Cargando…
Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification
An arrhythmia happens when the electrical signals that organize the heartbeat do not work accurately. Most cases of arrhythmias may increase the risk of stroke or cardiac arrest. As a result, early detection of arrhythmia reduces fatality rates. This research aims to provide a lightweight multimodel...
Autores principales: | Hammad, Mohamed, Meshoul, Souham, Dziwiński, Piotr, Pławiak, Paweł, Elgendy, Ibrahim A. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9736761/ https://www.ncbi.nlm.nih.gov/pubmed/36502049 http://dx.doi.org/10.3390/s22239347 |
Ejemplares similares
-
A Multi-Attention Approach for Person Re-Identification Using Deep Learning
por: Saber, Shimaa, et al.
Publicado: (2023) -
A Multimodel Fusion Method for Cardiovascular Disease Detection Using ECG
por: Song, Guanghui, et al.
Publicado: (2022) -
An Efficient Data Classification Decision Based on Multimodel Deep Learning
por: Hu, Wenjin, et al.
Publicado: (2022) -
ECG-COVID: An end-to-end deep model based on electrocardiogram for COVID-19 detection
por: Sakr, Ahmed S., et al.
Publicado: (2023) -
ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches
por: Rahman, Atta-ur, et al.
Publicado: (2022)