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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...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2020
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| 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 |