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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.

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Metadaten
Author:Lucas Weber, 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; ECG; Residual Neural Network
Pagenumber:4
Institutes:Fakultät Informatik
Open Access?:Ja
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Licence (English):License LogoLizenzbedingungen IEEE