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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...
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| Publicado no: | Biology (Basel) |
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| Main Authors: | , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
MDPI
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7761837/ https://ncbi.nlm.nih.gov/pubmed/33287366 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/biology9120441 |
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