Cardiac Abnormality Detection in 12-lead ECGs With Deep Convolutional Neural Networks Using Data Augmentation
- A residual neural network was adapted and applied to the Physionet/Computing data in Cardiology Challenge 2020 to detect 24 different classes of cardiac abnormalities from 12-lead. Additive Gaussian noise, signal shifting, and the classification of signal sections of different lengths were applied to prevent the network from overfitting and facilitating generalization. Due to the use of a global pooling layer after the feature extractor, the network is independent of the signal’s length. On the hidden test set of the challenge, the model achieved a validation score of 0.656 and a full test score of 0.27, placing us 15th out of 41 officially ranked teams (Team name: UC_Lab_Kn). These results show the potential of deep neural networks for ap- plication to raw data and a complex multi-class multi-label classification problem, even if the training data is from di- verse datasets and of differing lengths.
| Author: | Lucas WeberORCiD, Maksym GaidukORCiD, Wilhelm Daniel ScherzORCiD, Ralf SeepoldORCiDGND |
|---|---|
| DOI: | https://doi.org/10.22489/CinC.2020.229 |
| ISBN: | 978-1-7281-7382-5 |
| ISSN: | 2325-887X |
| ISSN: | 0276-6574 |
| Parent Title (English): | Computing in Cardiology (CinC 2020, September 13th-16th, Rimini, Italy, virtual) |
| Volume: | 47 |
| Publisher: | IEEE |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of Publication: | 2020 |
| Release Date: | 2021/01/29 |
| Tag: | Deep Convolutional Neural Network; Residual Neural Network; ECG |
| Page Number: | 4 |
| Institutes: | Fakultät Informatik |
| Open Access?: | Ja |
| Licence (German): |  Urheberrechtlich geschützt |
