Improving Deep Learning-based Cardiac Abnormality Detection in 12-Lead ECG with Data Augmentation

Qiu J, Oppelt M, Nissen M, Anneken L, Breininger K, Eskofier B (2022)

Publication Type: Conference contribution

Publication year: 2022

Original Authors: Jingna Qiu, Maximilian P Oppelt, Michael Nissen, Lars Anneken, Katharina Breininger, Bjoern Eskofier

Series: Institute of Electrical and Electronics Engineers

Conference Proceedings Title: : 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Event location: Glasgow, Scotland GB

DOI: 10.1109/EMBC48229.2022.9871969

Abstract

Automated Electrocardiogram (ECG) classification using deep neural networks requires large datasets annotated by medical professionals, which is time-consuming and expensive. This work examines ECG augmentation as a method for enriching existing datasets at low cost. First, we introduce three novel augmentations: Limb Electrode Move and Chest Electrode Move both simulate a minor electrode mislocation during signal measurement, and Heart Vector Transform generates an ECG by modeling a rotated main heart axis. These techniques are then combined with nine time series signal augmentations from literature. Evaluation was performed on ICBEB, PTB-XL Diagnostic, PTB-XL Rhythm, and PTB-XL Form datasets. Compared to models trained without data augmentation, area under the receiver operating characteristic curve (AUC) was increased by 3.5%, 1.7%, 1.4% and 3.5%, respectively. Our experiments demonstrated that data augmentation can improve deep learning performance in ECG classification. Analyses of the individual augmenion effects established the efficacy of the three proposed augmentations.

Authors with CRIS profile

Jingna Qiu Juniorprofessur für Artificial Intelligence in Medical Imaging Michael Nissen Machine Learning and Data Analytics Lab Lars Anneken Department of Medicine 2 – Cardiology and Angiology Katharina Breininger Juniorprofessur für Artificial Intelligence in Medical Imaging Björn Eskofier Machine Learning and Data Analytics Lab

Involved external institutions

Fraunhofer-Institut für Integrierte Systeme und Bauelementetechnologie (IISB) DE Germany (DE)

How to cite

APA:

Qiu, J., Oppelt, M., Nissen, M., Anneken, L., Breininger, K., & Eskofier, B. (2022). Improving Deep Learning-based Cardiac Abnormality Detection in 12-Lead ECG with Data Augmentation. In : 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Glasgow, Scotland, GB.

MLA:

Qiu, Jingna, et al. "Improving Deep Learning-based Cardiac Abnormality Detection in 12-Lead ECG with Data Augmentation." Proceedings of the : 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland 2022.

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