Name
Title | ||
|---|---|---|
Improving Deep Learning-based Cardiac Abnormality Detection in 12-Lead ECG with Data Augmentation. |
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 augmentation effects established the efficacy of the three proposed augmentations. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/EMBC48229.2022.9871969 | Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
DocType | Volume | ISSN |
Conference | 2022 | 2694-0604 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingna Qiu | 1 | 0 | 0.34 |
| Maximilian P Oppelt | 2 | 0 | 0.34 |
| Michael Nissen | 3 | 0 | 0.34 |
| Lars Anneken | 4 | 0 | 0.34 |
| Katharina Breininger | 5 | 3 | 5.85 |
| Bjoern Eskofier | 6 | 170 | 44.31 |