Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks
Abstract Detection and delineation are key steps for retrieving and structuring information of the electrocardiogram (ECG), being thus crucial for numerous tasks in clinical practice. Digital signal processing (DSP) algorithms are often considered state-of-the-art for this purpose but require labori...
Guardado en:
Autores principales: | Guillermo Jimenez-Perez, Alejandro Alcaine, Oscar Camara |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/3180f946800943f3a9609917b881d0e7 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Image dataset on the Chinese medicinal blossoms for classification through convolutional neural network
por: Mei-Ling Huang, et al.
Publicado: (2021) -
Deep Convolutional Neural Network with KNN Regression for Automatic Image Annotation
por: Ramla Bensaci, et al.
Publicado: (2021) -
Sequentially Delineation of Rooftops with Holes from VHR Aerial Images Using a Convolutional Recurrent Neural Network
por: Wei Huang, et al.
Publicado: (2021) -
Explaining deep neural networks for knowledge discovery in electrocardiogram analysis
por: Steven A. Hicks, et al.
Publicado: (2021) -
MAT-AGCA: Multi Augmentation Technique on small dataset for Balinese character recognition using Convolutional Neural Network
por: Ni Putu Sutramiani, et al.
Publicado: (2021)