learning_for_Classifying_ECG_Signals.pdf (612.52 kB)
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Deep discriminative domain generalization with adversarial feature learning for classifying ECG signals

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conference contribution
posted on 21.09.2021, 12:49 by Zuogang Shang, Zhibin Zhao, Hui FangHui Fang, Samuel Relton, Darcy Murphy, Zoe Hancox, Ruqiang Yan, David Wong
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.



  • Science


  • Computer Science


Computing in Cardiology (CinC 2021)


VoR (Version of Record)

Publisher statement

This is an Open Access paper under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at:

Publication date





Brno, Czech Republic

Event dates

12th September 2021 - 15th September 2021


Dr Hui Fang. Deposit date: 20 September 2021

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