SynSigGAN: Generative Adversarial Networks for Synthetic Biomedical Signal Generation

SIMPLE SUMMARY: This paper proposes a novel generative adversarial networks model, SynSigGAN, to generate any kind of synthetic biomedical signals. The generation of synthetic signals eliminates confidentiality concerns and accessibility problem of medical data. Synthetic data can be utilized for tr...

Descripción completa

Detalles Bibliográficos
Autores principales: Hazra, Debapriya, Byun, Yung-Cheol
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7761837/
https://www.ncbi.nlm.nih.gov/pubmed/33287366
http://dx.doi.org/10.3390/biology9120441

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7761837/
https://www.ncbi.nlm.nih.gov/pubmed/33287366
http://dx.doi.org/10.3390/biology9120441

Ejemplares similares