| Autor: |
Qiu J, Oppelt MP, Nissen M, Anneken L, Breininger K, Eskofier B |
| Jazyk: |
angličtina |
| Zdroj: |
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2022 Jul; Vol. 2022, pp. 945-949. |
| DOI: |
10.1109/EMBC48229.2022.9871969 |
| Abstrakt: |
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 augmentation effects established the efficacy of the three proposed augmentations. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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