Cargando…
MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG
Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) signal plays a critical role in early prevention and diagnosis of cardiovascular diseases. In the previous studies on automatic arrhythmia detection, most methods concatenated 12 leads of ECG into a matrix, and then input the matri...
Formato: | Online Artículo Texto |
---|---|
Lenguaje: | English |
Publicado: |
IEEE
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7963211/ https://www.ncbi.nlm.nih.gov/pubmed/33777544 http://dx.doi.org/10.1109/JTEHM.2021.3064675 |
Ejemplares similares
-
Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System
por: Li, Hongqiang, et al.
Publicado: (2016) -
A Novel Approach for Multi-Lead ECG Classification Using DL-CCANet and TL-CCANet
por: Yang, Weiyi, et al.
Publicado: (2019) -
Multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion
por: Zhang, Fuchun, et al.
Publicado: (2023) -
Automatic Classification Method of Arrhythmias Based on 12-Lead Electrocardiogram
por: Yang, Xiao, et al.
Publicado: (2023) -
Lightweight Multireceptive Field CNN for 12-Lead ECG Signal Classification
por: Feyisa, Degaga Wolde, et al.
Publicado: (2022)