TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Weber, Lucas
A1  - Gaiduk, Maksym
A1  - Scherz, Wilhelm Daniel
A1  - Seepold, Ralf
T1  - Cardiac Abnormality Detection in 12-lead ECGs With Deep Convolutional Neural Networks Using Data Augmentation
T2  - Computing in Cardiology
(CinC 2020, September 13th-16th, Rimini, Italy, virtual)
N2  - 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.
KW  - Deep Convolutional Neural Network
KW  - Residual Neural Network
KW  - ECG
Y1  - 2020
SN  - 2325-887X
SS  - 2325-887X
SN  - 0276-6574
SS  - 0276-6574
SN  - 978-1-7281-7382-5
SB  - 978-1-7281-7382-5
U6  - https://doi.org/10.22489/CinC.2020.229
DO  - https://doi.org/10.22489/CinC.2020.229
VL  - 47
SP  - 4
S1  - 4
PB  - IEEE
ER  -