Cargando…
Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification
Physicians manually interpret an electrocardiogram (ECG) signal morphology in routine clinical practice. This activity is a monotonous and abstract task that relies on the experience of understanding ECG waveform meaning, including P-wave, QRS-complex, and T-wave. Such a manual process depends on si...
Autores principales: | Tutuko, Bambang, Rachmatullah, Muhammad Naufal, Darmawahyuni, Annisa, Nurmaini, Siti, Tondas, Alexander Edo, Passarella, Rossi, Partan, Radiyati Umi, Rifai, Ahmad, Sapitri, Ade Iriani, Firdaus, Firdaus |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8953093/ https://www.ncbi.nlm.nih.gov/pubmed/35336500 http://dx.doi.org/10.3390/s22062329 |
Ejemplares similares
-
Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection
por: Nurmaini, Siti, et al.
Publicado: (2021) -
Robust electrocardiogram delineation model for automatic morphological abnormality interpretation
por: Nurmaini, Siti, et al.
Publicado: (2023) -
Deep Learning for Improving the Effectiveness of Routine Prenatal Screening for Major Congenital Heart Diseases
por: Nurmaini, Siti, et al.
Publicado: (2022) -
AFibNet: an implementation of atrial fibrillation detection with convolutional neural network
por: Tutuko, Bambang, et al.
Publicado: (2021) -
DAE-ConvBiLSTM: End-to-end learning single-lead electrocardiogram signal for heart abnormalities detection
por: Tutuko, Bambang, et al.
Publicado: (2022)