Cargando…
Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks
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 laborious rule...
Autores principales: | Jimenez-Perez, Guillermo, Alcaine, Alejandro, Camara, Oscar |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806759/ https://www.ncbi.nlm.nih.gov/pubmed/33441632 http://dx.doi.org/10.1038/s41598-020-79512-7 |
Ejemplares similares
-
Robustness of convolutional neural networks to physiological electrocardiogram noise
por: Venton, J., et al.
Publicado: (2021) -
Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network
por: Ji, Yinsheng, et al.
Publicado: (2019) -
Convolutional neural network optimized by differential evolution for electrocardiogram classification
por: Chen, Shan Wei, et al.
Publicado: (2023) -
Towards Automated Optimization of Residual Convolutional Neural Networks for Electrocardiogram Classification
por: Fki, Zeineb, et al.
Publicado: (2023) -
Electrocardiogram Delineation in a Wistar Rat Experimental Model
por: Arini, Pedro David, et al.
Publicado: (2018)