Shang, Z, Zhao, Z, Fang, H et al. (5 more authors) (2021) Deep Discriminative Domain Generalization with Adversarial Feature Learning for Classifying ECG Signals. In: 2021 Computing in Cardiology (CinC). 2021 Computing in Cardiology (CinC), 13-15 Sep 2021, Brno, Czech Republic. IEEE ISBN 9781665479165
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Health Sciences (Leeds) > Centre for Health Services Research (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Mar 2022 11:32 |
Last Modified: | 11 Mar 2022 10:09 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.23919/CinC53138.2021.9662844 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184472 |