Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization
Abstract Atrial fibrillation (AF) is an abnormal heart rhythm, asymptomatic in many cases, that causes several health problems and mortality in population. This retrospective study evaluates the ability of different AI-based models to predict future episodes of AF from electrocardiograms (ECGs) reco...
Guardado en:
Autores principales: | Pietro Melzi, Ruben Tolosana, Alberto Cecconi, Ancor Sanz-Garcia, Guillermo J. Ortega, Luis Jesus Jimenez-Borreguero, Ruben Vera-Rodriguez |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/d3fe7ade268d446db2798bffbd1c1372 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm
por: Yong-Soo Baek, et al.
Publicado: (2021) -
Improvement in Atrial Fibrillation-Related Symptoms After Cardioversion: Role of NYHA Functional Class and Maintenance of Sinus Rhythm
por: Ferre-Vallverdu M, et al.
Publicado: (2021) -
Inherited and Acquired Rhythm Disturbances in Sick Sinus Syndrome, Brugada Syndrome, and Atrial Fibrillation: Lessons from Preclinical Modeling
por: Laura Iop, et al.
Publicado: (2021) -
Rhythm Control in Heart Failure Patients with Atrial Fibrillation
por: William Eysenck, et al.
Publicado: (2020) -
ECG data dependency for atrial fibrillation detection based on residual networks
por: Hyo-Chang Seo, et al.
Publicado: (2021)